Garcia Solimar

Alexandra Cordeiro²

Fernando Gorni Neto

Irenilza De
email: irenilza@gmail.com

Alencar Nääs

Ecological footprint in the cotton supply chain: The consumers' view

Keywords: Textile Supply Chain, Sustainability, Cotton Clothing

The ecological footprint estimates the impact of individuals in nature as they maintain their lifestyle. It can be used as an indicator of environmental sustainability applied to individual lifestyles, regions, and nations. This study aimed to assess the ecological footprint and the awareness of sustainability of consumers based mostly on their choice of consumption of cotton clothing. The estimation was based on the answers to an online questionnaire containing questions related to the subject lifestyle. The ecological footprint was calculated, and the results were analyzed using data mining, considering the crossvalidation using 10% of the samples to obtain a decision. The results show that the better the index of the ecological footprint, the greater the awareness of the issue by the consumer.

Introduction

The environmental impacts caused by human activities and the level of consumption of natural and industrialized resources are environmental concerns amongst the countries, governments, academia, companies, and citizens. Climate change is a consequence of the consumer lifestyle, and it represents a threat to the natural environment [START_REF] Brito De Figueiredo | The carbon footprint of exported Brazilian yellow melon[END_REF][START_REF] Jamali-Zghal | Carbon footprint and emergy combination for eco-environmental assessment of cleaner heat production[END_REF]. Carbon footprint assessment (CFA) is an important approach for the control and management of noxious gasses -NG [START_REF] Dormer | Carbon footprint analysis in plastics manufacturing[END_REF]. Sustainability indicators have been the item of study in several areas, with the objective of operationalizing sustainable development [START_REF] Jamali-Zghal | Carbon footprint and emergy combination for eco-environmental assessment of cleaner heat production[END_REF]. It is necessary to monitor consumption of natural resources in the various segments of production that meet the human needs of survival, and thus presenting proposals that can absorb the impacts and residues generated by such consumption To evaluate sustainability information. The Ecological Footprint (EF) method [START_REF] Khatri | Cationic-cellulose nanofibers: Preparation and dyeability with anionic reactive dyes for apparel application[END_REF] was developed to estimate how much of the material is being used and waste generated by individuals, cities, countries, and worldwide, which draws attention to the unsustainable lifestyle that has spread since technological advances accelerated the consumption of natural resources. The model was quickly applied worldwide as a tool to assess sustainable development [START_REF] Khatri | Cationic-cellulose nanofibers: Preparation and dyeability with anionic reactive dyes for apparel application[END_REF]. The methodology can be used on several levels for organizations, individuals, families, regions, national and worldwide [START_REF] Gri | G3 Sustainable Reporting Guidelines[END_REF].

Cotton is an agribusiness product, and it is accountable for increasing the impact of the pollution. In Brazil, agriculture is responsible for 33% of the total emissions in 2014, and part of it comes from cotton production [START_REF] Brito De Figueiredo | The carbon footprint of exported Brazilian yellow melon[END_REF]. However, consumers are not conscious that by wearing clothes made of cotton they are harming the environment. Cotton processing integrates the fashion chain, known as one of the most polluting supply chains [START_REF]ABIT -Associação Brasileira da Indústria Têxtil e de Confecção: Perfil do Setor[END_REF]. Scientific studies are scarce in the areas of ecological footprint in textile chains, specifically the use of cotton to make yarns for making fabrics. This study aims to show the consumer's view on sustainability, having as a parameter the Ecological Footprint, adapted for the consumption of cotton clothes.

1

Literature Review

Global climate change has been discussed lately in worldwide meetings since the Brundtland Report by the World Commission on Environment and Development (WCED) in 1987. In that meeting it was defined the expression 'sustainable development,' summarized as the capacity to use natural resources to the extent in which nature can recompose itself, making the planet habitable to the next generations [START_REF] Grazi | Welfare economics versus ecological footprint: modeling agglomeration, externalities and trade[END_REF]. In 1992, with the achievement of Eco-92 in Brazil, the governments of all parts of the world committed themselves to reduce the emission of noxious gasses (NG), and consequently the carbon footprint. Agriculture, which includes cotton production, is one of the factors that elevate NG emissions. Brazil is self-sufficient in the manufacture of cotton, which is the fundamental raw material for the textile chain, with revenues of near US$ 4 billion in 2016, with the projection of producing 1,443.1 thousand tons in the 2016/2017 harvests [START_REF] Gri | G3 Sustainable Reporting Guidelines[END_REF]. Brazil is the world fifth largest producer of fiber, following India, China, USA, and Pakistan. The country also represents the last complete textile chain of the West, from fiber production and cotton planting to fashion shows, and going through spinning, weaving, processing, and retailing [START_REF]GLOBAL Footprint Network. Countries[END_REF]. In 2016, the textile and confection production chain earned US$ 31 billion, which represented 8% of Brazilian GDP, with around 32 thousand formal companies. Textile production stood at 2 million tons, ranking fifth amongst world producers. The Brazilian garment industry, the fourth in the world production, produced about 7 billion pieces, including clothing, accessories, bedding table and bath linen. It represents 17% of jobs with 1.5 million direct employees, and nearly 8 million indirect employees, being the second largest employer in the manufacturing industry. Brazil is the world second largest producer and third largest consumer of denim [START_REF]GLOBAL Footprint Network. Countries[END_REF].

The sector seeks differentiated products that use less non-renewable resources such as water and reduce energy consumption and chemical aggression in the handling of goods [START_REF] Brito De Figueiredo | The carbon footprint of exported Brazilian yellow melon[END_REF][START_REF] Ohl | A modest proposal: global rationalization of ecological footprint to eliminate ecological debt[END_REF]. The consumption of abundant water and effluents are a major problem in the sector. Effluent components use the common chemical dyes found in the textile industry [START_REF] Jamali-Zghal | Carbon footprint and emergy combination for eco-environmental assessment of cleaner heat production[END_REF], during the production phases iron, pre-ironing, bleaching, dyeing, stamping, washing and softening.

The developers of the ecological footprint concept in the early 1990s [START_REF] Khatri | Cationic-cellulose nanofibers: Preparation and dyeability with anionic reactive dyes for apparel application[END_REF] at the University of British Columbia, Canada, presented the footprint as the land surface area needed to maintain natural resource consumption levels and to house the residues of this consumption. In the next step, a tool was built up to spread the concept worldwide, and individuals, companies, and organizations can calculate the carbon footprint. Each inhabitant of the planet, on average, has a carbon footprint of four tons per year [START_REF] Ohl | A modest proposal: global rationalization of ecological footprint to eliminate ecological debt[END_REF]. In the United States, the production is 20 tons per person per year. In Europe, the UK has 20 tons and France, six. Governments and businesses are also aware of the world's carbon footprints [START_REF]Advanced the science of sustainability[END_REF].

The Global Footprint Network points to the availability of 1.8 hectares of productive land for each inhabitant, but the average has been on 2.2 ha, which makes it impossible for the land to replenish what has been consumed over a year. That points to Brazil in 59 th place in the list of countries that consume more natural resources than the planet is capable of replenishing. The Brazilian ecological footprint presents the index of 3.1 ecological footprints per capita (GHA, global hectares) in 2012 [START_REF] Pegada Ecológica | Questionaire to calculate the index in Brazil[END_REF]. The emission of greenhouse gasses (mainly carbon dioxide -CO 2 ) into the atmosphere is measured for obtaining the Ecological Footprint. It is also evaluated the presence of pollutants in the air, water, and soil.

Countries have responded to the Global Footprint effort. Switzerland has adopted the indicator as the basis for sustainability; Some European countries, such as Germany, Austria, France, Finland, Belgium, Scotland, and Wales, are reviewing their environmental accounting and presenting footprint initiatives. Canada, Ecuador and the United Arab Emirates work with the entity to reach a common denominator [START_REF] Grazi | Welfare economics versus ecological footprint: modeling agglomeration, externalities and trade[END_REF][START_REF] Pegada Ecológica | Questionaire to calculate the index in Brazil[END_REF]. Amongst the factors related to consumers' daily habits, the carbon footprint measurement analyzes various issues. They also include age, address location and size of household, monthly energy costs (water, light, gas), the quantity of household trash and their recycling habits, buying habits, what kind of food they consume and how they are produced, whether they travel a lot, and what modes of transportation they favor.

Materials and Methods

The Global Footprint Network has created a tool for citizens to measure their indicator, which has been used globally. In it, data on habits such as meat consumption, light spending at home, distance traveled by car per day, and some plane trips in the year, among others, is collected and can be performed on various websites [START_REF] Chambers | Ecological Footprint Analysis: Towards a Sustainability Indicator for Business[END_REF]. In Brazil, the assessment can be made answering an online questionnaire. Sustainability assessment tools still require adaptations for the various business chains. In this first phase of the methodology, a standard questionnaire was developed including questions about the consumption of clothes, and also questions related to the level of knowledge about sustainability by the consumer. Questions were designed aiming to evaluate the Ecological Footprint (EF) [START_REF] Chambers | Ecological Footprint Analysis: Towards a Sustainability Indicator for Business[END_REF].

The sample was chosen based on the size of the Brazilian population (nearly 200 million inhabitants). The sampling error adopted was 10%, and the estimated sample size was 97 participants applying Eq. 1 [START_REF] Triola | Introdução à estatística (v. 10)[END_REF] that was rounded to a minimum of 100 participants.

𝑛 = 𝑁 𝑍 2 𝑝(1 -𝑝) (𝑁 -1)𝜀 2 + 𝑍 2 𝑝(1 -𝑝) (1)
n = sample; N = population of each region; Z = confidence interval (95%); p = homogeneity degree (split 50/50); ε = sample error (10%).

The online questionnaire, developed using the Google Docs was distributed using the internet tools (e-mails and social media networks) obtained 209 answers, and all were employed in the analysis.

To determine the carbon footprint of each participant the scores of each subject was calculated. Each question had 2 to 5 alternatives, and each alternative was given value as described in Table 1. Subjects with a total of points from 50 to 70 had a good footprint. A total of points from 35 to 49 had a moderate footprint, and a total of points less than 35 had a bad footprint. For the analysis, the data were processed in the machine learning software WEKA® (3.5), using the algorithm J48, considering cross-validation with samples of 10% (10-fold cross-validation).

A total of 14 questions is related to the lifestyle of the subjects, with emphasis on means of transportation, diet, energy consumption and production and disposal of garbage. The results present an idea of the individual's lifestyle, which was submitted to the table of weights available in [START_REF] Scarpa | Pegada ecológica: qual é a sua? São José dos Campos[END_REF] (see Table 1).

D 1 3 - 1 1 - 1 5 5 2 1 - 1 - E 0 1 - - 0 - - - - - - - 0 -
Source: [START_REF] Scarpa | Pegada ecológica: qual é a sua? São José dos Campos[END_REF].

Results and Discussion

Table 2 presents the profile of the participants in the research. The majority of the subjects are between 26 and 60 years of age, female, with a minimum of college education and monthly income between US$ 550.00 and US$ 2,750.00. Although the questionnaire was largely distributed in the social media 95% was answered by subjects living in São Paulo State, which is the wealthier and most developed state of the federation, establishing a limitation to the research.

Table 2. Summarized description of the profile of the subjects who responded to the questionnaire Amongst the answers, 74% of respondents believe that it is important to buy products from companies that are sustainable and 21% believe that it is indifferent to choose companies for this purpose. Almost 60% of the respondents could not say if the company from which they made their purchase is sustainable, and 30% stated that the business is not sustainable. More than half (56.6%) of the participants were not able to answer if the garment they wear contributed to the pollution or the sustainability of the planet. More than a third (33.6%) realizes that clothing contributes to pollution and 10.1% for the sustainability of the world. Near 70% of the respondents do not know how the industrial production of fabrics for the manufacture of clothes is done, 23.9% know, and they care about it. The total 5.6% does not know or do not care about this information. The values used for the income were adopted by the Brazilian criteria of socioeconomics scale [START_REF] Abep | Associação Brasileira de Empresas de Pesquisa. Critério Brasil[END_REF]. The values were converted from Real to US Dollar (conversion rate=R$ 3.20 to US$ 1.00). Analyzing the data we found that these parameters, however, were not associated with the individual's ecological footprint, since they were not selected by the J48 algorithm to determine the model (see Fig. 1). Figure 1 presents the decision tree for the ecological footprint with 70.75% of accuracy. It is observed that in the present study, the parameter that is most related to the ecological footprint of the individual was the expenditure on clothes and shoes, followed by how much the individual is willing to pay for garments that pollute less the environment. According to the generated decision tree, persons who have bought clothes and shoes up to US$ 100.00 in the year are individuals who present a good footprint. The other hand, subjects who have spent over US$ 300.00 annually have a moderate footprint. When evaluating the people who spent between US$ 100.00 and US$ 300.00, we need to search for how much they are willing to pay extra for clothing products that do not pollute the environment. Those who are not prepared to pay anything or pay up to US$ 3.00 in the price of goods present a moderate footprint. Those willing to pay up to US$ 6.00 or US$ 10.00 or more show a good footprint.

The findings from the questionnaire were limited to São Paulo state population (95% of the responses were from São Paulo residents) characteristics, and it does not represent Brazilian society. The total answers were from women with a high degree of education, which also adds a limitation to the results.

In the Brazilian textile sector, suppliers seek to use some environmental indicators as a parameter in their cleaner production [START_REF]GLOBAL Footprint Network. Countries[END_REF] that might be used to evaluate the consumer perception on this matter. The ecological footprint was employed by [START_REF] Herva | An approach for the application of the Ecological Footprint as environmental indicator in the textile sector[END_REF] to assess the environmental impact in a garment industry, dividing the data into three broad categories energy, resources, and waste. The results showed that the main contribution to the reduction of the footprint was the class of resources due to the high value associated with the cloth. The energy consumed was the second and the waste, the third. After this analysis, the final results were divided by the production rates for comparison by other areas. In the present study, it was found the same trend of the consumer worries regarding the energy and wastes.

When studying the Chinese textile chain [START_REF] Wang | Carbon footprint of textile throughout its life cycle: a case study of Chinese cotton shirts[END_REF] used the carbon footprint calculation system in a manufacturer of pure cotton shirts and found the average throughout the life cycle of the product in the country. According to the authors, the carbon footprint of global production, including agriculture and industry, accounts for more than 90% of the world's total carbon footprint, 96% of which is indirectly absorbed by the use of energy and materials. Thus, tissue production and its consumption refer to a highly polluting sector. Consumers need to be concerned with reducing the carbon footprint and thus contribute to the planet's sustainability. The carbon emissions are directly linked to personal habits (more walking or using public transportation than driving cars, saving energy resources, investing in an alternative source of energy among others) [START_REF]Advanced the science of sustainability[END_REF]. When comparing the data in the current study, it was observed that the consumer's lifestyle directly influence the ecological footprint, even when the consumer does not present the related consciousness.

Measurement has been applied to assess the impact of individual lifestyles, regions, nations and even worldwide on the planet's sustainability. A report with ecological data Footprints is annually available [START_REF] Ohl | A modest proposal: global rationalization of ecological footprint to eliminate ecological debt[END_REF]. Other studies have been carried out for the EF of regions and cities all over the world, such as [START_REF] Oliveira | A Pegada Ecológica: uma avaliação da metodologia através do estudo de caso da cidade de Rio Claro (SP). Ecólogo -Instituto de Biociências da Universidade Estadual Paulista "Júlio de Mesquita Filho[END_REF], who calculated the footprint in the town of Rio Claro, Brazil. Other authors calculated EF from cities, for instance Barcelona [START_REF] Relea | An approach to the Ecological Footprint of Barcelona: Estimate Summarise and Reflection about the Results[END_REF]. The continuation of the present study will be the calculation of the ecological footprint in the whole textile supply chain.

Final remarks

Cotton production and the textile manufacturing process is one of the most polluting supply chains. The present study is the first initiative to assess the ecological footprint in the cotton chain and the Brazilian textile industry. The approach adopted in the research was to understand the perception of the consumer about sustainability in this cotton supply chain. One of the findings was that about 60% did not know if the supplier of the clothes they wear is sustainable. The same index was repeated regarding the issue on the clothes contributing to the pollution or for the sustainability of the planet. It was also observed that the better the index of the ecological footprint, the more awareness for the subject the researched individual demonstrates since they have a willingness to pay more to get products that are less aggressive to the environment.

Although there are already manufacturing processes and ecologically correct raw materials (sustainable fibers, such as organic and colored cotton), awareness about the subject is still small. A detected limitation of the present study was that more than 70% of the answers were provided by women.

Fig. 1 .

 1 Fig. 1. Decision tree for the ecological footprint of the obtained answers.

Table 1 .

 1 Weight attribution for each alternative and the question related to the carbon footprint.

	Alternatives								Questions
		1	2	3	4	5	6	7	8	9	10	11	12	13	14
	A	5	5	5	5	5	5	5	1	1	1	5	1	5	4
	B	5	5	1	4	4	2	4	4	2	5	4	3	4	0
	C	4	4	-	3	2	0	2	5	4	5	2	5	3	5

Acknowledgment:

The authors wish to thank CAPES and CNPQ, and the Universitat Politècnica de València (UPV).


Kosmas Alexopoulos

Spyros Koukas

Nikoletta Boli

Dimitris Mourtzis
email: mourtzis@lms.mech.upatras.gr

Resource Planning for the Installation of Industrial Product Service Systems

Keywords: Industrial Product Service Systems (IPSS), Resource Planning, Decision Making

In product-oriented Industrial Product Service Systems (IPSSs) the customers benefit from the combination of a product which offers some functionalities and a set of services. IPSS supports the provision of services which can be offered by the product manufacturer. The services can offer a wide range of functionalities that can range from ensuring the product's original functionality to augmenting the original functionality of the product. The shifting of a company to IPSS poses many challenges such as the changing of the company's business model. One of the most important challenges for the establishment of IPSS is the appropriate planning of the resources for production, deployment, and installation into the customers' site. However, companies that provide IPSS solutions are lacking the proper tools for resources' planning in a dynamic environment. In this work, a multi-criteria resource planning method and tool for optimizing the production, delivery, and installation of IPSS is presented. The proposed solution generates alternative IPSS's production and installation plans and evaluates them on performance measures for production and installation such as time and cost. Moreover, through the integration of the planning tool with the IPSS design phase, information for generating the Bill of Process and Materials is presented. The planning tool has been designed using the Software-as-a-Service (SaaS) approach and has been applied and validated in a pilot case from the laser cutting industry.

Introduction

In the new manufacturing revolution called Industry 4.0, the digitization of all the systems that comprise a manufacturing system is a key objective. The Industry 4.0 paradigm proposes the expansion of existing products, for example by adding smart sensors or Internet of Things (IoT) devices that can be used in tandem with data analysis tools, and delivers a completely integrated solution to the customers. Also, customer demand and globalization impose constantly new requirements to industries, making the manufacturing environment more complex and dynamic than ever [START_REF] Gu | Adaptable Design[END_REF]. The Product-Service-Systems (PSS) concept is the solution for providing services together or instead of a product's ownership, aiming to increase the adding-value of the products [START_REF] Mont | Clarifying the concept of PSS[END_REF]. This solution promises improved competitiveness and sustainability. In the industrial sector, the servitization of manufacturing equipment has shifted the attention to the Industrial Product-Service Systems (IPSS). This strategy is satisfactory adequate considering an industry which requires being agile and getting full advantage of the complex integration process of mechanical parts, sensors and IoT components, which are critical elements for the control and monitor of the production lifecycle [START_REF] Meier | Agile scheduling and control for industrial product-service systems[END_REF]. Following Meier's definition [START_REF] Meier | Industrial Product-Service systems-IPS2[END_REF], an IPSS is the hybrid combination of a product and services e.g. a laser machine together with sensor enabled services that increase the value of the machine. IPSS aims at fulfilling contractually defined customer needs by the provision of product as well as service shares and moreover, the operation of an IPSS includes strategic and operative scheduling of processes and resources [START_REF] Meier | Industrial Product-Service Systems (IPS2) : Paradigm shift by mutually determined products and services[END_REF]. IPSS are characterized by five knowledge dimensions relative to the product, service, infrastructure, network and customer [START_REF] Bagheri | The Customer Knowledge Management lifecycle in PSS Value Networks: Towards process characterization[END_REF].

However, designing and planning the production of an IPSS is a complex task that requires the development of proper engineering, production and installation planning methods. IPSS is not the result of the simple connection of product with services. It is a rather complex integration process of mechanical, sensors and IoT components and software with the involvement of heterogeneous stakeholders along the whole IPSS lifecycle. In IPSS design and implementation, the resources related to the integration and configuration of the services, have an important contribution to the efficient development and use of an IPSS. This work presents a method for planning the production, deployment, and installation of IPSS taking into account various types of aspects such as availability of resources in IPSS provider side and resources on the services and sensors supplier side. The planning of resources should be taken into account both during the phase of IPSS design as well as in the phase of IPSS production and delivery. Planning is evaluated through a set of Key Performance Indicators (KPIs) which quantify the efficiency of alternative planning scenarios. The solution presented in this work can be used by IPSS solution providers to plan the resources of the IPSS ecosystem so as to improve the efficiency of IPSS delivery to the customers.

Regarding the design of PSS, the main focus comes to be the evaluation of the design [START_REF] Mourtzis | A Lean PSS design and evaluation framework supported by KPI monitoring and context sensitivity tools[END_REF], [START_REF] Mourtzis | Performance indicators for the evaluation of product-service systems design[END_REF], [START_REF] Mourtzis | PSS Design Considering Feedback from the Entire Product-service Lifecycle and Social Media[END_REF]. The last decades, great effort has been devoted to the study of planning and scheduling problems encountered in the production systems [START_REF] Chryssolouris | An Integrated Approach to Process Planning and Scheduling[END_REF]. There are several approaches regarding planning and scheduling [START_REF] Jain | A state-of-the-art review of job-shop scheduling techniques[END_REF] in the literature. The proposed approach for the IPSS planning is an artificial intelligence method (ISA) [START_REF] Chryssolouris | A Decision-Making Approach to the Operation of Flexible Manufacturing Systems[END_REF]. This one compared with other existing approaches outweighs in the direction of computational time and of multiple criteria consideration. Existing planning methods have been developed focusing on capacity planning for the delivery of IPSS, like the work presented in [START_REF] Lagemann | Robust capacity planning for the delivery of Industrial Product-Service Systems[END_REF]. Additionally, relative research has been focalized on the different ways of resource planning in both centralized and decentralized networks [START_REF] Doukas | Planning of manufacturing networks using an intelligent probabilistic approach for mass customized products[END_REF]. A hierarchical planning for IPSS is proposed by [START_REF] Steven | Hierarchical Planning for Industrial Product Service Systems[END_REF] by focusing both on the strategic and operative planning level after identifying the planning problems concerning IPSS. Along similar lines, the work of [START_REF] Meier | Resource Planning of Industrial Product-Service Systems (IPS2) by a Heuristic Resource Planning Approach[END_REF] presents a heuristic resource planning approach to the resource planning of IPSS.

This work is part of the EU project ICP4Life that aims to develop a collaborative framework for supporting the entire development process of industrial PSS [START_REF] Matsas | An Integrated Collaborative Platform for managing Product-service across their Life Cycle[END_REF], [START_REF] El | Towards a new collaborative framework supporting the design process of industrial Product[END_REF]. The proposed approach is focused on the "Planner" software module. Based on the approach presented in [START_REF] Doukas | Planning of manufacturing networks using an intelligent probabilistic approach for mass customized products[END_REF], this paper aims to tackle the issue of providing a planning methodology for the production and installation of IPSS and also present a related software implementation.

The remaining of the paper is organized as follows: The proposed methodology is discussed in Section 2. Section 3 is devoted to the IPSS planning tool software implementation. Section 4 presents a pilot case from a laser cutting machine industry. Finally, the conclusions are reported in Section 5.

IPSS production and installation planning

In this section, the problem under investigation is defined and the proposed methodology is presented. IPSS planning in real industrial practice is a complex problem that involves several actors and constraints. In this work, the IPSS planning problem considers the following generic scenario. The process starts when an IPSS provider receives an order for an IPSS solution from a customer (Step 1). Then, the IPSS provider starts designing an IPSS solution that meets the requirements. During the design phase, a number of alternative designs are being developed. A design is a solution that integrates a product and a number of services that are implemented through the utilization of hardware (e.g. IoT devices) and software (e.g. data analytics) (Step 2). Every new alternative IPSS design, before it starts being produced, needs to be evaluated with respect to the production feasibility (i.e. there is a feasible process plan that can actually produce the conceived design) and KPIs, such as delivery time and cost (Step 3). A production engineer takes as input the IPSS design and, using some process planning method and tools, develops a feasible process plan (Step 4). The IPSS planning method proposed in this work is used to provide a rough estimate on the time and cost KPIs to implement the process plan. The proposed planning method takes into account the fact that there is a limited number of resources to implement the new IPSS order and that there are already IPSS orders scheduled (Step 5). IPSS designer receives input from the IPSS production engineer and decides if the design solution is acceptable or not. The IPSS designer and production engineer may repeat the whole process several times until a satisfactory result is achieved (Step 6). Those generic steps are depicted in Fig. 1 below. This work focus on proposing a planning method and also its implementation for addressing Step 5 in the workflow presented in Fig. 1.

The planning problem considers the following constraints and assumptions. It is assumed that the IPSS provider is also the supplier of the product part of the IPSS. This makes planning simpler since there is only one supplier option for the product delivery and moreover the transportation of the IPSS solution is only transported from the IPSS provider to the customer. Another constraint is that the IPSS is composed of a product and a number S of services. The planning problem does not include the production of the product which is assumed to be available. In many IPSS cases the product is produced separately, as a first step, and then the tasks that integrate the services to the product take place. The process plan describes a number of N processes and the order in which they are executed. A process, for example, such as making a drill to mount a sensor or the process of mounting a sensor in a specific location in the product can be performed on the IPSS provider site. There are a number of R resources that can perform one or more of the N processes. Resources are possessed by IPSS suppliers, product suppliers and service suppliers on the IPSS manufacturing network. There is a transportation attribute related to a resource since a resource may need to travel to the IPSS provider site or to the customer's site in order to perform a task. Finally, there is a number of M of IPSS orders already scheduled in the IPSS provider's ecosystem. This practically means that one or more resources within a given time horizon are already scheduled to support the implementation and delivery of other IPSSs.

Fig. 1. IPSS production and installation planning flowchart

The objective of the planning method described below is to find an optimal solution that decides what IPPS equipment (e.g. sensors) suppliers to select, which resources (e.g. IPSS service installation technicians) and when they should perform which processes/tasks at IPSS provider or customer site.

The planning method proposed in this work, is based on the approach proposed by Doukas et al [START_REF] Chryssolouris | A Decision-Making Approach to the Operation of Flexible Manufacturing Systems[END_REF] and it defines the approach for assigning a set of resources to a set of tasks under multiple and often conflicting optimization criteria. The planning method is composed of the following steps. The first step is the generation of a maximum number of alternatives (MNA). The second step is the calculation of decision-making criteria in order to satisfy a set of manufacturing objectives (CR1… CRn). Step 3 is the weight definition of those criteria. Subsequently, the calculation of the utility value of each one of the alternatives with respect to the selected criteria takes place. It is important to mention here that the utility value (U1… Um) where m ϵ [1, MNA] is the weighted sum of the normalized values of the criteria and takes its values in the range 0 to 1. The final step is the ranking of the alternatives (ALT1… ALTm) and selection of the best alternative with the highest utility value ]14], aka the best resource plan. In order to obtain a high-quality solution (high utility value) to the resource planning problem in a timely manner, the intelligent search algorithm (ISA) has been developed. ISA uses three adjustable control parameters, namely the maximum number of alternatives (MNA), the decision horizon (DH) and the sampling rate (SR). MNA controls the breadth of the search (i.e. the number of alternative trees to be created), DH controls the depth of the search (i.e. the layers searched forward) and SR guides the search through the solution space for the identification of high-quality paths (i.e. number of branches created for each alternative defined by the MNA). It should be noted that all assignments made by the ISA are random. The decision-making process can be formalized as a decision matrix (Fig. 1).

IPSS planning tool implementation

The IPSS planning method has been developed as a Software-as-a-Service (SaaS) oriented web application. The IPSS tool consists of a user interface and the REST service oriented module. The user interface is developed as a Java portlet, following the JSR 168 specification that allows the user interface to be deployed in compatible Java portals, such as Liferay. The portlet has been developed using the Vaadin Java framework and exposes the functionality of the IPSS planning tool to the users. The IPSS Planning Tool Portlet can access a knowledge repository where all required information is stored in an Apache Jena semantic repository. In particular, the repository contains information such as manufacturing resources, existing manufacturing schedules, designed PSS systems, sensors and product descriptions. The module receives as input all the information needed by the IPSS Planning algorithm and produces as an output the result of the planning which includes calculations on the requested KPIs (such as cost and time) and a plan for the production of the IPSS. The input of the service is provided in a specific XML format, and in a similar fashion, the output is provided in an XML format. IPSS Planning Tool Architecture is presented in Fig. 2.

Fig.2. IPSS Planning Tool Architecture

Pilot Case: Laser Cutting Industry

The proposed method has been applied and evaluated in the case of a laser cutting machines provider. The IPSS under investigation is a Laser Cutting machine (product) together with a set of services which are implemented through the integration of sensors and software. An evaluation scenario has been setup with the following aspects:  An IPSS provider receives a new customer request for a Laser Cutting machine with three different services namely a) machine performance monitoring using vibration sensors, b) process quality monitoring using vision sensors and c) machine health monitoring using temperature sensors.

 The integration tasks of a service are executed in two steps: a) sensor delivery and b) sensor mounting and service configuration. The modelling of the pilot is depicted in Fig. 3.  Each task can be performed by a number of alternative resources (i.e. sensor supplier and service technicians). Each one of the resources is characterized by their cost C (€), operation time OT (h) and also transportation TT (h). The aforementioned resources, as well as their attributes, are given in Table 1. Also downtimes are considered (e.g. non availability during weekends).  When the new customer request is received a number of 10 IPSS orders are being produced. Thus not all resources are available to produce the new IPSS. First step is to add the new IPSS order in the workload and define its arrival and due date. The user may "lock" the tasks (see Fig. 4) of the ten PSS orders so they are not rescheduled for new resources and time frames. Then a new schedule is being generated including the tasks for the new PSS order. The results of the resource planning for the ten locked IPSS orders and the new one IPSS order, depicted with red borders, are presented in the following Gantt chart (Fig. 5) including the best alternative combination of resource planning for the scheduled IPSS accompanied with the estimation of time and the cost of the generated resource planning. In terms of competence, it is necessary to note that the industrial case modeling initially had been constructed taking into account more IPSS and more variations of resources and tasks in terms of validity of the results. A small scale industrial case was selected in order to give a comprehensive example of the implementation of the proposed tool. The proposed approach could be applied in several industries regardless their scale, considering the could-technology provided opportunities.

Conclusions

In this work, a multi-criteria resource planning method and a tool for optimizing the production, delivery and installation of IPSS is presented, with a demonstration case from laser cutting industry. The purpose of the planning tool is to provide decision support in the design and production of an IPSS order. The resource planning method generates a good plan by considering multiple and contradicting criteria such time and cost. Preliminary results gave a reduction of lead time and respectively cost per IPSS planning since all the available resources are occupied in the most convenient way based on the defined criteria. Future work will consider customer's time availability that adds a new constraint to the problem since the IPSS could only be delivered within the time window provided by the customer. Moreover, in the current study it is assumed that the product provider is the same as the IPSS provider. In future work, there will be alternative product suppliers from whom to optimally select the product.

Fig. 3 . 1 .

 31 Fig. 3. Pilot Case ModellingTable 1. Used Resources and their attributes

Fig. 4 .Fig. 5 .

 45 Fig. 4. Pilot case resource planning: 10 IPSS scheduled

Acknowledgments. This work has been partially supported by the H2020 EC funded project "An Integrated Collaborative Platform for Managing the Product-Service Engineering Lifecycle -ICP4Life" (GA No: 636862).


Adam Sanders
email: adam.sanders@hsu-hh.de

Karthik R K Subramanian

Tobias Redlich

Jens P Wulfsberg

R K Karthik

Tobias Subramanian

Jens P Redlich

Wulfsberg

Industry 4.0 and Lean Management -Synergy or Contradiction?

Keywords: Industry 4.0, Lean Management, Production Management

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Industry 4.0, the high-tech strategy initiated by the German government, is changing the way the products are manufactured which leverages the advancements of internet of things (IoT) and information and communication technology (ICT) to integrate digitalization into the production process. Businesses are in pressing need to respond to these evolving trends and develop the capability to produce highly customized products until the lot size of one in order to meet the rapidly changing consumer preferences thus managing to stay competitive in the today's dynamic market environment.

Manufacturing companies are looking for efficient means to accommodate the integration of Industry 4.0 concepts into the existing setup. Here, the complexity to effectively manage the implementation of new technologies arises and studies are required to find its impact on the existing shop floor practices. Particularly, the potential dilemma over the compatibility of lean philosophy and Industry 4.0 existing together in future smart factory arises owing to the reason that lean principles try to reduce complexity but Industry 4.0 increases the complexity. On the other hand Industry 4.0 can also be seen supportive for the idea of a lean production as the emerging transparency due to the introduction of intelligent networked systems will benefit the essential continual improvement process (CIP). This research aims for a broader understanding of the above mentioned impasse and for the identification of the impact of Industry 4.0.

Method

In this work, firstly a general overview about the topic of the compatibility of Industry 4.0 and LM is given. Then an interdependence matrix of the LM tools from Toyota's lean house and design principles for Industry 4.0 is formulated. The basic notion in framing this plot is to identify the LM tools which are getting benefited (beneficiary coefficient) from the induction of Industry 4.0 design principles into the existing production setup and also determine the cumulative support (supporting coefficient) that each Industry 4.0 design principle offers to the considered LM tools. This matrix is then used to solve the dilemma of whether Industry 4.0 and lean philosophy compliment or oppose each other by highlighting the basic LM tools that are required for Industry 4.0 implementation. The LM tools from TPS house are considered for detailed analysis about their functionality and applicability in Industry 4.0. Then by means of the interdependence matrix, the beneficiary and supporting coefficient scores are calculated and their interaction effects are explained in detail by considering specific scenarios inside the future smart factory.

Background and literature review

Lean production as a subfield of lean management can be defined as an intellectual approach comprising of a set of principles, methods and measures which when implemented provide waste elimination and competitive advantages [START_REF] Warnecke | Lean production[END_REF]. Therefore, the lean production principles have been widely accepted by the companies in early 1990s [START_REF] Kolberg | Lean automation enabled by industry 4.0 technologies[END_REF]. Thus lean production with its simplicity, higher productivity, improved quality, reduced development time and inventory were able to capitalize on the decline of CIM and became the status quo of production [START_REF] Dickmann | Schlanker Materialfluss: mit Lean Production[END_REF]. However, lean production also has its limits with regard to addressing the futuristic highly volatile customer demands (until lot size of one) which lead to highly fluctuating work content. This acts against the lean production's aim of evenly flowing production [START_REF] Erlach | Towards a Lean Factory. Value Stream Design[END_REF]. Also the TPS forecasts demand based on the actual demand in the market which cannot be increased or decreased arbitrarily [START_REF] Ohno | Toyota production system: beyond large-scale production[END_REF][START_REF]Lean revisited: Taking a fresh look at lean manufacturing strategies[END_REF]. Thus the suitability of lean production to assist Industry 4.0 for tackling the challenges of highly volatile customer demand and requirement of customized products with short life cycles (which Industry 4.0 is focused on) are put to question.

It can be understood that a skepticism prevails over the compatibility of Industry 4.0 and lean philosophy but there are also supporting aspects. The concept of lean automation was proposed in the 1990s whereby most of the tasks in auto assembly were automatized and the productions units were populated with highly skilled problem solvers whose responsibility was to ensure smooth and productive running of the production system [START_REF] Womack | Machine that changed the world[END_REF]. This term was just a watchword in the last decades which was not paid much attention [START_REF] Kolberg | Lean automation enabled by industry 4.0 technologies[END_REF][START_REF] Appleton | Automation, motivation and lean production reconsidered[END_REF]. However, [START_REF] Kolberg | Lean automation enabled by industry 4.0 technologies[END_REF] argues that in the context of Industry 4.0, automation and lean technology can be combined for realizing more benefits. Industry 4.0 can be considered as a logical evolution of lean principles which will assist to realize its complete potential. So on the first hand, the lean concepts and lean thinking can be embedded firmly into the business model for being able to build towards Industry 4.0 [START_REF] Staufen | China -Industry 4[END_REF]. Further it is shown that research activities in Industry 4.0 even enable the philosophy of lean manufacturing [START_REF] Sanders | Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing[END_REF].

A central difference between LM and Industry 4.0 lies in the strategic approach. Lean tries to reduce complexity for achieving simple solutions by simple means whereas Industry 4.0 simplifies the complexity from the view point of user by decentralized control and intelligent assistants. But still some questions remain about which lean methods supports Industry 4.0? Which lean tools benefit from the introduction of Industry 4.0? Will there be some principles that needs to be adapted? Moreover, which LM tools will become obsolete with manufacturing digitalization? This research will shed light to answer these questions.

4

Compatibility of lean management and Industry 4.0

In this section an interdependence matrix is formulated in order to solve the above questions and skepticism that is prevailing over the compatibility. The basic concept in building this matrix is to develop an individual two-way interaction between LM tools and Industry 4.0 design principles where each interaction is rated. Individual rating in each cell signifies the supporting and hindering effects of Industry 4.0 design principles on LM tools in the matrix. The support that each LM tool offers to Industry 4.0 is not rated but the basic lean elements for implementing Industry 4.0 are highlighted. To create this matrix the tools/principles of both lean and Industry 4.0 have to be defined.

Lean management tools

Employees influence the organization culture by means of their practices thereby each organization possess its own characteristics and dimensions [START_REF] Hofstede | Cultures and organizations: Software of the mind[END_REF]. Thus LM tools and practices are influenced by the organizational culture and varies between different business contexts [START_REF] Höök | Lean principles in industrialized housing production: the need for a cultural change[END_REF]. This diversity led to creation of different versions of lean models [START_REF] Liker | The Toyota Way: 14 management Principles from the World´s Greatest manufacturer[END_REF][START_REF] Dennis | Lean production simplified: a plain-language guide to the world's mostpowerful production system[END_REF]. In order to provide a common and widely accepted source, the TPS lean house was chosen as it is the base for LM tools and techniques in practice [START_REF] Liker | The Toyota way in services: the case of lean product development[END_REF]. While it comes in many variants the version in [START_REF] Dennis | Lean production simplified: a plain-language guide to the world's mostpowerful production system[END_REF] is considered as the standard model for competitive manufacturing in this research work. This plethora of LM tools, techniques and methodologies aim at elimination of waste [START_REF] Green | Baseline analysis diagnoses manufacturing[END_REF]. However, these tools possess multiple names and potentially overlaps with each other and could also have different methods of implementation by various researchers [START_REF] Pavnaskar | Classification scheme for lean manufacturing tools[END_REF].

Based on the TPS-House, the technical requirements of lean practices to be adopted by the companies [START_REF] Bhasin | Lean viewed as a philosophy[END_REF] and the LM tools which are addressed in a thorough literature review in [START_REF] Bhamu | Lean manufacturing: literature review and research issues[END_REF] 14 LM tools (Fig. 1) are identified which will be the basis for further analysis.

Fig. 1. Lean Management Tools

Industry 4.0 principles

In order to assist in successful implementation of pilot projects of Industry 4.0, six design principles are derived from independent Industry 4.0 components (CPS, IoT, IoS, Smart Factory) by means of extensive literature review to identify the central aspects of Industry 4.0 [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios[END_REF]. These principles are used in this paper as the Industry 4.0 principles for further analysis (Fig. 2).

Fig. 2. Lean Management Principles

Interdependence matrix

Using the identified lean and Industry 4.0 design principles, the interdependence matrix can be formed by inserting the LM tools in vertical direction and Industry 4.0 principles in the horizontal direction (Fig. 3). Each Industry 4.0 design principle has a benefiting effect, hindering effect or no effect on each of the identified LM tools and these effects are represented with scores ranging from 10 to -10. The interaction scores are allocated based on literature and on the authors' perception.

The interdependence matrix portrays a two-way interaction between LM and Industry 4.0 principles: (i) To what extent the Industry 4.0 design principles are supporting LM tools (represented by beneficiary and supporting coefficient) (ii) The basic LM tools which assist Industry 4.0 implementation (no scoring, colored blue). The beneficiary coefficient implies the extent to which each single LM tool getting benefitted from the Industry 4.0 design principles. It is calculated by summing up the scores for each interaction and dividing it by the total number of Industry 4.0 design principles. On the other hand, supporting coefficients imply the degree of support that each Industry 4.0 design principle gives to all the LM tools. It is calculated by summing up all the values except for the fields which have no impact and dividing it by the total number of LM tools that has a score value other than 0 [START_REF] Gerberich | Lean oder MES in der Automobilzulieferindustrie: Ein Vorgehensmodell zur fallspezifischen Auswahl[END_REF]. This is done to negate the neutral effect of LM tools on the overall score. The other way interaction is represented by the cells which are highlighted in blue. It signifies that they are the basic lean elements that serve as a foundation and support successful implementation of Industry 4.0 (detailed in 5.3).

Fig. 3. Interdependence matrix

Discussion of results

The matrix displays that most of the LM tool interactions with Industry 4.0 design principles receives either a supporting or at least a neutral effect. Only exception is the takt time which encounters a hindrance effect. This signifies that the concept of takt time will be eliminated in future smart factories. On the other hand, TPM had benefited the most from the Industry 4.0 design principles with a beneficiary coefficient score of 9.5. Since these two LM tools are lying at the opposite extremes on the scoring scale, they are considered for detailed explanation in the following. With regard to supporting coefficient, real-time capability is offering the highest support to the LM tools with a score of 6.8. Rest of the Industry 4.0 design principles are offering high to moderate support. The other way interaction (LM tools that support Industry 4.0 design principles) is highlighted in blue. It indicates the basic LM tools which are essential for successful implementation of Industry 4.0.

5.1

Interaction of TPM with Industry 4.0 design principles TPM receives a comprehensive support from all the stated design principles of Industry 4.0. TPM will execute its functions more effectively in the future smart factory with assistance from Industry 4.0 techniques. The interaction of TPM and real-time capability yielded a score of 10 due to the fact that machine and plant conditions can be monitored in real time (e.g. energy consumption, machine breakdowns, output quality, OEE). By means of intelligent algorithms, failure patterns can be predicted in advance and concerned personnel can be notified which in turn makes the maintenance planning, forecasting, spare parts logistics more easy and efficient.

After failure detection, maintenance engineers will be notified with the location of the component in which failure is imminent. They can then use the machine history and detailed step by step 3D troubleshooting procedures which promotes high autonomy problem solving. Augmented reality and interactive 3D trouble-shooting guidelines on smart devices assist to carry out maintenance activities. On account of such highly decentralized activities, the interaction of TPM and decentralization was awarded with a score of 10. In case of a bigger malfunction, others machines can be contacted via M2M to find their availability for taking over the workload. Alternatively, the service availability of CPS and CPPS devices in other plants can also be verified via IoS to transfer the production orders to other units. After rectification, the solution is stored into the cloud. This along with failure pattern can be communicated with other machines which can then learn the mistake and prevent it from happening again. TPM highly benefits from interoperability and service orientation principles that the score of 10 is awarded.

If a failure demands a part to be replaced, then the new spare part can be printed using 3D printer. Parts of the machines are made modular for easy plug and play changeover, which enables maintenance replacement of the newly manufactured part. This often exclude parts which are more complex and require precise machining. So a score of 7 is given to the TPM and modularity interaction. CPS and CPPS devices perform data collection (e.g. tool wear) and data analysis. This data is compared with the stored standard reference models, its own historical performance data and performance data of other machines in the cloud to determine the current operating performance. Thus TPM and virtualization interaction is granted with a score of 10. TPM tool will serve as an important enabler for successful functioning of a connected industry [START_REF]Industrie 4.0: Innovationen für die Produktion von Morgen[END_REF].

Interaction of takt time with Industry 4.0 design principles

The interaction of takt time with Industry 4.0 design principles range from high to full hindrance. Decentralization was evaluated with a full hindrance score of -10 owing to the fact that decision about production planning, takt time calculation is made centrally with the help of forecasted demand and product variants. So rush orders cannot be easily integrated into the production with fixed takt times which is completely contradictory to the Industry 4.0 objective of decentralization and autonomy. Modularity also cannot be enforced as production schedules, product variants and the takt time are fixed. Real time product demand fluctuations cannot be accommodated into the production line which is a complete hindrance. Communication with other machines about delay and quality issues are of less importance if the takt time and work steps are already determined and it has to follow only a fixed production sequence. Also, takt times will not allow flexible capacity planning thereby blocking the services of machines and workers to other participants. Even data collected from the physical process cannot be used for implementing an immediate change in the production of successive products owing to limited flexibility, variants and output, which are already determined by the takt time. Changes can be implemented only during the next production cycle. Because of all these reasons, a high hindrance with a score of -7 is assigned to their interactions with takt time. In summary, takt time will be an obsolete LM tool in the future smart factory. This conception is also supported by various industry statements [START_REF] Automotiveit | VW: "Industrie 4.0 beschäftigt uns elementar[END_REF][START_REF] Reil | Smart Faction: Audi Rethinks Production[END_REF].

5.3

Basic lean elements for Industry 4.0 implementation Some of the LM tool interactions (like modularity vs. standardization, decentralization vs. SMED) might have scored less with regard to the beneficiary coefficient but these LM tools in turn assist successful implementation and functioning of Industry 4.0. The most important of which is muda (waste). It is very much essential, that most of the waste in the factory and entire business process must be removed before digitalization. Standardization is also equally important for achieving modularity and interoperability as all the CPS and CPPS devices in a smart factory should have a standard protocol (e.g. OPC UA) for communication. To enable Plug&Play principle for flexible interchanging machine modules different manufacturers of the module should adopt standards for integration. It is important for virtualization to maintain consistent data standards for further processing. Likewise, SMED assists Industry 4.0's target of reduced batch sizes for achieving a lot size of one by reducing the setup time. The value streamed data is fed into cloud and machines access it continuously via IoT. The present status of value stream is monitored and if there are any discrepancies, it reacts independently to solve the problem without central control.

6

Summary and outlook

This paper provides an explanation to the co-existence of lean management and Industry 4.0 in future smart factories via an interdependence matrix. The results of the matrix show the existence of numerous synergies between the considered LM tools and Industry 4.0 design principles. From the two-way interactions, LM tools like TPM, Kanban, production smoothing, autonomation and waste elimination benefit the most by the introduction of Industry 4.0. Real-time capability, decentralization and interoperability design principles offer highest support to the LM tools. Furthermore, it is shown that the concept of takt time will no longer be needed for production lines in future smart factories. The other way interaction shows that LM tools like SMED, VSM, standardization and waste elimination are supporting and represent the prerequisites for upgrading towards Industry 4.0. As a result, integration of Industry 4.0 modules in a lean manufacturing environment will add considerable value to a company. The authors are aware of the subjectiveness of the scores and therefore this paper aims for further discussion in the scientific community Finally, it can be concluded that the concept of lean management will not fade away with the advent of Industry 4.0 but it is likely to become more important for successful implementation of Industry 4.0. The provided interactions between lean management and Industry 4.0 serve as basis for further research regarding the implementation of the lean philosophy into the future smart factory.


Espen Rød
email: espen.rod@himolde.no

Mikhail Shlopak
email: mikhail.shlopak@himolde.no

Green distribution -A comparative study of sea and road transport modes for a Norwegian manufacturing company

Keywords: SCM, distribution, sustainability

This paper presents a case study comparing sea and road transport modes for a Norwegian producer of plastic pipes for the construction industry. The study is based on three cases in which CO2e emissions and transport costs were calculated and compared. The main research problem was to analyze whether sea transport is more environmentally friendly than road transport in terms of emissions of greenhouse gases (GHG) and cost-effectiveness. The results from the three analyzed cases show that sea transport is not always the most environmentally friendly and cost-effective mode. We suggest that in order to evaluate the environmental impact and cost-effectiveness of sea transport as an alternative to road transport, such factors as transport distances, load weight/volume ratios, ship load factors, number of port calls, ship sizes, and ship fuel types for each specific case need to be analyzed.

Introduction

The global economic growth in the last century has led to huge increases in the consumption of goods. However, the production, transportation, storage, and consumption of these goods have created environmental problems. Global warming is now a major environmental concern and there is consensus among most climate scientists that this is mainly caused by large-scale emissions of GHG [START_REF] Dekker | Operations Research for green logistics-An overview of aspects, issues, contributions and challenges[END_REF].

According to Crum et al. [START_REF] Crum | Sustainable supply chain management: evolution and future directions[END_REF], supply chain managers are in an advantageous position to impact environmental and social performance, both positively and negatively, through such means as supplier selection and supplier development, modal and carrier selection, vehicle routing, location decisions, and packaging choices. According to Hjelle [START_REF] Hjelle | Atmospheric Emissions of Short Sea Shipping Compared to Road Transport Through the Peaks and Troughs of Short-Term Market Cycles[END_REF], the issue of global warming receives wide attention on the global political agenda, mainly related to direct emissions of GHG from vessels and vehicles, but also to emissions related to the manufacturing of these means of transport.

This paper is a part of an ongoing research project called Manufacturing Networks 4.0, conducted by Molde University College, NTNU, SINTEF and Møreforsking Molde, along with four industrial partners. One of the industrial partners was interested in analyzing whether changing from road to sea transport mode would reduce GHG emissions and simultaneously reduce its distribution costs. The case company is an international manufacturer of plastic pipes and fittings. The cases presented in this paper are related to delivery of pipes to road construction projects in Norway. Hjelle [START_REF] Hjelle | Atmospheric Emissions of Short Sea Shipping Compared to Road Transport Through the Peaks and Troughs of Short-Term Market Cycles[END_REF] pointed out that the efficiency of short sea shipping in a setting with small consignments and frequent port calls needs to be demonstrated relative to road transport alternatives.

The purpose of this study was to find out whether a manufacturer can reduce GHG emissions and transport costs by switching from road to sea transport.

Theoretical background

Environmental issues related to road and sea transport are traditionally divided into environmental hazards that have local, regional, or global impacts [START_REF] Cullinane | Assessing the environmental impacts of freight transport[END_REF]. Locally, the most severe effects are related to poor air quality from emissions of SO2, NOX, and particles. At the regional level, the concerns are emissions of SO2 and NOX, oil spills, and disposal of waste products such as ballast water. On the global scale, the issue of global warming has received the greatest attention, mainly related to emissions of GHG from vessels and vehicles [START_REF] Hjelle | Short Sea Shipping's Green Label at Risk[END_REF]. International shipping contributes approximately 2.4 percent of global GHG emissions and this share is expected to increase in the future [START_REF] Winnes | Reducing GHG emissions from ships in port areas[END_REF]. GHG emissions are defined as the total mass of a GHG released to the atmosphere over a specified period of time. GHG include such gases as carbon dioxide (CO2), methane (CH4), and nitrous oxides (NOx). Each type of GHG has a different global warming potential and GHG emissions are usually reported in carbon dioxide equivalents (CO2e). CO2e is a unit used for comparing the radiative forcing of a GHG to that of carbon dioxide [START_REF] Cullinane | Assessing the environmental impacts of freight transport[END_REF].

The CO2e emissions can be calculated based on fuel consumption. The emission factors for different fuel types are presented in Table 1 [START_REF] Cen | Methodology for calculation and declaration of energy consumption and GHG emissions of transport services (freight and passengers)[END_REF], [START_REF]Emission Factors for Greenhouse Gas Inventories[END_REF] . Several factors affect the performance of sea and road transport mode with regard to fuel consumption. According to Hjelle [START_REF] Hjelle | Short Sea Shipping's Green Label at Risk[END_REF], key performance indicators for sea transport are vessel types and operating speed, load factors, fuel, and engine types. In road transport, such factors as operating speed, road gradient, congestion, driver behavior, and vehicle characteristics affect the fuel consumption and GHG emissions [START_REF] Demir | A review of recent research on green road freight transportation[END_REF].

The globalization of trade has resulted in the creation of worldwide shipping networks that make use of a limited number of large ports. This concentration of flows has produced a division between deep-sea shipping (DSS) and short-sea shipping (SSS) [START_REF] Gouvernal | Short sea and deep sea shipping markets in France[END_REF]. SSS is typically defined as the movement of cargo and passengers by sea between ports that does not involve an ocean crossing [START_REF] Johnson | Barriers to improving energy efficiency in short sea shipping: an action research case study[END_REF]. According to Hjelle [START_REF] Hjelle | Atmospheric Emissions of Short Sea Shipping Compared to Road Transport Through the Peaks and Troughs of Short-Term Market Cycles[END_REF], deep-sea and bulk operations are superior to other transport modes in terms of GHG emissions, while the superiority of SSS needs to be demonstrated relative to road transport alternatives.

Research methodology

This paper presents a case study comparing sea and road transport modes for distribution of plastic pipes for road construction projects in Norway. The main focus has been to calculate GHG emissions and distribution costs for three cases. To collect the data needed for our analysis, such as price, fuel consumption, and number of port calls, we contacted a shipping agent, which provided us with information from three ship-owners.

In each of the cases, the calculations were based on the real-world data provided by the case company, ship owners, and shipping agents. GHG emissions were calculated using a method based on the European standard [START_REF] Cen | Methodology for calculation and declaration of energy consumption and GHG emissions of transport services (freight and passengers)[END_REF].

4

Case Study Analysis

Case description

The case company, a manufacturer of plastic pipes, was interested in identifying whether it could reduce its GHG emissions and transportation costs by switching from road transport to sea transport. In this context the sea and road transport costs and GHG emissions on three specific cases were calculated. Case 1 is a delivery to a road construction project in the North of Norway (Tromsø), Case 2 is a delivery to a project in the South-East of Norway (Fredrikstad), and Case 3 is a delivery to a project on the West coast of Norway (Haugesund).

The road mode includes transportation from the factory directly to the construction site. The sea mode includes transportation from the port closest to the factory to the port closest to the road construction site, as well as transportation by truck from the factory to the port of origin and from the destination port to the road construction site, assumed to be 10 km each way. Table 2 shows the approximate distances for both sea and road transport modes for the three cases.

According to CEN [START_REF] Cen | Methodology for calculation and declaration of energy consumption and GHG emissions of transport services (freight and passengers)[END_REF], the CO2 emissions from different fuel types are based on the fuel consumption. The trucks in road transport mode use diesel fuel, which has an emission factor of 3.21 kg CO2e per kg fuel used. In our study, in sea transport mode, three types of fuel are used: marine diesel oil (MDO), marine gas oil (MGO), and liquefied natural gas (LNG). The fuels have different densities, but the emission factor for MDO and MGO is 3.24 kg CO2e per kg fuel used, while for LNG the emission factor is 2.83 kg CO2e per kg fuel used.

Plastic pipes have a high volume/weight ratio. Several methods can be used to calculate the share of GHG emissions that are caused by transportation of a specific load (plastic pipes in our case), which utilizes only a part of the ship's capacity. One of the methods is based on the ratio between the weight of the transported goods and the dead weight tonnage of the ship. However, in our case, because of large volume and relatively light weight of plastic pipes we chose to base the calculations on the ratio between the volume of the transported goods and the total utilized load capacity of the ship.

Cargo ships usually have a load factor of 50-70 percent [START_REF] Oterhals | NyFrakt : analyse av kystfrakteflåten : flåteutviklingutnyttelsesgradforbedringsmuligheter[END_REF], [START_REF] Hjelle | Atmospheric Emissions of Short Sea Shipping Compared to Road Transport Through the Peaks and Troughs of Short-Term Market Cycles[END_REF]. The load factor will have a significant impact on the final result of the CO2e emissions that a specific load accounts for.

Fuel Consumption

The ships used in our study vary in terms of their size, fuel type, and load capacity. All of these factors influence actual fuel consumption.

The estimated fuel consumption in our cases is provided by the ship-owners and relates to a "normal" load for the ship. In Case 1, we look at two alternatives: transportation by a MDO-fueled ship and transportation by a LNG-fueled ship. In this case, for a direct route from Surnadal to Tromsø, ca. 11,700 kg of MDO will be used, while MDO consumption for the same route with 15-20 port calls is ca. 18,000 kg. In the other alternative, a LNG-fueled ship will use 40,800 kg of LNG from Surnadal to Tromsø with several port calls. In Case 2, the estimated fuel consumption for a route from Surnadal to Fredrikstad is ca. 13,350 kg of MGO. In Case 3, the estimated fuel consumption for a route from Surnadal to Haugesund is 9000 kg MGO.

For the road transport mode, we have calculated three different alternatives. The actual truck fuel consumption will vary, based on the weight of the cargo, the geography, driver behavior, etc. According to the case company's transport provider, the average fuel consumption is 0.45 liters of diesel per kilometer. To be able to see the effects of these variations we have also calculated the GHG emissions based on an average consumption of 0.4 and 0.5 liters of diesel per kilometer.

Load factor

Load factors have a large impact when calculating GHG emissions in sea transport. A number of sources show varying load factors for short-sea shipping in Norway. A study by Hjelle [START_REF] Hjelle | Atmospheric Emissions of Short Sea Shipping Compared to Road Transport Through the Peaks and Troughs of Short-Term Market Cycles[END_REF] shows that there is a large difference in load factors between southbound and northbound transport routes in Norway. Southbound routes had a load factor of 66 percent, while northbound routes had a load factor of 47 percent. Oterhals et al. [START_REF] Oterhals | NyFrakt : analyse av kystfrakteflåten : flåteutviklingutnyttelsesgradforbedringsmuligheter[END_REF] found that an average load factor for ships operating on the Norwegian coast was 67 percent.

Results of the analysis

Because of the variation in load factors, we chose to analyze the influence that different load factors (namely, 50 percent, 60 percent, and 70 percent) had on the GHG emissions a company will need to "pay" for. We also looked at different values for average fuel consumption for the road transport mode. The results of this analysis are presented in Table 4. Table 5 shows the results of the cost and GHG emissions calculations for the three cases. Case 1a (MDO) shows almost equal GHG emissions for sea and road transport modes, while the sea transport mode is ca. 30 percent cheaper than the road transport mode. In Case 1b (LNG) the GHG emissions are ca. 30 percent lower for sea transport mode than for the road transport mode, and at the same time sea transport mode is ca. 22 percent cheaper than the road mode. Case 2 shows approximately 10 percent lower GHG emissions for sea transport mode compared to the road transport mode, but the cost of sea transport mode is significantly higher (ca. 76 percent) than the road mode. The third case shows substantially lower GHG emissions (almost 40 percent) for the sea transport mode compared to the road transport mode, while the costs are only slightly (ca. 6 percent) higher for sea transport mode than for the road mode.

Discussion

As this study is based only on three cases, its results are not generalizable. However, the findings create a background for discussion around the efficiency of sea and road transport modes in terms of GHG emissions and distribution costs.

Several sources present sea transport as a "by default" greener alternative in terms of emissions of GHG compared to road transport [START_REF] Hjelle | Short Sea Shipping's Green Label at Risk[END_REF]. However, the results of the study presented in this paper show that this is not always correct when different factors are taken into consideration. Below we provide a discussion about the influence of such factors as number of port calls and load factors on GHG emissions and costs connected to transport of loads on relatively short distances. In addition, such factors as ship size and fuel type also influence the level of GHG emissions in each specific case. For instance, according to Hjelle [START_REF] Hjelle | Short Sea Shipping's Green Label at Risk[END_REF], larger ships are more fuel efficient than smaller ones. According to a study by Burel et al. [START_REF] Burel | Improving sustainability of maritime transport through utilization of Liquefied Natural Gas (LNG) for propulsion[END_REF] conducted for a 33,000 DWT tanker ship, usage of LNG leads to a 35 percent reduction of operational costs and a 25 percent reduction of CO2 emissions compared to usage of heavy fuel oil.

Port Calls

An example from Case 1a shows that the fuel consumption for a direct route between the factory and Tromsø is 13,000 kg MDO; however, for a more realistic route with 15-20 stops, the estimated fuel consumption is 18,000 kg MDO. Because the ship in this case is relatively small, the GHG emissions the case company accounts for would be lower if the company chartered the whole ship and shipped its goods directly. Although many ships run on regular routes up and down the coast, many ships make additional calls to other ports, and several ships only make port calls if they are notified of available goods.

Because of these variations, it can be difficult for companies to choose the "greenest" transport alternative.

Load factors

Several sources show that load factors in short-sea shipping in Norway vary from 50 percent to 70 percent. The actual load factors are very important for concluding whether the sea transport mode is "greener" than road transport, as shown in Table 4. The achieved load factors will vary between northbound and southbound routes, between ship-owners, and will depend on the origin and destination of the transported goods.

The results of the present study show that there is no simple answer to the question of whether sea or road transport is more cost-efficient and environmentally friendly over relatively short distances. In order to determine this, each specific case has to be analyzed by comparing the alternatives while considering such factors as transport distance, number of stops, weight/volume ratio of load, load factors, fuel types, and ship sizes.

Conclusion

This paper has presented an analysis of GHG emissions and costs connected to distribution of plastic pipes by sea and road transport modes in Norway. The objective of the study has been to find out whether sea transport is more environmentally friendly in terms of GHG emissions and more cost-efficient than road transport, based on three specific cases. In each case, the calculations were based on the real-world data provided by the case company, ship-owners, and shipping agents. The results from the study show that the widespread opinion that sea transport is "greener" than road transport is not always correct when considering transportation of loads to relatively short distances. For example, loads have different weight/volume ratios; ships on different routes have different average load factors and use different types of fuels; road distances can be longer or shorter than sea distances in each specific case; and ships often have different number of stops on each route. All of these factors need to be taken into consideration in order to create a more realistic picture of which transport mode is cheaper and more environmentally friendly for each specific case.

Table 1 . CO2e emission factors Fuel type Density (kg/l) kg CO2e/kg kg CO2e/l

 1

	Diesel	0.832	3.21	2.67
	Marine diesel oil	0.9	3.24	2.92
	Marine gas oil	0.89	3.24	2.88
	Liquefied natural gas	0.42	2.83	1.18

Table 2 .

 2 Approximate transport distances from the factory to destination (km)

		Sea	Road
	Case 1a, b -Tromsø	977	1272
	Case 2 -Fredrikstad	1064	610

Table 3 .

 3 Influence of different load factors on GHG emissions that transport of different volumes of plastic pipes accounts for

		Sea transport mode		Road transport mode
		GHG emissions, kg CO2e	GHG emissions, kg CO2e
		LF	LF	LF	FC	FC	FC
		50%	60%	70%	0.4 l/km	0.45 l/km	0.5 l/km
	Case 1a, Tromsø	45,913	38,261 32,795	33,972	38,218	42,464
	(MDO), 2253 m 3
	Case 1b, Tromsø	32,068	26,723 22,906	33,972	38,218	42,464
	(LNG), 2253 m 3
	Case 2, Fredrikstad	19,700	16,417 14,072	16,291	18,328	20,364
	(MGO), 1366 m 3
	Case 3, Haugesund	17,037	14,197 12,169	20,297	22,835	25,372
	(MGO), 2253 m 3
	Legend: LF -load factor, FC -fuel consumption

Table 4 .

 4 Sea transport GHG emissions and costs as a percentage of road transport GHG emissions and costs on respective routes (LF 60%, FC 0.45 l/km)

		GHG emissions	Transport costs
	Case 1a -Tromsø (MDO)	100.1%	69.6%
	Case 1b -Tromsø (LNG)	69.9%	77.7%
	Case 2 -Fredrikstad (MGO)	89.6%	176.4%
	Case 3 -Haugesund (MGO)	62.2%	105.7%


Mohan Chiriki

Goal Programming for Supply Chain Optimization with Insufficient Capacity

Keywords: Goal programming, multi-criteria optimization, relaxed constraints

This article presents three different approaches to modeling and solving the classic transportation-location problem including the traditional cost-minimization mixed integer linear program. It is shown that modeling these problems as a profit maximization mixed integer program instead allows for the relaxation of a set of constraints. An alternative multi-objective optimization model using goal programming is also presented. A representative model of the fluid milk supply chain in the U.S. is developed to demonstrate the scenarios and solutions achieved by the three different models to conclude that the multi-objective model is a robust approach to solve these optimization problems even when there is only a single explicit objective.

Introduction

A standard transportation-location problem is defined as shipping goods from origins with fixed capacities to destinations with defined demand requirement. The objective is to select source nodes to open and distribute the goods to destinations subject to capacity and demand constraints minimizing the total cost [1]. Key assumptions are that there is only one kind of good being shipped from the origins to the destinations and that the unit transportation cost does not depend on the volume shipped.

These problems are traditionally modeled as an overall cost minimization mixed integer linear program. However, this approach fails to return a feasible solution when the assumption that "capacity equals or exceeds demand" is not met, which is a fairly common scenario in real life scenarios. One alternative approach is to model the problem as a maximize profit instead of minimize cost, thereby eliminating the necessity to define "capacity equals or exceeds demand" as a hard constraint. Although this mitigates the limitation of the cost minimization model, it does not provide the flexibility to the decision maker to define a minimum requirement at some or all of the demand points. This condition can be incorporated by defining a "supply greater than or equal demand multiplied by fill rate" constraint, where fill rate is specified by the decision maker. The profit maximization model fails to return a feasible solution when there is not enough capacity in the system to fulfil this constraint across all the demand points.

We are interested in modeling this problem in such a way that a best possible solution is returned when there is not enough capacity to fulfil the demand, enabling decision makers to make the most of the situation. This way, they can schedule the shipments or inventory as optimized by the model and arrange for back ordering to fulfil the demand in the remaining nodes at a later point of time. To achieve this, the transportation-location problem can be modeled as a multi-objective optimization model with maximizing fill rate at the demand nodes and minimizing overall system cost.

The following sections present a review of the literature of interest and relevant to this work, the mathematical formulation of the different models described above and an illustrative case study of the fluid milk supply chain in U.S. demonstrating the performance of these models.

Literature Review

Perl and Daskin [START_REF] Hillier | Introduction to Operations Research[END_REF] define and solve a transportation problem simultaneous as a facility location and vehicle routing problem. A mixed-integer programming formulation was developed to minimize the system cost and also a heuristic was presented to solve this large and complex problem which decomposes the original program into three sub problems.

Hillier and Lieberman [START_REF] Maas | Mitigating Hard Capacity Constraints with Inventory in Facility Location Modeling[END_REF] present algorithms for linear programming with multiple objectives. Goal programming is defined as "an approach to establish a specific numeric goal for each of the objectives, formulate an objective function for each objective, and then seek a solution that minimizes the (weighted) sum of deviations of these objective functions from their respective goals". If the different objectives are of same importance, it is a non-preemptive goal program and if there is a priority order for the objectives, it is a preemptive goal program.

Maas et. al. [START_REF] Nicholson | Environmental and Economic Impacts of Localizing Food Systems: The Case of Dairy Supply Chain in the Northeastern United States[END_REF] present a mixed-integer program that captures the operational options that facility managers have to meet demands in excess of capacity for short periods of time. This study empirically demonstrates the operational flexibility available with managers in reality which is not captured in the traditional facility location problems because of the use of hard capacity constraints.

Nicholson et. al [START_REF]Annual Summary[END_REF] present an analysis of localization policies with the case of diary supply chain in the northeastern states of USA. A baseline scenario was defined with the objective is to minimize the cost of the entire system and two scenarios with additional constraints placed on shipments between sources and destinations and then eval-uated on multiple criteria like food miles, supply chain costs, and greenhouse emissions. Milk demand is stated as being seasonal and two typical months of a year were considered-March (supply exceeds demand) and September (demand exceeds supply).

Formulation

In this section, we present the mathematical formulation of the three different models discussed. The decision variables common to all three models are the following. 𝑥 𝑖𝑗 -𝑣𝑜𝑙𝑢𝑚𝑒 𝑠ℎ𝑖𝑝𝑝𝑒𝑑 𝑓𝑟𝑜𝑚 𝑤𝑎𝑟𝑒ℎ𝑜𝑢𝑠𝑒 (𝑖) 𝑡𝑜 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 (𝑗)

𝐵 𝑖 = { 1 𝑖𝑓 𝑠𝑜𝑢𝑟𝑐𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑖 𝑖𝑠 𝑐ℎ𝑜𝑠𝑒𝑛 𝑡𝑜 𝑜𝑝𝑒𝑛, 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒.
The capacity constraint remains the same for all different models and is defined as

∑ 𝑥 𝑖𝑗 𝑗 ≤ 𝑘 𝑖 × 𝐵 𝑖 , ∀ 𝑖 𝜖 𝐼 (1)

Cost Minimization Problem (CMP)

The CMP solves for the decision variables x ij and B i by minimizing the overall cost of the system, formulated in (2) subject to (1) and the demand constraint defined in [START_REF] Maas | Mitigating Hard Capacity Constraints with Inventory in Facility Location Modeling[END_REF].

Min ∑ 𝑓 𝑖 𝐵 𝑖 𝑖 + ∑ ∑ 𝑥 𝑖𝑗 𝑐 𝑖𝑗 𝑗 𝑖 (2) ∑ 𝑥 𝑖𝑗 𝑖 = 𝑑 𝑗 , ∀ 𝑗 𝜖 𝐽 (3)

Profit Maximization Problem (PMP)

The PMP solves for the decision variables by maximizing the overall profit of the system, and the objective function is defined in (4).

Max -∑ 𝑓 𝑖 𝐵 𝑖 𝑖 + ∑ ∑ 𝑥 𝑖𝑗 (𝑝 -𝑐 𝑖𝑗 ) 𝑗 𝑖 (4)
The capacity constraint remains the same as in (1) but the demand constraint (2) is modified as in [START_REF]Annual Summary[END_REF]. An additional constraint (6) can be imposed if a minimum fill rate is required at each demand point.

∑ 𝑥 𝑖𝑗 𝑖 ≤ 𝑑 𝑗 , ∀ 𝑗 𝜖 𝐽, (5)
∑ 𝑥 𝑖𝑗 ≥ ℎ 𝑗 𝑑 𝑗 , ∀ 𝑗 𝜖 𝐽 𝑖 (6)

Multi Criteria Problem (MCP)

A new set of decision variables is introduced in this model. Fill rate h j for each demand point j, defined as the fraction of demand to be supplied at each destination j such that

0 ≤ ℎ 𝑗 ≤ 1, ∀ 𝑗 𝜖 𝐽 (7)
The two objectives of this model are to maximize the fill rate across the system and to minimize the overall cost, which can be mathematically expressed as

𝑀𝑎𝑥 ∑ ℎ 𝑗 𝑗 , (8)
𝑀𝑖𝑛 ∑ 𝑓 𝑖 𝐵 𝑖 𝑖 + ∑ ∑ 𝑥 𝑖𝑗 𝑐 𝑖𝑗 𝑗 𝑖 [START_REF]Population by ZTCA; using American FactFinder[END_REF] Subject to (1), ( 7) and a modified demand constraint defined in (10).

∑ 𝑥 𝑖𝑗 𝑖 = ℎ 𝑗 𝑑 𝑗 , ∀ 𝑗 𝜖 𝐽 (10)
This problem can be modelled to be solved as a linear program introducing auxiliary slack variables U, E and target values for the different objectives.

ℎ 𝑗 + 𝑈 𝑗 1 -𝐸 𝑗 1 = 𝑇 𝑗 1 ∀ 𝑗 𝜖 𝐽, (11)
∑ 𝑓 𝑖 𝐵 𝑖 𝑖 + ∑ ∑ 𝑥 𝑖𝑗 𝑐 𝑖𝑗 𝑗 𝑖 + 𝑈 2 -𝐸 2 = 𝑇 2 , ( 12
)
𝑈 𝑗 1 , 𝐸 𝑗 1 , 𝑈 2 , 𝐸 2 , 𝑇 𝑗 1 , 𝑇 2 ≥ 0 (13)
The final objective function for MCP is defined in [START_REF]LINGO -The Modeling Language and Optimizer[END_REF], where M is an arbitrarily large number, and subject to constraints (1), ( 7), (10), ( 11), [START_REF] Beilock | Modeling Weekly Truck Rates for Perishables[END_REF], and (13).

𝑀𝑖𝑛 𝑀 ∑ 𝑈 𝑗 + 𝐸 𝑗 (14)

Illustration and Results

A mathematical model of the fluid milk supply network in the U.S. was developed with the geographic centers of each mainland state excluding Washington D.C. as the candidate locations for the sources. Three scenarios were developed, differing on the system capacity (SC) available to satisfy the overall demand (OD). These scenarios are differentiated by the ratio SC/OD and any minimum fill rate (FR) condition specified (h i in (6) and T j 1 in (11)) at each destination and are listed in Table 1.

Demand and Supply Data

The warehouse capacities were sized proportionally to the fluid milk produced in that particular state as obtained from USDA [START_REF]Milk Production[END_REF][START_REF]Capacity of Refrigerated Warehouses 2015 Summary[END_REF][START_REF]Calculated (or Derived) based on data from The Nielsen Company (US), LLC and marketing databases provided by the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business[END_REF]. The annual milk production data from USDA was scaled to the time horizon to obtain the supply capacity of each warehouse and fixed costs of opening such warehouses have been estimated proportionally.

We model the demand points as the U.S. Zip codes and use a dataset acquired from the marketing company, Nielsen, via the James M. Kilts Center for Marketing at the University of Chicago, Booth School of Business [START_REF]Population by ZTCA; using American FactFinder[END_REF] to calibrate our demand. The Nielsen Homescan Consumer Panel dataset contains six years of longitudinal panel data of consumer products. The data files were consolidated and segregated so as to obtain parameters in accordance with the purpose of our analysis. This extracted data was combined with the population distribution in zip code tabulation areas from the U.S. Census Bureau [START_REF]Population by ZTCA; using American FactFinder[END_REF] to calibrate the demand in two different time periods of the year, when demand exceeds supply and when there is sufficient capacity to meet demand.

Longitudinal and latitudinal parameters were attributed to the zip codes to compute the distance matrix for the network using the formula presented in [START_REF]CPLEX User's Manual[END_REF], where lat1, lat2, lon1 and lon2 are the latitudes and longitudes of the two points respectively and R ~ 3,963 miles [START_REF] Weisstein | Great Circle[END_REF] is the equatorial radius of the Earth returning distance between two zip codes as flown by a crow in miles [START_REF] Beilock | Modeling Weekly Truck Rates for Perishables[END_REF]. It is assumed that all shipments are done in regular sized, fully loaded refrigerated trucks for which an average rate/mile is computed, taking into account fuel charges and refrigeration costs. Using a conversion factor, the distance in miles and capacity of each truck, the shipping rate per each unit can be computed using simple arithmetic [13,[START_REF]LINGO -The Modeling Language and Optimizer[END_REF].

cos -1 [sin(𝑙𝑎𝑡1) sin(𝑙𝑎𝑡2) + cos(𝑙𝑎𝑡1) cos(𝑙𝑎𝑡2) cos(𝑙𝑜𝑛2 -𝑙𝑜𝑛1)] * 𝑅 (15)

Benchmarking Results

All three models of a simplified version of the problem were formulated and solved in two software packages -Lindo Systems' LINGO [START_REF]CPLEX User's Manual[END_REF] and IBM's ILOG CPLEX [16] on a Windows 10 machine with Intel Core i7-2670QM CPU @ 2.20 GHz and 8 GB of memory. This version considers only the transportation part of the original problem with all the source locations open, thereby making it a pure linear program and reducing the solving times. Both the software packages returned identical solutions in all three scenarios as can be seen in Table 2 and their performance is benchmarked for solving time in Fig. 1. CPLEX, being considerably faster was chosen to solve the complete MILP under different scenarios. All three models return a global optimal solution in Scenario 1 as expected. The PMP yields the same solution as CMP even with (2) modified to (4) because each additional unit of demand supplied contributes to an increase in profits and hence, the model has an inherent incentive to satisfy as much of the demand as possible. This property enables the PMP to return a best feasible solution in Scenario 2 when the overall demand is greater than the system capacity while CMP reaches infeasibility. Best feasible can described as satisfying the most profitable portion of the demand, given the system capacity. As for the MCP, first the fill rates for the demand nodes are decided depending on the system capacity and then, the shipping schedules are optimized for minimal cost. In Scenario 1, all nodes are given a fill rate 1 and in Scenario 2, the nodes with least cost to fulfill demand were allotted a fill rate 1. This adds up to the least expensive demand to fulfill within the system capacity. The solutions returned by PMP and MCP in Scenario 2 are identical and termed best feasible because all the constraints have not been satisfied completely, but have been satisfied to the best capabilities of the system.

In Scenario 3, we simulate a situation where the decision makers want to maintain a minimum fill rate h j at each of the demand nodes, which is done by introducing (6) with h i = 0.8 into the PMP and changing the T j 1 to 0.8 in the MCP. The PMP reaches infeasibility because constraint set (6) cannot be satisfied at all the nodes. The number of nodes supplied by the MCP is more in Scenario 3 than Scenario 2 as the overall capacity is the same but each node is now being filled up to 80% only.

Identical results were returned when the complete MILP problem was run on CPLEX, but with longer solving times than the pure LP problem. These times can be seen in Fig. 2 and there is an increase in solving times of CMP and PMP to MCP as expected, because of the introduction of a new set of decision variables.

Conclusions

Modeling the classic transportation-facility problem as a multi-objective optimization problem with maximizing fill rate at demand points and minimizing overall system cost proved to be a versatile and flexible approach. The model achieved the objective of returning the best feasible solution to decision makers as can be seen in the results of the illustrative example. While the PMP performed better than the CMP in solving the MILP problem, it could not offer the level of constraint flexibility the MCP could.

The problem size in MCP is larger due to introduction of new variables -one heuristic could be to check for the system feasibility and then choose between the PMP or MCP models to find the shipping schedules. As the demand and transportation matrices are sparse matrices, with numerous 0 entries, the solving times can be reduced by using heuristic algorithms to traverse through these matrices. Further studies could be conducted on the effect of the target values defined in MCP on the solution quality, defining the second objective in MCP as a profit maximization, and the solutions differ when the priorities of the two objectives are switched.

Fig. 2 .

 2 Fig. 2. Solving times in seconds in CPLEX for complete MILP

  The followings are the model parameters. 𝐼 -𝑆𝑒𝑡 𝑜𝑓 𝑠𝑜𝑢𝑟𝑐𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠 𝐽 -𝑆𝑒𝑡 𝑜𝑓 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛𝑠 𝑜𝑟 𝑑𝑒𝑚𝑎𝑛𝑑 𝑝𝑜𝑖𝑛𝑡𝑠 𝑓 𝑖 -𝑓𝑖𝑥𝑒𝑑 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑙𝑜𝑐𝑎𝑡𝑖𝑛𝑔 𝑎 𝑓𝑎𝑐𝑖𝑙𝑖𝑡𝑦 𝑎𝑡 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒 𝑠𝑖𝑡𝑒 𝑖 𝜖 𝐼 𝑘

𝑖 -𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝑤𝑎𝑟𝑒ℎ𝑜𝑢𝑠𝑒 𝑎𝑡 𝑠𝑖𝑡𝑒 𝑖 𝜖 I 𝑐 𝑖𝑗 -𝑐𝑜𝑠𝑡 𝑜𝑓 𝑠ℎ𝑖𝑝𝑝𝑖𝑛𝑔 𝑜𝑛𝑒 𝑢𝑛𝑖𝑡 𝑓𝑟𝑜𝑚 𝑠𝑖𝑡𝑒 𝑖 𝑡𝑜 𝑛𝑜𝑑𝑒 𝑗 𝜖 𝐽 𝑑 𝑗 -𝑑𝑒𝑚𝑎𝑛𝑑 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡 𝑎𝑡 𝑒𝑎𝑐ℎ 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑗 𝜖 𝐽 ℎ 𝑗 -𝑓𝑖𝑙𝑙 𝑟𝑎𝑡𝑒 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑠 𝑎𝑡 𝑒𝑎𝑐ℎ 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑗 𝜖 𝐽 𝑝 -𝑢𝑛𝑖𝑡 𝑠𝑒𝑙𝑙𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒 𝑜𝑓 𝑝𝑟𝑜𝑑𝑢𝑐𝑡

Table 1 .

 1 Definition of scenarios for benchmarking

	Scenario	SC/OD	FR

Table 2 .

 2 Results achieved by the three models under different scenarios

	Scenario	Model	Result
	Scenario 1	CMP	Global Optimal
		PMP	Global Optimal
		MCP	Global Optimal
	Scenario 2	CMP	Infeasible
		PMP	Best Feasible
		MCP	Best Feasible
	Scenario 3	CMP	Infeasible
		PMP	Infeasible
		MCP	Best Feasible

Fig. 1. Solving times in seconds for Lingo and CPLEX under different scenarios