Shigeki Umeda
email: shigeki@cc.musashi.ac.jp

Sensitivity analysis of reverse supply chain system performance by using simulation

Keywords: Reverse supply chain, Reverse logistics, Simulation, Performance evaluation, ANOVA

This paper proposes a methodology of performance sensitivity analysis of reverse supply chain systems by using simulation. This paper discusses two types of reverse logistics model: PUSH-type and PULL-type. And, it proposes a generic method to analyze system performance by using discrete-event simulation and factorial experiment design. The characteristics of reverse supply chain sy stems (PUSH-type and PULL-type) are shown in detail. The result of these analyses would provide useful data for planning reverse supply chain systems.

Introduction

Supply chain management (SCM ) has received tremendous attentions both from the business world and fro m academic researchers during the last two decades. SCM is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize systemwide costs while satisfying service level requirements. Problems for supplier selection [START_REF] Amin | An integrated model for closed-loop supply chain configuration and supplier selection[END_REF] and performance evaluation models [START_REF] Estampe | A framework for analyzing supply chain performance evaluation models[END_REF] are, fo r examples, discussed from various points of view.

In the last decade, due to environmental and ecological responsibility, enterprises are trying to reuse, remanufacture and recycle the used products to reduce the neg ative impact on environ ment, especially the manufacturers of the electrical consumer products. Requirements for corporate responsibility and sustainability are getting more urgent. Kara and Onut discussed a two-stage stochastic and robust programming approach to strategic planning of a reverse supply network through a case of paper recycling supply chain [START_REF] Kara | A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recy cling[END_REF]. Kenne et al. applied a similar approach to production planning of a hybrid manufacturing-remanufacturing system under uncertainty within a closed-loop supply chain [START_REF] Kenne | Production planning of a hybrid manufacturingremanufacturing system under uncertainty within a closed-loop supply chain[END_REF]. Kocabasoglu et al. discussed a investment issue on supply chains linking with reverse flo ws [START_REF] Kocabasoglu | Linking forward and reverse supply chain investments: The role of business uncertainty[END_REF]. Ku ma and Malegeant discussed a closed-loop supply chain thorough a case of manufacturer and eco -non-profit organization [START_REF] Kumar | Strategic alliance in a closed-loop supply chain, a case of manufacturer and eco-non-profit organization[END_REF]. Nativ i and Lee discussed RFID information-sharing strategies on a decentralized supply chain with reverse logistics operations [START_REF] Josenativi | Impact of RFID information-sharing strategies on a decentralized supply chain with reverse logistics operations[END_REF]. Rah man and Subramanian scoped computer recycling operations in reverse supply chain and analyzed factors for implementing system operations [START_REF] Rahman | Factors for implementing end-of-life computer recycling operations in reverse supply chains[END_REF].

Performance analysis of supply chain systems is a critical issue in its design stage. Simu lation is such a generic approach that gives solutions of performance analysis of supply chain systems. Chan et al. applied simulat ion to analysis of impact of co llaborative transportations in supply chain systems [START_REF] Chan | The impact of Collaborative Transportation M anagement on supply chain performance: A simulation approach[END_REF]. Chatfield et al. developed a supply chain simu lation system by using an object-oriented modeling method [START_REF] Chatfield | SISCO: An object-oriented supply chain simulation system[END_REF]. Labarthe et al. proposed an agent-based modeling and simu lation of supply chain systems [START_REF] Labarthe | Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context[END_REF]. Umeda and Lee developed a general purpose supply chain simulator [START_REF] Umeda | Integrated Supply Chain Simulation -A Design Specification for a Generic Supply Chain Simulation[END_REF].

Tannock et al. developed a data-driven simu lation of aerospace sector's supplychain [START_REF] Tannock | Data-driven simulation of the supply -chain-Insights from the aerospace sector[END_REF]. Yoo et al. proposed a hybrid algorith m for d iscrete event simu lation based supply chain optimization [START_REF] Yoo | Hybrid algorithm for discrete event simulation based supply chain optimization[END_REF]. Zhang et al. used a simulat ion software for analysis of a demand-driven Leag ile supply chain Operations Model [START_REF] Zhang | Research on Demand-driven Leagile Supply Chain Operation M odel: a Simulation Based on AnyLogic in System Engineering[END_REF]. Persson and Olhager applied a performance simu lation of supply chain designs. This work is based on discrete-vent simu lation technologies [START_REF] Persson | Performance simulation of supply chain designs[END_REF], meanwh ile, Fiala used SD simulat ion to analyze information sharing in supply chains [START_REF] Fiala | Information sharing in supply chains[END_REF]. Tako and Robinson reviewed jou rnal papers that use these modeling approaches to study supply chains, published b etween 1996 and 2006 are rev iewed. A total of 127 journal art icles are analyzed to identify the frequency with which the two simu lation approaches are used as mode ling tools for DSS in LSCM [START_REF] Tako | The application of discrete event simulation and system dynamics in the logistics and supply chain context, Stewart Robinson[END_REF]. Previous researches discussed system concepts of reverse supply chain system, and proposed methodologies of performance evaluation by using simulation metho dologies. This paper proposes a methodology of performance evaluation of reverse supply chain systems by using simu lation and experiment design. Generic models are introduced and analysis examp les of indiv idual features will be provided [START_REF] Umeda | Performance Analysis of Reverse Supply Chain Systems by Using Simulation[END_REF].

2

Scenarios and models

Reverse logistics scenarios

Reverse logistics systems require taking back products from customers and the repairing, remanufacturing (value-added recovery), or recycling (material recovery) the returned products. The reverse logistics in supply chains is strongly related to all sta ges of a product development and is also a crit ical problem to all level of the industry.

There are many types of reverse logistics [START_REF] Gupta | Sustainable supply chain management: Review and research opportunities[END_REF]. We, here, consider a virtual supply chain system, which is composed of the following components: Chain manager, Su pplier, Manufacturer, Retailer, Customers, Co llector, and Remanufacturer (Fig. 1). This model supposes home electric appliances such as PCs, TVs, and refrigerato rs.

Supplier, Manufacturer, and Retailer are members that form arterial flo ws (produ ction generation flows) in a chain. Supplier provides parts or materials to Manufacturer according as supply orders fro m Chain manager. Manufacturer provides products to Retailer according as production orders from Chain manager. Retailer provides pro ducts to Customer according as Demand (Purchase) order fro m Customer. Customer uses products and disposes them (generates the disposed materials).

Meanwhile, Collector and Remanufacturer are members that form venous flows (reverse logistic flo ws) in a chain. The Co llector reclaims used products from Cu stomer, when he/she disposes the used product. And, it detaches reusable materials fro m the disposed product, and sends them to Re manufacturer. Remanufacturer regenerates products by using materials provided by Co llector. And, it provides them to Manufacturer, such as spare-parts.

Chain Manager is a supervisor of the chain the processes order informat ion in the chain. It receives de mand order fro m Customer. It pred icts demand in next ordering duration by using Customer's order. It also gives production orders production orders to Manufacturer and Supplier by using the predicted demands. Deliverer connects these members and carries materials fro m its upstream to its downstream.

The configuration of these members is shown in Fig. 1 and Fig. 2. These models are based on an analogy between arterial-venous blood flows in a hu man body and material-flo w in a supply chain. Solid lines are production generation flow (arterial-flo w), mean while, dashed lines are reverse logistics flo w (venous -flow) in Fig. 1 and Fig. 2. Arterial-flows and venous-flow should be synchronized with each other. The system synchronizes venous flows with arterial flo ws.

Reverse logistics models

The flow fro m Customer to Remanufacturer by way of Co llector is a reverse logistics flow. Customer sends "used-products" to Collector, when Customer d isposes them. The role of Collector is to distinguish reusable materials fro m the disposed products, and stores them. Th is paper introduced two types of logistics model that controls this reverse logistics flow: PUSH-type and PULL-type.

The PUSH-type is that Co llector and Remanufacturer sends reverse products to Manufacturer in an orderly manner. In PUSH-type, remanufactured products are s equentially pushed into Manufacturer, synchronizing with occurrence of reverse. Remanufactured product would be kept as material inventory in Manufacturer. In PUSH-type, remanufactured products are sequentially pushed into Manufacturer, synchronizing with occurrence of reverse. Remanufactured product would be kept as material inventory in Manufacturer (Fig. 1).

Meanwhile, the PULL-type is that Collector and Remanufacturer work according as PULL signals fro m their downs-streams. In PULL-type, reverse products are stocked at Collector. These products stay at there, during no PULL signal fro m Remanufacturer. And, Remanufacturer does not work until it receives PULL signal. In Fig. 2, Collector works as "Stock-driven" mode. Co llector continuously observes stock volu me at Remanufacturer. It starts to produce products when the stock volu me is smaller than the stock-replenishment level, and continues to work until the stock volume is equal to or greater than the stock-volume level. Th is works according to the following operational sequences:

1. Collector periodically observes stock volume data at Remanufacturer. 2. Collector starts producing while stock volu me at Remanufacturer goes down b elow the stock-replenish ment level. 3. Collector stops producing when the stock volume reaches the stock-volu me level. This logic is also applied to the case of between Remanufacturer and Manufactu rer. 3 System sensitivity analysis by using simulations

Preliminary experi ments and Experi ment design

We, first of all, d id preliminary experiments to extract major of this system model. The conditions of this experiment are: simu lation duration (100 days), Customer's orders interval (5 days), Distribution function of customers' demands (Unifo rm distribution between 6 lots to 10 lots (U(6,10)), and Collection rates of Collector (high level (0.6) and low level (0.2)). This experiment result demonstrates that models and collection rates are major factors giving effects on system performance. Tab le.1 re presents the differences between PUSH-type reverse and PULL-type reverse. The PULL system indicates higher utilization of Collector than the PUSH system. In PUSH system, the Collector wo rks only when the materials arrive fro m its Upstream (Customer). Meanwhile, in PULL system, Co llector wo rks to replenish inve ntories at the downstream (Remanufacturer). This mechanism, accord ingly, makes higher resource utilization, when the Collection Rate is at lo w level. In both PUSH system and PULL system, all of the reusable materials generated at Customer (market) are t ransferred to Collector. In PUSH system, the gathered materials in Collector are sent to Remanufacturer, which is a re -production process. After this regeneration process, materials accu mulate on Manufacturer as its input materials. Meanwhile, in PULL system, the reusable materials staying at Collector would be transferred to Remanufacturer, only when the withdrawal signals fro m its downstream has been occurred. Therefore, reusable materials stocked in Collector demonstrates an upward trend. Th is reason suppresses increase of the materials in both Remanufacturer and Manufacturer.

Analysis of Vari ance (ANOVA)

Based on the above discussion, we configure a factorial design of simu lation experiments. Three factors are defined;  Factor A: "Log istics types", PUSH-type and PULL-type  Factor B: "Range of demand distribution". Three distribution functions are defined U(4,12), U(6,10), and U(7,9), respectively.  Factor C: "Co llect ion Rate: Three rates are defined, h igh-level (0.7), middle-level (0.4), and low-level (0.1), respectively.

Therefore, 18 simu lation runs are required. Factorial experiments are designed with respect to these three factors. Table 1, 2, and 3 represent inventory means . The factor A (Logistics type) and the factor C(Collection Rate) are significant in th e case of Manufacturer (Table .4). The factor A (Log istics type) and the factor B (Demand variance) are significant in the case of Retailer (Table.5). And, the factor A (Logistics type) and the factor C (Co llect ion rate) are significant in the case of Colle ctor (Table .6). The F value o f factor A (Logistics type) is large in every case. This result is as a corollary. The effect of Collection rate variance is large in Manufacturer and Collector. Th is result is considered reasonable and proper judging by chain structure.

In contrast, it is Retailer that the effect of demand variance (factor B) is large. Moreover, it should be noted that mutual factor with factor A (Logistics type) is large.

Manufacturer and Collector are sensitive with Factor A and Factor C ( Collection Rate).

Conclusion and future research

Full factorial design of simulat ion experiments and analysis of variance (ANOVA) represent that difference of systems factor gives a large influence on system performance of reverse supply chain systems. Manufacturer and Retailer are, especially, affected by interactions of independent factors. In PUSH system, material inventory volume at Manufacturer increases according as time progress. Meanwhile, the inventories at both Collector and Remanufacturer do not fluctuate so much. In PULL system, the material consumption at Collector synchronizes with material inventory volume at Remanufacturer, and the material consumption at Remanufacturer synchronizes with material inventory volume at Manufacturer. When the Manufacturer possesses sufficient volume of input material, Remanufacturer does not need to provide Manufacturer with materials any more.

The next stage of this simulation analysis will need to consider processes cost factors at both reverse supplier (Co llector and Remanufacturer). When the regeneration process at both Collector and Remanufacturer is expensive, the PULL system would be better choice.

Fig. 1 .Fig. 2 .

 12 Fig. 1. PUSH-type reverse logistics model

Table 1 .

 1 Simulation results (Utilizations of each supply chain member)

	Model	Collection Rate Utilizat ion@	Utilizat ion@	Utilizat ion@
			Manufacturer	Collector	Re-manufacturer
	push	0.6	0.92	0.32	0.30
	push	0.2	0.92	0.12	0.10
	pull	0.6	0.91	0.36	0.21
	pull	0.2	0.90	0.24	0.15

Table 2 .

 2 Average of inventory volumes at "M anufacturer"

	Factors			C (Collection Rate)
	A(Logistics types)	B(Demand)	0.1	0.4	0.7
	PUSH	D(4,12)	27	30	36
		D(6,10)	25	32	37
		D(7,9)	25	30	35
	PULL	D(4,12)	33	33	33
		D(6,10)	34	34	34
		D(7,9)	33	33	33

Table 3 .

 3 Average of inventory volumes at "Retailer"

	Factors			C (Collection Rate)
	A(Logistics types)	B(Demand)	0.1	0.4	0.7
	PUSH	D(4,12)	8	8	8
		D(6,10)	13	13	13
		D(7,9)	10	10	10
	PULL	D(4,12)	11	11	11
		D(6,10)	9	9	9
		D(7,9)	8	8	8

Table 4 .

 4 Average of inventory volumes at "Collector"

	Factors			C (Collection Rate)
	A(Logistics types)	B(Demand)	0.1	0.4	0.7
	PUSH	D(4,12)	11	13	17
		D(6,10)	11	14	16
		D(7,9)	11	14	17
	PULL	D(4,12)	2	3	9
		D(6,10)	2	3	8
		D(7,9)	2	3	7

Table 5 .

 5 Analysis of Variance (ANOVA) of "M anufacturer")

	Factor	Squared Sum	Freedom	M ean Square	F0
	A(Logisics)	29	1	29	38.67**
	B(Demand)	5	2	2.5	3.33
	C(Collection)	81	2	40.5	54**
	AxB	0	2	0	0
	AxC	78	2	39	52**
	BxC	3	4	0.78	1.04
	Error	3	4	0.75

Table 6 .

 6 Analysis of Variance (ANOVA) of "Retailer"

	Factor	Square Sum	Freedom	M ean Square	F0
	A(Logisics)	5	1	5	40**
	B(Demand)	14	2	6.8	54.4**
	C(Collection)	0	2	0	0
	AxB	39	2	19.5	156**
	AxC	0	2	0	0
	BxC	1	4	0.25	2
	Error	1	4	0.125

Table 7 .

 7 Analysis of Variance (ANOVA) of "Collector"

	Factor	Square Sum	freedom	M ean square	F0
	A(Logisics)	401	1	401	1604**
	B(Demand)	0	2	0	0
	C(Collection)	108	2	54	216**
	AxB	0	2	0	0
	AxC	2	2	1	4
	BxC	3	4	0.78	3.12
	Error	1	4	0.25


Nils Weinert

Rafael Fink

Christian Mose

Friedrich Lupp

Florian Müller

Jan Fischer

Ingo Bernsdorf
email: ingo.bernsdorf@siemens.com

Alessandro Cannata
email: alessandro.cannata@siemens.com

Comprehensive improvement of industrial energy efficiency: Pilot case in a European rolling stock factory

Keywords: eco-factory, rolling stock manufacturing, energy efficiency

Energy and resource efficiency requires more suitable approaches to be consistently implemented in industry. Factories are more complex systems than residential or commercial buildings; in industrial domain, improvements for energy efficiency require measures that are difficult to be identified and that vary from building to manufacturing processes fields. This paper presents a comprehensive approach to systematically address industrial energy efficiency improvement. The approach has been developed and tested at a European rolling stock manufacturer. Insights from the pilot case presented confirm that this holistic view helps properly address energy efficiency in industrial domain.

Introduction

Manufacturing is one of the main consumers of global primary energy consumption and producer of related emissions. Improvement of energy efficiency in factories is a key driver to support the achievement of European 20/20/20 goals. Hence, industry has to rethink current approaches about design and management of manufacturing systems, to take a significant step towards energy efficient factories. However the task is challenging, since factories are complex systems made of interacting elements such as people, production assets, material handling equipment, building service equipment, etc. A holistic approach which, moving away from local optimization, considers the factory as a whole has been developed within EMC²-Factory, an on-going FP7-European Research project (www.emc2-factory.eu).

To show its industrial applicability, the approach has been tested in a real case, a European rolling stock factory several measures to improve energy efficiency have been identified and developed, of which the most relevant and impactful are presented in this paper.

The approach is briefly introduced in section 2; section 3 and 4 describe the pilot case and the identified measures; section 5 includes final conclusions.

Approach adopted

EMC²-Factory project aims at developing new solutions for planning and operating eco-efficient factories, as well as using existing methods and tools combining them in a more effective way. An integrated view has been adopted in the project to prevent problem shifting due to the mentioned complex system interconnections, e.g. from production to technical building services. This integrated view fully considers all the components in a factory and their reciprocal influence on each other. Solutions (both developed within EMC²-Factory and available externally) for this variety of fields are very diverse and end up in a high-number of alternatives that the decision maker can choose. Moreover, since factories are significantly different for every specific case, this complicates the decision making for energy efficiency improvement in the individual industrial case. Hence, a method to identify the case-specific measures has been developed. This method, of which details are published in [START_REF] Fischer | Method for selecting improvement measures for discrete production environments using an extended energy value stream model[END_REF], helps the user to systematically address the most impactful measures and optimize energy efficiency and lean production for the overall factory system by selection of appropriate solutions for a specific discrete production environment. Solutions, in this context, are for instance technologies, organizational principles and frameworks or software tools developed in the EMC²-Factory project by all partners, in addition to existing and established approaches from academia and industry. The methodology was tested against the pilot described in the following chapteran existing Siemens AG rail car factory. The methodology is based on two phases with six sequential process steps. A numerically ranked set of applicable solutions is derived by narrowing down possible solutions from a larger set and thus improving the proposed ranking stepwise. In the first phase, the user specifies preferences for certain types of solutions by specifying criteria values that differentiate between solution elements. Criteria are e.g. the applicable factory level, the life cycle phase or the problem solving phase. Each criterion is then numerically weighted against one another by the user. In the second phase, a hierarchical, class-based factory model is set up, mainly describing the physical elements of a factory. This model is used in the extended energy value stream analysis (EEVSA) [START_REF] Posselt | Extending Energy Value Stream Models by the TBS Dimension -Applied on a Multi Product Process Chain in the Railway Industry[END_REF] to store the properties of the physical elements required for deriving the values of the EEVSA. The main goal of the EEVSA is to map the total energy consumption of a factory to individual process chains/valuestreams, taking all peripheral systems into account. Based on the KPIs coming from the EEVSA, a set of certain Energy Drivers can be ranked in order to differentiate sources for the total energy consumption of processes. In the next step the ranked Energy Drivers are mapped to the so-called Design Parameters, returning a ranked set of these. Design Parameters are levers of a factory that can usually be utilized to improve energy efficiency or lean performance (e.g. factory layout, long-term production schedule, machine control). Since the correlation between Design Parameters and the solutions can be numerically approximated, these solutions can be ranked in a subsequent mapping step by matrix multiplication. The ranked solutions are then selected for application in a final step, using Total Cost of Ownership (TCO) methodology and cross-impact analysis.

Pilot case description and motivation

The selected pilot case is a Siemens AG rolling stock manufacturing site located in Vienna. It produces rail vehicles of stainless steel or of aluminium for Metros, Coaches and Light Rail. Several processes are there performed, such as machining, grinding, joining, coating, and final assembly in a shop floor area of 63000 m². Before this research activity started, the site already addressed the topic of energy and resource efficiency with different projects on building renovation (insulation, ventilation systems, etc.), a new surface treatment center, and product design improvement from product life cycle perspective (e.g. lighter materials to consume less energy during the use phase of the trains, use of recyclable material), however the holistic factory perspective adopted in this project was not yet part of the considerations. In order to show impact of the developed approach on energy efficiency, the pilot has been focused on a single building of the site. In this building, the main train components such as undercarriages, roofs and sides of a train car are manually grinded and manually and/or automatically welded. Moreover in the same building two machining centres are available for pre-and post-welding processes. With an area ca. 4500 m², the building was chosen for three reasons:  Representative: It includes typical manufacturing processes of train production;  Relevant: It shows complex material flow and interaction among processes;  Challenging: It was recently improved from energy perspective such as air heat exchangers, building insulation, and air doors. Hence, further improvement requires going beyond state-of-the-art technologies and approaches.

Overview of measures analysed

For the investigated building and the affected part of the metro car's value stream, the solution finding process was applied as described in chapter two. First the as-is analysis has been performed gathering both energy and production-related information. Using the results of the EEVSA, the solution finding process was performed with several iteration loops. Each iteration loop was defined with different user preferences according to the momentary step in the problem solving process and the related focus of application (e.g. analysis vs. solution phase, process vs. process-chain perspective). Fig. 2 depicts the results of an exemplary iteration loop, targeting the analysis phase and the value stream process utilizing the biggest electrical consumer.

Fig. 2. Results of exemplary iteration cycle of solution finder

After several iterations, the solution finder indicated the biggest potential for energy management, production planning, alternative welding-technologies and layout.

Energy Transparency and Management System

As one of the common principles in engineering, knowledge gained from transparency is often seen as a prerequisite for improvement. Following this maxim, as a first step certain relevant production entities have been energetically measured using onetime measurements. First insights on energy consumption and distribution were used as input for the solution finder [START_REF] Weinert | Lean and Green Framework for Energy Efficiency Improvements in Manufacturing[END_REF].

One-time measurements are sufficient for providing a basic knowledge on the consumption in a steady application, but in a dynamic environment like present in production continuous monitoring is required to detect shortages. Beyond developing transparency, actual management of energy consumption becomes possible, leading to the decision to implement an Energy Management System (EMS) for the investigated building. The system consists of several measuring points for electrical energy and pressurized air. Using an industrial PC, measured consumptions are monitored automatically in real time, generating signals for control measures like machine standby opportunities or air leakage detection. The architecture (Fig. 3) is designed for later enhancements, e.g. automated interventions in those cases, or for being integrated in an overall, factory-wide Energy Management System as required, e.g. by ISO 50001 or by future integration in Smart Grid environment.

Fig. 3. Energy Management System -concept

Production Planning Tool

Depending on the range and the complexity of the goods produced production planning can be a difficult task. Presumably that is one of the reasons why most contributions from scientific literature and scheduling principles applied in practice often just focus on single economic aspects like the adherence to delivery dates or the minimization of the make-span. Yet, production planning is a highly multi-objective task, in general. If, for instance, it is not accounted for potentials to improve energy efficiency during the planning process, these potentials will not be tapped in the production execution phase, either.

In the following some examples for levers are provided to include the aspect of energy efficiency into the production planning process:  A minimization of non-value-adding work (e.g. setup and transportation activities) helps to avoid the wasting of time and energy. Additionally it reduces the logistic complexity of the production process.  An appropriate choice of the utilized production machines and modes can help to reduce the energy cost without harming other planning objectives (e.g. if alternative machines are available or velocity can be regulated).  Load balancing and the minimization of peak loads can contribute to significantly lower energy costs, if peak-load is charged and the production process is very energy intensive.  A smart production organization (e.g. synchronization of individual shift systems) can help to reduce energy costs related to TBS like air conditioning, lighting, heating and exhaustion systems, etc.  The anticipation of applied control strategies (which e.g. decide if and when machines are switched off during idle periods) can lead to a smoother course of production and therefore contribute to improve energy efficiency. Based on this list of levers and the circumstances in the industrial pilot case, a multicriteria production planning software tool was developed and implemented within the EMC²-Factory project. With the help of that tool production planners can now simultaneously optimize the production process with respect to different KPIs like adherence to delivery dates or the energy consumption. Tradeoffs between these KPIs can now be quantified by calculating and evaluating not just one single solution but a set of alternative non-dominated schedules. The algorithmic approach is based on a multi-start priority rule based schedule construction heuristic. For details on the algorithmic concept that has been realized in the software tool see [START_REF] Fink | A priority rule based multi criteria scheduling framework for energy efficiency aware detailed production planning[END_REF].

Alternative Welding Technologies

In a factory, the total energy consumption is significantly higher than the actual energy required for the main, value adding manufacturing steps. One reason for this is the effectiveness of the processes as well as secondary processes that are required to perform the core process. Furthermore, a complete process chain includes several preand post-processes consuming energy themselves, e.g. milling a work piece as preparation for a joining process. Additionally large variety of secondary energy consumers is required, including maintenance and transport operations and technical building services (TBS) [START_REF] Mose | Evaluation of Process Chains for an Overall Optimization of Manufacturing Energy Efficiency[END_REF].

All of these pre-, post-and secondary processes are consuming energy only for enabling the actual value adding manufacturing steps. Consequently, in the design of a process chain, it is not sufficient to focus on the main steps only, but rather to investigate energetic demands more comprehensive to achieve an overall optimum.

For doing so, process designers need to have the possibility to compare alternative process chains by providing an indicator, including the energy demand of all relevant primary and secondary consumers. This key element has been developed as a processdependent, normative energy indicator. Focussing on welding processes as the prevalent manufacturing process in the rail car industry, the specific energy demand per welded length is used. The indicator was defined as the accumulated energy over all considered process states and the whole production sequence, divided by the total length of weld created in that time [START_REF] Mose | Process and process chain evaluation for an overall optimization of energy efficiency in manufacturing -The welding case[END_REF]. Dominant factors influencing the indicator are found within the areas of:

 A particular process technology (e.g. GMAW or FSW);  Particular process parameters for the observed work piece;  Auxiliary systems implemented in the observed manufacturing equipment;  Amount of non-productive time and associated energy demand on the equipment;  Organizational influence by equipment's productive -non-productive time ratio;  Organizational influence through the load of the equipment.

The approach introduced was applied in the Siemens plant to investigate alternative welding processes. Essential results are that within welding processes for aluminium, Friction Stir Welding (FSW) has an advantage over Gas-Metal-Arc Welding (GMAW) regarding its energy demand of the main process and even more for secondary consumptions -mainly caused by the fume emission of GMAW and the required exhaust systems -emphasizing the need to explicitly consider these in process chain design. However, a short term change of process chains for existing products is difficult to realize due to reasons of approved designs and processes, etc. Anyways, now the data is available to consider this advantage for future product designs.

4.4

Layout Reconfiguration

The production system layout is of special interest for an energy efficient design of buildings. A production system is the surface organization and the area where production environment conditions (e.g. illumination, temperature) interact with the equipment. While general planning guidelines (e.g. minimal floor load zones) are state of the art in planning, the planning of elements like layout, TBS and disposal infrastructure across interfaces requires to enforce communication, collaboration and a faster decision making. Additional multidisciplinary experts need to provide knowledge and analytical methods together if the planning activity is properly designed.

A workshop-based green layout planning method was developed to position machines and equipment with environmental aspects in mind (for details see [START_REF] Müller | Green Factory Planning -Framework and modules for a flexible approach APMS[END_REF]). Concerning the pilot case three alternative layouts were developed that can be beneficial:

 Separation of welding and machining: Welding and machining of metal parts requires filtering and exhaust ventilation of a different kind. If welding and machining operations would be locally separated smaller and better designed TBS for one specific function could be used. A local decentralized exhaust system would be another option to avoid a central TBS system completely. Security and process barriers caused by electrical conductibility of car bodies make this an unrealistic option.  Isolated preheating zone for car body: Because of quality reasons the car body needs to have a certain temperature to be processed so a preheating time up to 24 hours can be necessary in the facility. Therefore additional heat will be supplied through the radiators. Preheating next to surface treatment to use waste heat from there would make the heating system consume less energy and reuse otherwise wasted thermal sources.  Discrete milling area: Despite local chip disposal, coolant system and workplace lighting the two milling centers are on the both outsides of the building with machining and welding operations. If the milling would be separated, there would be no exhaust and TBS system required. Additional transport way and handling effort has to be considered for the production planning.

Since cost for a building and TBS change are disproportionately high the implications have to be considered in the future building (re-)planning of the site. Nevertheless they are general principles, whose implications from environmental side can be easily identified through layer evaluation (for details on this approach see [START_REF] Müller | Integrierte Bewertung als zentraler Bestandteil einer grünen Fabrikplanung[END_REF]).

Discussion and conclusions

The proposed approach started from creating transparency with an energy management system and covered different areas normally assessed only for productionrelated performance (production planning, substitution of processes and layout reconfiguration). The solutions highlighted from the approach are highly interdisciplinary and they address the challenge of energy efficiency from different perspectives. Two transversal remarks came out from testing the approach:

1. To address energy efficiency, the complete system made of production machines, processes, TBS, and building shell has to be analysed as entity. For example, the improvement of the direct energy requirement for welding process (local optimization), can have less impact than the reduction of energy for auxiliary systems (fume exhaust) or to the substitution of process technologies to avoid fumes. 2. Although countless measures for energy efficiency improvement are available, each case has specific features and issues that can be almost unique. An approach that helps to structure the problem and focus on the most impactful measures is a promising approach to extend and facilitate decision making when implementing energy efficiency measures in industrial domain.

Since industrial decision-making is driven by cost-benefits analysis, the presented approach should include economical analysis to be smoothly accepted by decisionmakers. This aspect is already under consideration and will be integrated in the approach within future developments.

6

Fig. 1 .

 1 Fig. 1. Representation of energy sankey diagram performed for the pilot case

Acknowledgment

This research is partially funded by the European Commission under the 7 th Framework Programme -research project EMC²-Factory (www.emc2-factory.eu).


S Zanoni

B Marchi

Optimal sizing of Energy Storage Systems for industrial production plants

Keywords: Energy storage systems, renewable energy sources, batteries

Most developed countries around the world are seriously concerned about recent global warming, the depletion of fossil fuels and environmental degradation. So as to meet the environmental burden reduction targets set by different international agreements, manufacturing companies are seriously encouraged to invest considerable efforts in the field of energy. Energy storage systems (ESS) have the potential to revolutionize the way in which electrical power grids are designed and operated. Presently, power grids require that the generation of electricity continuously balance the demand. The constant balancing of supply and demand has significant operational and cost implications. Incorporation of storage devices into the grid should reduce this constraint by enabling electrical energy to be withdrawn from the grid when there is excess generation and held in reserve until needed. In this work we consider stationary applications with medium discharge time (minutes to hours), thus batteries have been considered. The object is to find the optimal sizing of the energy storage device (i.e. batteries) with which it is possible to minimize the cost of energy in a production plant.

Introduction

Most developed countries around the world are seriously concerned about recent global warming, the depletion of fossil fuels and environmental degradation. Accordingly, effective energy use and reduction of greenhouse gas (GHG) emissions are becoming increasingly important goals. The development and use of renewable energy sources (RES) has experienced rapid growth over the past few years and, in the next 20-30 years, all sustainable energy systems will have to be based on the rational use of traditional resources and greater use of renewable energy [START_REF] Ibrahim | Energy storage systems-Characteristics and comparisons[END_REF]. One of the main characteristics of most of the RES is the variability in the energy production, generally linked to environmental conditions, often disjointed from real necessity of energy from users. Valuable energy produced by RES can be effectively used by storing surplus energy in a storage device and using it when required; in this way RES can be used to aid the transition for a newer and cleaner energy generation technology [START_REF] Mahlia | A review of available methods and development on energy storage; technology update[END_REF].

The main characteristics of electricity is that ideally it must be used at the same time and in the same place as it is generated. The proper amount of electricity must always be provided to meet the varying demand, and an imbalance will damage the stability and quality (voltage and frequency) of the power supply even when it does not lead to totally unsatisfied demand. The issues caused by these characteristics are:

--High generation cost during peak--demand periods;

--Need for continuous and flexible supply to meet changing power consumption, relying on an accurate forecast of the variations in demand;

--Needs for a more efficient grid: in order to avoid congestions, interruptions in the supply …

In the last years, the concept of self-consumption has become increasingly relevant thanks to the many benefits that introduces in terms of energy efficiency. The use of ESS leads to a growth of the rate of energy self-consumed and improves the utilization of the PV system. For that reason, in this paper we will analyse the affordability of energy storage for an industrial plant. We consider the scenario in which a PV system is integrated with an ESS. The analysis made leads to relevant considerations on the barriers and opportunities on the use of energy storage devices.

The paper will be structured as follow: in Section 2 is presented a literature review on the ESSs; in Section 3 it is shown the proposed model and the results of the analysis and, finally, in Section 4 the concluding remarks are presented.

Energy Storage System

ESS refers to a process of converting electrical energy from a power grid into a form that can be stored for converting back to electrical energy when needed [START_REF] Chen | Progress in electrical energy storage system: A critical review[END_REF]. ESSs have the potential to revolutionize the way in which electrical power grids are designed and operated. Presently, power grids require the constant balancing of supply and demand and it has significant operational and cost implications. Incorporation of storage devices into the grid would relax this constraint by enabling electrical energy to be withdrawn from the grid when there is excess generation and held in reserve until needed [START_REF] Bradbury | Economic viability of energy storage systems based on price arbitrage potential in real-time U.S. electricity markets[END_REF]. ESS is expected to play an important role thanks to the rapidly accelerating rate of technological development with anticipated unit cost reductions [START_REF] Chen | Progress in electrical energy storage system: A critical review[END_REF] (Figure 2).

$/kWh

Year

There are two major emerging market needs for ESS as a key technology: to utilize more renewable energy and less fossil fuel, and the future Smart Grid. The increased ratio of RES generation may cause several issues in the power grid. First, the fluctuation in the output of RES generation makes system frequency control difficult. Secondly, RES output is undependable since it is affected by weather conditions, and any surplus power may be thrown away when not needed on the demand side (see Figure 3). ESS may intervene against those issues, stabilizing RES output, increasing self-consumption and through time shifting (i.e. matching the supply and demand of energy). Therefore valuable energy can be effectively used by storing surplus electricity in ESS and using it when necessary and it can also be sold when the price is high (i.e. generation shifting scenario). As is said in [START_REF] Rudolf | Financial analysis of utility scale photovoltaic plants with battery energy storage[END_REF], the generation shifting scenario is expected to be the first widespread application of ESSs as it is the one that may potentially generate additional revenues in a PV project. ESSs can be characterized in terms of: energy and power capacity, round trip efficiency, response time, discharge duration, discharge frequency, depth of discharge and self-discharge. A complete definition of those characteristics can be found in [START_REF] Bradbury | Economic viability of energy storage systems based on price arbitrage potential in real-time U.S. electricity markets[END_REF]. A widely-used approach for classifying ESS systems is the determination according to the form of energy used: mechanical, electrochemical, chemical, electrical and thermal ESSs [START_REF]Electrical Energy Storage[END_REF]. Different applications with different requirements demand different features form electrical ESS. In this paper we consider stationary applications with medium discharge time (minutes to hours), in which electrochemical ESS (i.e. batteries) -for larger capacities -and flywheel energy storage (FES) are the dominant technology. Rechargeable/secondary battery is the oldest form of electricity storage. As is said in [START_REF] San Martín | Energy Storage Technologies for Electric Applications[END_REF], batteries are devices that convert chemical energy into electrical energy, by oxidation and reduction of their materials. The details of various types of batteries have been discussed in numerously paper - [START_REF] Ibrahim | Energy storage systems-Characteristics and comparisons[END_REF] - [START_REF] Chen | Progress in electrical energy storage system: A critical review[END_REF] and [START_REF] Divya | Battery energy storage technology for power systems-An overview[END_REF]. Batteries are in some ways ideally suited for electrical energy storage applications because they provide fuel flexibility and environmental benefits, they can respond very rapidly to load changes and enhance the system stability. Batteries usually have very low standby losses and can have high energy efficiency. The construction of a secondary battery is facilitated by the short lead times and the technology's modularity. However, battery storage has some disadvantages too: low energy densities, small power capacity, high maintenance costs, a short cycle life a limited discharge capability and most batteries contain toxic materials. Hence the ecological impact from uncontrolled disposal of batteries must always be considered [START_REF] Chen | Progress in electrical energy storage system: A critical review[END_REF]. Some of the most used technologies are:

1 6 11 16 21 Power [kW] Time [h] Load Max Avg Min
Lead Acid, Sodium Sulphide (NaS) and Lithium Ion (Li-Ion) Batteries. In Table 1, are shown the main characteristics from literature of the mentioned batteries:

The Model

The object of this work is to analyze the scenario in Figure 5, in order to find the optimal sizing of the energy storage device (i.e. batteries) with which it is possible to minimize the cost of energy management and purchase in an industrial system. Figure 3. Reference scheme of the PV+ESS system [START_REF] Hanna | Energy dispatch schedule optimization for demand charge reduction using a photovoltaic-battery storage system with solar forecasting[END_REF] The main notation used is given in the following table: The model consists in the minimization of the total annual cost of energy (i.e. cost for the energy purchased from the grid and costs due to the investment and use of BESS): ( ) (1) where

minTC P nom ( ) = c e,t ⋅
DOD P P nom ⋅ = max (2) C BESS P nom ( ) = C BESS,0 P nom ( ) + C BESS,i P nom ( ) (1+ ρ) i i ∑ n with i = L BESS , 2 ⋅ L BESS ,.., N -L BESS , i > 0 (3) ( ) ( ) ( ) i P C P C nom BESS nom i BESS ln 0 , , ⋅ - = α (4)
The constraints of the model are following reported:

( ) ( ) ( )

0

≤ ≤ [START_REF] San Martín | Energy Storage Technologies for Electric Applications[END_REF] Due to the presence of several key technologies of electrochemical storage, it has been decided to compare different scenario considering the three types of batteries mentioned in the previous section. The proposed model guarantees that the hourly demand of the production plant is met by the joint action of the energy generation of the PV system and the energy stored in the batteries. The connection to the power grid serves as a backup resource, if the production from RES is not sufficient to cope with the demands.

The data used in the implementation of the model are referred to an industrial plant in Italy already equipped with a photovoltaic system (PV), whose profiles of demand and PV production are shown in Figure 6. For the electricity cost and incentives it has been considered the regulatory framework of the country. In addition to the total cost, it is interesting to observe how the values of the following indexes change: self-consumption (share of the energy produced that is selfconsumed without being fed into the grid) and self-sufficiency (share of total energy demand that is satisfied with that produced by the PV system).

Table 3 shows the results of the model: In Figure 7, it is possible to observe the benefits and costs introduced with BESS. As it can be seen, the use of new more performing technologies (Li-Ion) is replaced by mature technologies because of the cost of investment still too high. When the investment in storage technologies is profitable, we attend an increase of both the indices (i.e. self-consumption and self-sufficiency). See Table 3.

Pnom [MW]

Lead Acid NaS Li--Ion --Lower financial incentives (or even higher energy cost) leads to an increased installed power (Figure 8a); --Total costs are very sensible to the cost of the batteries; thus, with a low reduction of the battery's capital cost it is possible to obtain a great increase of the benefits (Figure 8b). Even Li-Ion batteries become affordable, when their cost is at least halved.

Conclusions

ESSs is a recent and relevant topic in energy efficiency, as they may help to overcome the intermittency of RES and they may also be used to shift electricity to times when it costs less. Thus, it will be very interesting and useful to consider its impact in a production plant, already equipped with a PV system. This work is one of the first attempt to depict the problem of affordability of the integrated system PV+ESS. Similar analysis has never been yet considered in the industrial context: actually, does exist only some studies on the domestic storage.

In the present work an analytical model has been developed so as to perform an economic analysis of the problem presented, in order to analyse the economic convenience of an investment in energy storage devices. From this point of view the analysis is strongly affected by the country and year considered (due to the incentive framework). From the analysis carried out, it can be concluded that the economic benefit introduced through the use of BESS and due to the increase of plant's selfconsumption is highly dependent on the type of battery used, and most importantly,

P * nom [MW]

ΔBattery's Cost on its cost of investment. In the specific case considered, it is possible to observe that Lead Acid and Sodium Sulphide batteries results convenient while, on the contrary, the most efficient Lithium-Ion batteries are still too expensive. Given the recent advances in technology, in the next few years, it is expected a trend characterized by a significant reduction in the investment cost that will make the same Lithium-Ion battery affordable: i.e. able to generate economic benefits greater than the costs introduced in the system. Finally it should be noted that the analysis included here, if properly adjusted, could be applied to any kind of production plants, ESS (e.g. batteries, supercapacitors, …) and energy system (e.g. wind power, biomass or biogas systems).

Figure 1 .

 1 Figure 1. Lithium Ion Battery Price per kWh From the viewpoint of consumers, the main roles of ESS are:--Time shifting and cost savings by using ESS to reduce peak power and to purchase electricity at off-peak times; --Emergency power supply to operate during an outage.

Figure 2 .

 2 Figure 2. Energy consumption and PV production

Figure 4 .

 4 Figure 4. Demand and PV Production patterns over a year

Figure 5 .

 5 Figure 5. Total annual energy cost varying the nominal power of the BESS

Figure 6 .

 6 Figure 6. Sensitivity analysis: (a) reduction of Financial Incentives and (b) the Batteries' Cost

Table 1 .

 1 Batteries' Characteristics

		Lead Acid	NaS	Li--Ion
	Cost [€/kWh]	150	250	1000
	Round Trip Efficiency [%]	80	89	98
	Life [cycle]	2000	2500	4000
	Life [years]	12	15	20
	DOD [%]	80	100	80
	Self--Discharge [%/month]	5	0	1

Table 2 .

 2 Main Notation

	E stored,t	Energy stored at the beginning of t [kW h]

p e,t

Unit price to sell a kWh to the grid in t [€/kW h]

  E grid,t P nom ( ) -p e,t ⋅ E sold,t P nom ( ) -p self ,t ⋅ E self ,t P nom ( )

	8760
	∑	(	)	+ C BESS P nom
	t=1

Table 3 .

 3 Model's Results

				PV		PV+BESS
					Lead Acid	NaS	Li-Ion
	P *	nom	[MW]	0	3,83	4,21	0
	TC	[€/year]	1.351.550,12 1.323.523,91	1.306.654,44	1.351.550,12
	Self-Consumption	[%]	36%	44%	44%	36%
	Self-Sufficiency	[%]	30%	36%	37%	30%


Peter G Higgins
email: phiggins@swin.edu.au

Mohammad Yarahmadi
email: myarahmadi@swin.edu.au

Cooperation as a driver of development and diffusion of environmental innovation

Keywords:

Environmental management and innovation literature has revealed gaps concerning the influence of business cooperation on environmental innovation and diffusion. Cooperation with external partners in the development of environmental innovation is explored using the Australian Business Longitudinal Database. Complementing this is the exploration of the cooperation of supply chain partners in the adoption of environmental products and processes through three case studies from the manufacturing industry.

Introduction

Despite the many advantages of environmental innovations, they are not easily developed and diffused. Unwillingness of firms to promote their development and adoption are in part due to the characteristics of environmental innovations. Other factors are associated with the internal characteristics of a firm (e.g., the lack of financial resources, skills, knowledge and physical capital) and the external environment (e.g., the lack of pressure from influential stakeholders). Forming partnerships, however, may be a feasible strategy to overcome these barriers. Partners may support each other in managing common problems, such as awareness of and compliance with new environmental regulations. Partners may provide access to resources and capabilities that are not available in-house.

In many aspects, development of environmental innovation represents a separate sub-group of general innovation with the aim of avoiding or reducing environmental harm. Likewise, diffusion of environmental innovation across the supply chain requires firms applies a subset of processes known as green supply chain practices. Both buyers and suppliers are involved in implementing these practices, with the aim to eliminate or reduce environmental impacts.

This paper focuses on the role of cooperation between businesses in development of environmental innovation and diffusion of environmental practices across the supply chain. It explores the extent to which buyer organisations implement such practices and the corresponding ways they interact with their suppliers.

2

Nature of environmental innovation

Carillo-Hermosilla et al. [START_REF] Carrillo-Hermosilla | Diversity of eco-innovations: Reflections from selected case studies[END_REF] distinguish three groups of environmental innovation in processes: component, sub-system and system. Component change consists of incremental improvements to extant systems, in which companies opt for less demanding incremental changes to "end-of-pipe" technologies for reactive solutions for polluting emissions [START_REF] Klassen | Collaboration and evaluation in the supply chain: the impact on plant-level environmental investment[END_REF]. In sub-system changes, the replacement or modification of underlying systems eliminates sources of some emissions. Whereas, system changes are systemic redesigns of open-loop systems-in which polluting emissions breach system boundaries-into closed loops that divert wastes into inputs for new processes.

Trifilova et al. [START_REF] Trifilova | Sustainable Innovation in Action: Experiences from China[END_REF] distinguish four types of environmental innovation, from the sterile to the potent. The "weakest" type is the chicanery they labelled passive/cosmetic in which there is "cosmetic" publicity of "green credentials" without any active innovation. Next is improving existing products or processes, followed by opportunity-driven creation of new products or processes that go beyond compliance with the aim to gain competitive advantage. System-level creation of innovation by engaging external organizations in designing new processes and services is the most efficacious. It belongs to the class of systemic improvements that cause radical instrumental changes in place of incremental changes normally associated with technological change [START_REF] Carrillo-Hermosilla | Diversity of eco-innovations: Reflections from selected case studies[END_REF], [START_REF] Hellström | Dimensions of environmentally sustainable innovation: the structure of eco-innovation concepts[END_REF].

Characteristics beyond conventional innovation

The factors associated with environmental innovation are more expansive than conventional innovation, which focuses on the development of a new or improved product or process that is a "game changer" in terms of functionality and/or productivity. It includes changes to social norms, cultural values and institutional structures [START_REF]OECD: Eco-innovation in industry : Enabling green growth[END_REF][START_REF] Rennings | Redefining innovation: eco-innovation research and the contribution from ecological economics[END_REF][START_REF] Falk | Inventing a sustainable future: Australia and the challenge of ecoinnovation[END_REF]. Drivers of conventional innovation are market expectations and consumer preferences (demand pull) and research and development (push factors). Environmental innovation also often has governmental regulation as a key driver [START_REF] Rennings | Redefining innovation: eco-innovation research and the contribution from ecological economics[END_REF], [START_REF] Porter | Toward a new conception of the environmentcompetitiveness relationship[END_REF]. Where an environmental innovation is not aligned with normal drivers of the market, its success depends on transformation of societal behavior through changes to beliefs, knowledge and values [START_REF] Hellström | Dimensions of environmentally sustainable innovation: the structure of eco-innovation concepts[END_REF]. Hellström [START_REF] Rennings | Redefining innovation: eco-innovation research and the contribution from ecological economics[END_REF] contends that as eco-innovation needs evolution in social arrangements and institutional support, a radical innovation strategy is required to break out of entrenched social practices that incremental changes do not provoke.

An innovator of a new product or process has to ensure that the competitive advantage gained has to significantly offset the disadvantages accrued in its development. A competitive advantage is dependent in part on legal protection of intellectual property, but critically on the ownership of valuable and rare resources and capabilities that cannot be easily imitated and in which there are no readily available substitutes. To offset financial exposure, companies may pool resources by collaborating with other businesses for mutual gain.

Unlike conventional innovation, dominant drivers may not be located in the market. While wide adoption of an environmental innovation reduces the impact on the environment, this positive externality may not be realized. If it is not valued by the market, then it is likely to fail, with consequential impact on the financial sustainability of the innovating firms. Consequently, the likelihood of them becoming a casualty of the gap between the private and social returns provides little incentive in becoming pioneers of environmental innovation [START_REF] Hellström | Dimensions of environmentally sustainable innovation: the structure of eco-innovation concepts[END_REF].

Cooperation and Environmental Innovation

In the literature on environmental innovation a commonly stated proposition is that the inclusion of environmental aspects requires more extensive cooperation with external sources than does conventional innovation of products or process [START_REF] Peña | Business cooperation from theory to practice[END_REF][START_REF] Chesbrough | The logic of open innovation: managing intellectual property[END_REF][START_REF] Terziovski | The relationship between networking practices and business excellence: a study of small to medium enterprises (SMEs)[END_REF][START_REF] Beyerlein | Innovation through collaboration[END_REF][START_REF] Mohannak | Innovation networks and capability building in the Australian hightechnology SMEs[END_REF][START_REF] Soosay | Supply chain collaboration: Capabilities for continuous innovation[END_REF][START_REF] Bigliardi | Open innovation practices and supply chain management: a case study in the food machinery supply chain[END_REF]. In testing the proposition that research and development tends to move from an internal activity to collaboration with other organizations, Yarahmadi and Higgins [START_REF] Yarahmadi | Motivations towards environmental innovation: A conceptual framework for multiparty cooperation[END_REF] explored the relationship between environmental innovation and cooperation between businesses by reprocessing data from the Business Longitudinal Database, which is the product of a survey of 2,732 Australian businesses by the Australian Bureau of Statistics for the period 2006 to 2007. They deduced that for 28.8% of firms that environmental innovation was a major reason for businesses to cooperate, which they compared to 15.3% claiming cooperation for other types of innovation (see Table 1). However, this increased to 73.3% and 60% for customers and suppliers, respectively. Putting this in context of conventional innovation, 37.7% stated that they had introduced improvements in goods or services, operational processes, organizational/managerial processes or marketing methods. While cooperation may be significant for innovation, by far a major reason for innovation was profitrelated (79.4% of respondents), with factors concerning responsiveness to customer needs, maintenance of market share, establishment of new markets and increasing efficiency of delivering goods and services (54.1%, 47.3% and 39.2% of respondents, respectively) were also dominant reasons for innovation. The least important drivers for innovation were responding to government regulations (10.9%) and standards and reducing environmental impacts (12.1%).

The industrial sector that had the largest group of innovators was manufacturing1 . While 35% of firms within this sector were innovators, only 8.23% of these were environmental innovators. The largest group of environmental innovators was in the construction industry; 21% were innovators of which 20% were environmental innovators. Cooperation -other 60% 46.6% Performing a logistic regression analysis of the data, Yarahmadi and Higgins [START_REF] Yarahmadi | Motivations towards environmental innovation: A conceptual framework for multiparty cooperation[END_REF] found three significant variables associated with innovation: cooperation, research and development, and size of the firm. Thus, partnering and investment on R&D distinguish between firms who do and do not introduce environmental innovation and the distinction is very strong. Size of the firm is significant and its coefficient is positive, indicating that larger organizations are more engaged in introducing environmental innovations than small to medium enterprises. Nevertheless, exports, grants, subsidies and industry were not significantly different from zero at 5% level.

Firms that introduced innovations-20% in the construction, 12.82% in the wholesale trade, 11.90% in the transport and storage, and 11.76% in the personal and other services industry-reported reduced environmental impacts (environmental innovation) as the prime motivator. Surprisingly, given the size and propensity of the Australian mining industry, it showed no evidence of innovation that aimed to reduce impact on the environment. For firms that identified the environment as a main concern, manufacturing firms scored as the largest environmental innovators with 25%. For the wholesale trade, construction, personal and other services, and transport and storage, environmental innovation were 19%, 12%, 12%, and 10%, respectively.

There are numerous reasons why firms do not develop or adopt environmental innovations [START_REF] Biondi | Achieving sustainability through environmental innovation: The role of SMEs[END_REF][START_REF] Schaper | The challenge of environmental responsibility and sustainable development: Implications for SME and entrepreneurship academics[END_REF][START_REF] Collins | Business networks and the uptake of sustainability practices: the case of New Zealand[END_REF]. Nevertheless, it is not easy to come up with a solution, as this is a multi-faceted issue. However, there is ongoing research on the positive role of cooperation with external partners in helping firms to develop environmental innovations. The analysis of the data set of Australian businesses also confirms this hypothesis. The sign and the magnitude of the regression coefficient of cooperation show this strong relationship. According to the resource-based view and institutional theory, the advantages and competitive benefits that result from cooperative arrangements include: access to new markets, acquisition of sources of technical support and expertise, increased market power, risk and investment sharing, economies of scale and scope, reductions in government or trade barriers, and the acquisition of institutional legitimacy [START_REF] Dacin | The legitimacy of strategic alliances: An institutional perspective[END_REF][START_REF] Lin | Strategic Alliances for Environmental Protection[END_REF]. Nevertheless, as Carrillo-Hermosilla et al. [START_REF] Carrillo-Hermosilla | Diversity of eco-innovations: Reflections from selected case studies[END_REF] state, this may be a chicken and egg situation, where the more competency that firms possess, the more they engage in cooperation.

Case Studies

Yarahmadi, Clements and Higgins [START_REF] Yarahmadi | Greening supply chains: compliance and monitoring-oriented practices vs. cooperative-oriented practices[END_REF] further explored the role of cooperation through in-depth interviews of three manufacturing firms in Victoria, Australia. The manufacturing was chosen as it is a major contributor of greenhouse gas emissions (27.7% total emissions of Australian industry), ranking second to the agriculture industry. Contrastingly, they are among the largest green innovators (25% of all Australian industry [START_REF] Yarahmadi | Motivations towards environmental innovation: A conceptual framework for multiparty cooperation[END_REF]). The firms were selected to contrast situations (see Table 2). For the objective of exploring practices of green supply chains, the firms selected had already proven to be environmentally aware and had provided evidence of commitment to adopt or develop environmental practices. Also, they were medium or large size manufacturers with the ability to influence their suppliers. Firm B1 is a supplier of automotive parts, for which the supply network quality assurance is mandated to comply with ISO/TS16949:2009. Reducing costs and meeting delivery times are major concerns. However, although an environmental system is not mandatory for the suppliers of B1, it prefers that its suppliers are certified to ISO14001 or have either an environmental policy or engaged in some environmental activities such as recycling. To control for the conformity of suppliers with desired criteria, B1 monitors suppliers regularly and checks the expiry date of their certificates. If a supplier fails to meet the requirements, B1 first considers the extent that the failure is affecting its business and operations. In one occasion, B1 had to stop dealing with a supplier because the supplier did not have adequate OH&S, environmental and quality systems and was not willing to take any action.

Firm B2, a clothes manufacturer, has hundreds of stores worldwide. It has a few thousand employees in Australia, including employees in the retail sector. The company does not have an environmental department. However, it has a "risk and compliance department" that handles environmental issues. Its major suppliers are Asian, with the China being the largest. Suppliers must agree not to avoid environmental harm. Compliance is checked through twice yearly audits that examines suppliers against ethical, environmental and quality requirements. B2's expectation is in line with ISO standards ISO9001, ISO14001 and ISO18001 for quality, environmental, and health and safety issues, respectively. Nevertheless, since certification is difficult to obtain, B2 evaluates the suppliers only on the critical elements of those standards. The audits are conducted without notice: either by an internal team from B2 or a third party.

Firm B3, a manufacturer of water-related equipment that has 180 employees, is certified to ISO 9001 and ISO14001 and sources its materials and products both locally and abroad. It has worldwide export to many countries. Its environmental policy emphasises continuous improvement of its environmental performance. The company has adopted a proactive approach towards the environment since 2006 and has, as a consequence, won environmental awards. As its policy emphasises waste reduction, it demands that all packaging must consist of recycled materials (paper, wood paper, and cardboard). Other than these demands, it does not incorporate environmental requirements in tenders or contracts. Their audit of suppliers is purely quality driven. Furthermore, it does not dictate suppliers to use environmental management system such as ISO14001.

A comparison of three case studies suggests that there are similarities and differences in the way purchasers implement Green Supply Chain Practices. In all cases, buyers implemented compliance and monitoring practices. For B1 and B2, there were formalised practices for assessing and evaluating suppliers. Although environmental criteria are part of the suppliers' rating, they were not mandatory. All buyers indicated that decisions regarding selecting or maintaining a supplier are more flexible concerning environmental qualifications than price, quality and delivery.

In implementing green compliance and monitoring-oriented practices, the three cases treat all suppliers similarly. Contrastingly, for cooperative-oriented practices, buyers prefer to work with those suppliers who are keen and a major part of their business. One possible justification for such an approach is that compliance practices are less expensive to implement, while safeguarding buyers from the risk of breaching regulatory requirements due to the poor environmental performance of their suppliers. On the other hand, cooperative arrangements would not generate successful outcomes, unless participants dedicated resources and were willing to share their knowledge and capabilities, which in fact is not easy to handle.

When asking buyers about the impact of their practices on suppliers, compliance practices were directed towards avoiding risks of not meeting legal requirements. These practices, at their maximum potential, have enforced suppliers to generate incremental innovations. For instance, B3 enforced suppliers to change their packaging. Whereas, cooperative-oriented practices exhibit more breakthrough and systematic changes as in the case of B1's supplier, the partnership lead to certification to ISO14001. Likewise, the cooperation between B2 and a supplier provided the opportunity to produce a more efficient product.

Conclusion

The findings from the study of the Business Longitudinal Database imply that cooperating with external partners increases the likelihood of introducing environmental innovations. Having this knowledge helps corporate managers with intention to develop environmental innovation strategies to reinforce their relationships with existing partners (e.g., supply chain partners) or/and form new partnerships. The implication for policy makers is to set regulations and strategies [START_REF] Porter | Toward a new conception of the environmentcompetitiveness relationship[END_REF], [START_REF] Horbach | Determinants of environmental innovation: New evidence from German panel data sources[END_REF] that facilitate the cooperative activities of businesses.

The conclusion of the case studies is that buyers use both compliance and monitoring-oriented and cooperative-oriented practices to some degree. However, the results suggest that there is more emphasis on compliance practices and firms are in the early stages of incorporating environmental expectations into their supplier selection procedures. Cooperative practices are not yet well established and there were only limited occasional cooperation with suppliers on small environmental improvement projects. Further research could study how buyers and suppliers can move beyond compliance to cooperative practices and become more innovative in their supply chains with benefits to all supply chain members.

Regardless of the industry sector, while all claimed cooperation with suppliers on environmental innovations, examples of such practices were not demonstrable. The main reason for this could be that environmental issues are perceived as new areas for collaboration but are not historical suppliers' measures. Another reason could be that environmental innovations are costly and the payback period of such investments is often longer than other business investments. Therefore, those firms who have access to limited fund prefer to invest it on joint initiatives that result in more immediate returns.

Table 1 .

 1 Major factors for environmental innovation derived from Business

	Longitudinal Database.
	Factor	Main reason: environmental-	Main reason:
		innovation	Other
	Size 0-4	13.5%	26.4%
	5-19	44.2%	36.1%
	≥20	42.3%	37.5%
	Exports	25%	25.7%
	Subsidies	5.8%	3%
	Grants	15.4%	9.8%
	R&D	30%	15.9%
	Cooperation (Yes/No)	28.8%	15.3%
	Cooperation with clients,	73.3%	39.7%
	customers
	Cooperation with suppli-	60%	48.3%
	ers

Table 2 .

 2 Characteristics of interviewed firms

		Firm B1	Firm B2	Firm B3
	Sector	Automotive	Clothing	Machinery and Equipment
	Size (number of employees)	139	~3000	180
			No: but has risk
	Presence of an environmental department	Yes: embedded function in the quality	an compliance handles environ-department that	Yes: six employees
			mental issues
	Environmental management system	Yes: ISO 14001 No	Yes: ISO 14001

Some reservation most be noted on the representativeness of these results. Most firms in the database were small: 46% had less than 19 employees. Furthermore, manufacturing and construction firms only comprised 16.4% and 5.2% of the dataset, respectively.


Vidyuth Anand

Seokgi Lee

Vittaldas V Prabhu
email: prabhu@engr.psu.edu

Energy-Aware Models for Warehousing Operations

Keywords: Energy control, Queueing, Warehousing, Forklifts

There is a growing need in industries worldwide to become more sustainable and energy efficient. Due to rapid increase in demand of goods, there has been a rise in demand of logistics and operational services. This necessitates needs for a large number of warehouses and distribution centers to satisfy demand. It is imperative that warehouses follow the same sustainable development model practiced in other industries. This paper extends energy efficiency techniques suggested for manufacturing to warehousing. Specifically, warehouses are modeled as M/M/c queues where forklifts are servers and this model is used to evaluate performance of energy control policies. The model is then extended to general distribution queues. Experiments based on real-world data yield results that indicate that for system utilization values between 40% and 100%, as the number of servers in the system increases by a factor of 4, energy consumption increases by a factor of 3.78.

Introduction

The world today has incorporated a more ecological approach in the utilization of energy resources, targeted toward the exploration of "green" options and renewable sources of energy. Various efforts have been taken to understand and reduce the consumption of energy in the manufacturing sector [START_REF] Prabhu | Simulation Modelling of Energy Dynamics in Discrete Manufacturing Systems[END_REF]. From a logistics standpoint, research has been undertaken to find more sustainable choices due to increase in demand of various products. To put this into perspective, the amount of money invested in business logistics in the U.S. in 2012 was $1.33 trillion, which is 8.5% of the GDP [2]. Warehousing, a critical element in the logistics sector, accounts for 8% of the total energy consumption of all commercial buildings in the country [START_REF] Williams | A No-Brainer: Retrofitting Warehouse Heating Systems[END_REF]. According to an analysis of the energy consumed in warehouses/distribution centers (DCs) [START_REF]Energy Information Administration: Commercial Buildings Energy Consumption Survey[END_REF], HVAC and lighting are critical components for both non-refrigerated and refrigerated warehouses, accounting for a significant portion of energy utilization.

Discounting the smaller consumers of energy such as office equipment, it is safe to assume that other than heating, ventilation and air conditioning (HVAC), a predominant contributor to energy usage is the movement of material from place to place within the DC. Material movement contributes to a significant portion of the final product's cost, and warehouses contain specific "Warehouse Management Systems" (WMS) to aid in handling the material, including non-automated and automated systems [START_REF] Rouwenhorst | Warehouse design and control: Framework and literature review[END_REF]. Automated Storage/Retrieval Systems (AS/RS) are an important tool used in material handling in warehouses and most modern factories for work-in-process storage [START_REF] Hur | A performance estimation model for AS/RS by M/G/1 queuing system[END_REF]. However, manually operated forklifts continue to play a major role in the efficient functioning of a warehouse. In 2012 alone, the top twenty manufacturers of lift trucks worldwide shipped $30.4 billion worth of forklifts, and the forklift domain is increasingly becoming a platform to enable better technology for more productivity. Forklifts, in performing the functions of order picking and put-away in warehouses, contribute to most of the energy consumption among all other material handling systems in terms of fuel cells and electric charge required [START_REF] Greenway | Fuel Cell Lift Trucks: Key Considerations for Fleet Conversion[END_REF]. Warehouses may be roughly modeled as a queueing system in which stock-keeping units (SKU's) are customers that arrive at the receiving dock, where they join a queue usually serviced by forklifts for storage, until they are shipped out [START_REF] Bartholdi | Warehouse & Distribution Science[END_REF]. The primary concern of this paper is to establish a queueing system considering forklifts as servers and relate the same to an energy model with the inclusion of energy waste reduction controls during forklift idling, similar to idling of machines in a manufacturing unit [START_REF] Prabhu | Modeling Green Factory Physics -An Analytical Approach[END_REF].

A majority of existing literature is devoted to research in the modeling of energy aware manufacturing systems, useful in the shop floor. Reduction of wastage of energy by using heuristics for various dispatch rules has also been proposed [START_REF] Prabhu | Modeling Green Factory Physics -An Analytical Approach[END_REF]. In terms of warehousing, literature is focused on the development of energy efficient material handling applications by finding an optimized travel path sequence, considering the Traveling Salesman Problem (TSP) to address the problem of order picking [START_REF] Makris | Energy-saving methodology for material handling applications[END_REF]. Algorithms have been designed for effective performance end-of-aisle order picking systems, and travel times have been analyzed considering queueing models for item location in warehouses [START_REF] Chew | Travel time analysis for general item location assignment in a rectangular warehouse[END_REF]. Queueing models for centralized inventory information in warehouses and warehouses with autonomous vehicles have been developed to evaluate congestion effects in storage and retrieval transactions [START_REF] Roy | Queuing Model for Unit-Load Warehouse Systems using Autonomous Vehicles[END_REF], and there has been recent research dedicated to the development of queueing models for warehouse AS/RS [START_REF] Hur | A performance estimation model for AS/RS by M/G/1 queuing system[END_REF], [START_REF] Lee | Performance analysis for automated storage and retrieval systems[END_REF]. There is a requirement to develop computationally intuitive energyaware warehousing models as it relates to queueing theory, which effectively captures the consumption of energy on a large scale, to facilitate more productive insights during warehouse planning and design stages.

Warehouse Layout

The design of work-in-process warehouse layouts are heavily influenced by response times of the material handling system. [START_REF] Pandit | Response Time Considerations for Optimal Warehouse Layout Design[END_REF] propose a warehouse layout for a multi-vehicle handling system. The warehouse is considered to be rectangular in shape to facilitate ease of storage of rectangular units in the form of pallets, stored within the warehouse on racks [START_REF] Berry | Elements of Warehouse Layout[END_REF]. The racks are arranged back-to-back in the form of blocks, and space between blocks form aisle, creating a guide-way network which expedites forklift movement and reduces congestion. The layout suggested is a probable aisle arrangement in large warehouses, in which the rack accessibility of forklifts is maximized. Fig. 1 details the warehouse layout considered for the purpose of this paper. The warehouse operates under the following assumptions: storage and retrieval requests arrive at a door whenever a truck moves into the loading/unloading dock. Immediate fulfillment of the request is done if a forklift is free. If the order is waiting on a forklift, it enters itself into a queue which empties on a first-come, first-serve (FCFS) basis rather than a priority based emptying of the queue, for ease of calculation. The system is modeled as M/M/c queue, in which c indicates total number of forklifts operated in the warehouse.

Warehousing Energy Control Model

The recently proposed machine-level EC1 energy control policy is considered to be the basis for the model proposed in this paper. The main objective of the policy is the reduction of energy consumption during machine idling, thus reducing wasted energy. The policy states that this can be achieved if the machine is switched to a lower power consumption state if its idle time exceeds a minimum threshold value τ [START_REF] Prabhu | Simulation Modelling of Energy Dynamics in Discrete Manufacturing Systems[END_REF], [START_REF] Prabhu | Modeling Green Factory Physics -An Analytical Approach[END_REF]. Determining an optimal value of τ could involve a trade-off between energy savings and loss of production [START_REF] Prabhu | Simulation Modelling of Energy Dynamics in Discrete Manufacturing Systems[END_REF]. The concept of utilization of a minimum threshold value of time to conserve energy has been explored in terms of manufacturing systems where production schedules are fixed at least τ ahead of time [START_REF] Prabhu | Modeling Green Factory Physics -An Analytical Approach[END_REF]. The understanding of interplay between energy control (EC) policies and production control (PC) policies as it relates to key performance indicators (KPIs) becomes necessary. It is safe to assume that the same model can be applied to a distribution center as well, seeing as the loading or unloading of trucks in loading docks to pick material for packing and shipping or to put-away material in storage respectively takes place on a fixed schedule, with planned shipments [START_REF] Frazelle | Supply Chain Strategy[END_REF].

In the context of this paper, we consider τ to represent the average idle time threshold of the forklift system in the warehouse. The EC1 energy control policy serves to link warehousing decisions which would influence KPIs of the system with EC and PC policies. Major warehousing decisions include configuration issues viz., how material flow should be organized, design of the order picking and shipping process, sizing of the warehouse, allocation of storage capacity, determining lighting and electricity requirements, batch sizing and scheduling, and level of automation required [START_REF] Rouwenhorst | Warehouse design and control: Framework and literature review[END_REF]. The KPIs of interest include energy consumption per forklift, total system energy consumption over the long term, cycle time for each forklift, throughput of the facility, and utilization and availability of the forklift system.

Multi-Server Queueing Model with Energy Control

A key characteristic of discrete manufacturing is that the energy consumed by a machine tool while it is idling or busy is quite similar [START_REF] Makris | Energy-saving methodology for material handling applications[END_REF]. For any system, energy can be expressed as the product of power and time. Power consumed by any machine in the system, in this case c forklifts, changes according to the state of the system. For practical purposes, systems tend to consist of two states on average: busy and idle. We define an intermediate third state for the system which occurs when forklifts perform non-value added activities. We consider this to be the apparent idling condition of the forklift system. Average power values for the 3 system states are defined as W 0 for idle condition, W 1 for apparent idling and W p for busy state. The idle time of the system is composed of time taken by the forklifts to perform non-value added activities (considered as apparent idle time of the system) and time in which the forklift is idle (real idle time of the system). An average idle time threshold τ is specified for the system. We define power consumption during real idle time (W 0 ) to be zero, i.e., the forklift is switched off when not performing any activity. Probabilistic models will be constructed in this section to find out the state of the system at any given time, enabling us to estimate consumption of energy in the system. Consider a multi-server machine system. Orders arrive at the loading dock at a rate λ according to a Poisson process with exponential inter-arrival times and get serviced at a rate μ according to an exponential distribution, with c forklifts being the servers. The queue is processed on a FCFS basis, with jobs departing the system after they have been processed by any of the servers. This is the M/M/c model under consideration.

For M/M/1 queue, utilization of the machine is given by the following relation [START_REF] Hopp | Factory Physics[END_REF]:

⁄ ⁄ ( )
For the M/M/c queue, average utilization of the system is expressed as:

⁄ ( )
For stability and to provide bounds to the system, the utilization value should be lower than 1. Utilization parameter ρ represents the average fraction of time during which each of the c servers is occupied with a task. The fraction of time that the system will be idle is expressed as [START_REF] Askin | Modeling and Analysis of Manufacturing Systems[END_REF]:

[ ( ) ( ) ∑ ( ) ] ( ) { [ ( ) ( ) ∑ ( ) ] ( ) } ( )
where ∫ ( ) = 1 -F(τ) for any distribution.

When the utilization of the forklift is high, W 1  0 and the only term that will affect the equation is W p ρ. Similarly under low utilization condition, only the W 1 term would affect the equation. It is to be noted that when utilization is high, i.e., W p  1, the EC1 energy control policy will not influence energy consumption. For a 100% utilization of the system, i.e., when all forklifts are in operation,

( )

Depending upon the type of warehouse system considered, certain constraints are introduced to the equation which is then differentiated with respect to c to find the optimal value yielding minimum energy consumption. Constraints could be the maximum amount of throughput a warehouse is capable of handling, layout of warehouse, capacity, response time, storage density and inventory availability.

Experimentation and Results

The analysis of the real time operations of one of the DCs of a personal care manufacturer located in Brazil was done for experimentation purposes. The layout of the DC in consideration is similar to the one suggested in Fig. 1 of this paper. Analysis of arrival rates of products over a three-day period showed that the products arrived with a beta distribution on day 1 with shape parameters α = 1.87 and β = 2.05; a triangular distribution on day 2 with parameters a = 0.58, b = 4.9 and c = 0.62; a beta distribution on day 3 with shape parameters α = 2.17 and β = 2.26.

Assuming average forklift speed of travel is 5 mph for all operations, the worst case scenario assumes that on average, each forklift travels to all rack positions in the DC to drop off orders and picks up orders to drop them off at the loading dock every day. Since average demand per day is a constant number, it is safe to assume that service rates follow the same arrival distribution over the three days observed.

Consider that W 1 = W p , a fair assumption to make as forklifts would usually spend the same amount of power doing non-value added activities as they do for value-added work. By logic, average idle time threshold τ decreases with an increasing number of forklifts in the system. The number of forklifts in the system would be dependent on the size of the warehouse. Let us consider 5 forklifts for a smaller warehouse and 20 forklifts for a larger one. Consider that for 5 forklifts, τ value is 60 minutes, and for 20 forklifts, it is 10 minutes. Varying utilization, using Equation ( 9), surface plots of the daily energy values for the three distributions are illustrated in Fig. 2. For different utilization values, the energy consumption increases by an average of 278.8% as number of forklifts in the system increase from 5 to 20. Thus it can be seen that larger warehouses with n times the number of forklifts as smaller ones consume nearly n times more energy for any arrival distribution.

It is observed that the values obtained for the general distribution model do not vary significantly between distributions, because of the scaling down of the W 1 term due to minute values of idle probability of the system. This is because the chance of the entire forklift system being idle at the same time is almost zero. In such situations, only the W p term of the equation would affect the energy value. Energy varies proportionally with utilization for a constant c value and number of forklifts for a constant ρ. Fig. 3(a) details the variation of daily energy with respect to change in utilization with 5 forklifts in the system. Fig. 3(b) illustrates energy variation with respect to number of forklifts at 70% utilization. It is observed that variation of energy is almost linear in both cases.

Conclusions

The motivation for this work was the need for an energy-aware warehousing model which leveraged the principles proposed by the EC1 energy control policy to forklift queues in warehouses and DCs. The paper suggests a model for which the recently proposed M/M/1 manufacturing model with energy control is the basis, and extends it to an M/M/c queue in a warehouse. Experimentation on the model yielded results which are largely dependent on the number of servers considered in the system. For a larger number of servers, the apparent idling energy of the system would tend to be on the lower side. The analytical model developed can be used to determine availability of forklifts to accomplish tasks and assignment of forklifts for the same, determination of energy spent by forklifts, and calculation of optimal number of forklifts to reduce energy in the system based on certain constraints. Future work can be extended to the determination of the energy consumption in other avenues in warehouses, especially newer ones with higher capital investment where automation will come into play, and implementation of other energy control policies for the same. Validation of the suggested model can be carried out by simulating the queueing of forklifts in Arena and employing the suggested equation to calculate energy.

Fig. 1 .

 1 Fig. 1. Layout details (Source: Pandit & Palekar, 1993)

Fig. 2 .Fig. 3 .

 23 Fig. 2. Energy surface plot of different distributions

 [START_REF] Prabhu | Modeling Green Factory Physics -An Analytical Approach[END_REF]calculated the probability that inter-arrival time is more than the average threshold idle time τ by the following equation:

where ( )for an exponential distribution = λe -λx . By logic, (1 -e -λτ ) will be the probability of forklifts doing non-value added activities. Consequently, the probability that the system is in the idle state and time between arrivals is greater than τ is calculated as:

Under steady state condition, arrivals are independent of the state of the system. Thus the long term energy consumption equation for the system over time T is:

Substituting the value of W 0 as 0 in Equation ( 6),

The ratio of energy wasted during idle time (E w ) to the energy that is actively used when the forklifts are accomplishing value-added activities (E p ) is given by the following relationship:

The model presented has assumed a Poisson input process. However, this assumption would be violated if arrivals definitely do not occur randomly for the warehousing system. This necessitates the use of an arbitrary distribution queueing model [START_REF] Hillier | Operations Research[END_REF]. The queue is classified as a G/G/c model, where arrival and service distributions usually follow different processes but can be the same as well. Using Equation (7) as a basis, different energy relations based on various inter-arrival distributions can be calculated using the following general equation: