Georg Heinecke
email: georghe@ethz.ch

Jonathan Köber
email: jonathan.koeber@claas.com

Raffaello Lepratti
email: raffaello.lepratti@siemens.com

Steffen Lamparter
email: steffen.lamparter@siemens.com

Andreas Kunz
email: kunz@ethz.ch

Event-Driven Order Rescheduling Model for Just-In-Sequence Deliveries to a Mixed-Model Assembly Line

Keywords: Just-In-Sequence, Mixed-Model Assembly Line, Rescheduling

Today's buyer markets and lean supply chains require build-to-order assembly systems with just-in-sequence (JIS) deliveries. Simultaneously production systems have become prone to supply disturbances (i.e. events) that endanger the synchronized delivery of all JIS components to the assembly line. To uphold production sequence stability, rescheduling is frequently required. Current methods, however, make assumptions that are often insufficiently aligned with real-world problems and focus on production issues while neglecting the implications of today's tight integration of supply chain with production processes. To this end, this contribution derives a general model of a mixed model assembly line. It then proposes and evaluates an event-driven rescheduling model for JIS deliveries. The results indicate that rework due to missing JIS components can be avoided without compromising performance.

Introduction

Today's buyer markets have forced production systems to shift towards mass customization, where a large product portfolio and many customization options are key success factors for winning customer orders and improving competitiveness [START_REF] Kotha | Mass Customization: Implementing the Emerging Paradigm for Competitive Advantage[END_REF]. Thus, customer-specific products replaced standardized ones, in the course of which forecasting-based build-to-stock production strategies were abandoned in favour of buildto-order (BTO). It is a demand-driven production approach where a product is scheduled and built in response to a confirmed customer order [START_REF] Holweg | The Second Century: Reconnecting Customer and Value Chain through Build-to-Order Moving Beyond Mass and Lean in the Auto Industry[END_REF]. Since neither demand volume nor product configurations can be anticipated, however, inventories for highly individualized, costly components were replaced with just-in-sequence (JIS) deliveries. Thus, today a customer order triggers the entire supply chain (SC) where several supply sequences of customer-specific components merge into one 'pearl chain' on an OEM's assembly line, which has two drawbacks. First, the production volume of the SC has to align itself with volatile customer demand, requiring flexible and responsive systems [START_REF] Gunasekaran | Build-to-Order Supply Chain Management: A Literature Review and Framework for Development[END_REF][START_REF] Holweg | Towards Responsive Vehicle Supply: A Simulation-based Investigation into Automotive Scheduling Systems[END_REF]. Second, disturbances in JIS processes endanger the synchronized merging of all supply sequences at the OEM and the assembly of a customer-specific product from the respective components [START_REF] Christopher | Supply Chain 2.0": Managing Supply Chains in the Era of Turbulence[END_REF][START_REF] Thun | An Empirical Analysis of Supply Chain Risk Management in the German Automotive Industry[END_REF]. This paper investigates the latter issue because today's global supply networks are prone to disturbances that range from deviations (e.g. transport delays) to disasters (e.g. floods) [START_REF] Gaonkar | Analytical Framework for the Management of Risk in Supply Chains[END_REF]. Through the seamless connection of lean processes with neither stock nor time buffers, manufacturers are unprotected against disturbances that destabilize the SC system [START_REF] Radjou | Adapting to Supply Network Change[END_REF]. Risk management can reduce the potential for disasters but it can neither prevent nor efficiently address smaller disruptions and deviations. In these cases companies need a supply chain event management (SCEM) system that identifies disturbances (i.e. events) early through a comprehensive real-time monitoring system (e.g. RFID-based) and suggests counter-measures. These reactions have an operational focus and try to uphold an efficient production despite impending knockon effects of events. One large group of measures, besides e.g. the reintroduction of time and stock buffers, are adaptions of the production schedule and thus, the pearl chain sequence. However, rescheduling methods make assumptions that are often insufficiently aligned with real-world problems [START_REF] Vieira | Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods[END_REF][START_REF] Portougal | Production Scheduling Theory: Just Where is it Applicable? Interfaces[END_REF][START_REF] Pinedo | Scheduling: Theory, Algorithms, and Systems[END_REF] and focus solely on production issues while neglecting the implications of today's tight integration of supply chain with production processes. Despite ample methods [START_REF] Raheja | Reactive Recovery of Job Shop Schedules -A Review[END_REF][START_REF] Vieira | Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods[END_REF][START_REF] Ruiz | A Comprehensive Review and Evaluation of Permutation Flowshop Heuristics[END_REF], planers still find it difficult to react to events and to hold the supply chain stable. Hence, this paper proposes and evaluates a rescheduling approach for a pearl chain sequence of a BTO assembly line that includes implications of event-prone JIS delivery processes.

2

Literature review

The scheduling environment is divided into a static and dynamic environment [START_REF] Vieira | Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods[END_REF][START_REF] Raheja | Reactive Recovery of Job Shop Schedules -A Review[END_REF]. In the former, a finite set of orders has to be scheduled without the presence of uncertainty. This paper assumes a dynamic and stochastic environment in which an infinite set of orders is subject to uncertainty of some parameters. A scheduling problem of a manufacturer is specified further with the α | β | γ notation [START_REF] Pinedo | Scheduling: Theory, Algorithms, and Systems[END_REF] that refer to the machine environment, processing characteristics and the scheduling objective. Two of the more complex machine environments are job and flow shops that are characterized by the existence or lack of process flow variability, which is due to a multiple stage production process [START_REF] Pinedo | Scheduling: Theory, Algorithms, and Systems[END_REF]. We model a MMAL and consequently a flow shop where every order has to be processed on the same sequential stages. Assumptions for processing characteristics of flow shops differ considerably from job shops and often exclude preemption (i.e. interruption of processing), recirculation, setup times, and machine breakdowns [START_REF] Ruiz | A Comprehensive Review and Evaluation of Permutation Flowshop Heuristics[END_REF]. One important characteristic of flow shops is the availability and size of buffers (i.e. in-process inventories) between stations. A limited buffer implies blocking while a no-wait system like the tact-driven, constantly moving MMAL considered in this paper demands that orders cannot wait between stations and thus production start is delayed until processing is ensured [START_REF] Pinedo | Scheduling: Theory, Algorithms, and Systems[END_REF]. The problem can be framed as a proportionate flow shop problem with equal processing times at each station and without intermittent buffers (MMAL does not halt). The (re)-scheduling objective aims either at operational (e.g. asset utilization) or market targets (on-time deliveries) [START_REF] Pinedo | Scheduling: Theory, Algorithms, and Systems[END_REF]. For instance, makespan is the completion time of the last scheduled order. Its minimization implies a good utilization [START_REF] Pinedo | Scheduling: Theory, Algorithms, and Systems[END_REF]. Other measures are often a variation of the earliness/tardiness criterion that are sometimes combined with the nervousness criterion to balance permutations [START_REF] Lamparter | Event-based Reactive Production Order Scheduling for Manufacturing Execution Systems[END_REF].

In practice, scheduling is driven by uncertainty [START_REF] Mckay | Unifying the Theory and Practice of Production Scheduling[END_REF] while rescheduling is driven by the occurrence of a disturbance. Both concepts can be applied to different degrees in a dynamic and stochastic environment. Approaches with a focus that is entirely offline (i.e. robust scheduling) devise an initial schedule that is not updated while online approaches (i.e. totally reactive scheduling) make all processing decision locally in real-time. A representative for the latter is the choice of the most appropriate dispatching rule [START_REF] Jeong | A Real-Time Scheduling Mechanism for a Flexible Manufacturing System: Using Simulation and Dispatching Rules[END_REF]. In a dynamic and stochastic environment, predictive-reactive scheduling is the only approach that combines scheduling and rescheduling [START_REF] Vieira | Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods[END_REF][START_REF] Raheja | Reactive Recovery of Job Shop Schedules -A Review[END_REF]. It follows a two-step approach where an initial schedule is devised (generation step) and then updated (control step). The interval of an update step is defined as periodic, event-driven, or a combination of both. Since the JIS deliveries to the assembly system are disrupted, a predictive-reactive (re)-scheduling with an event-driven policy is employed. A final aspect is the applied repair strategy that takes effect when a policy triggers a rescheduling. It is characterized by the degree to which it overhauls the initial schedule (partial or complete). This paper presents a partial repair strategy for event-prone JIS deliveries to BTO production systems.

Problem formulation

Fig. 1 illustrates a tractor assembly line. From the virtual order bank (VOB), customer orders are sequenced on a weekly basis through a scheduling based on order priority.

The scheduled sequence is one week long and fixes the production programme for week 5 -i.e. specific assembly times and the according delivery dates for JIS components. The preceding 4 weeks were scheduled earlier and constitute the frozen zone. It is a pearl chain of over 1200 customer-specific orders that will be assembled over 4 weeks. The length of the frozen zone is determined by the JIS supplier with the longest order to delivery (OTD) time. Around 10 components of the tractor (e.g. drive) are delivered JIS. For each component, a customer can choose between several versions. Thus, each JIS supplier has his own customer-specific sequence that runs in parallel to the order sequence of the manufacturer. They all converge on the assembly line. From this use case, a model is developed that is generalizable for most BTO assembly systems with JIS deliveries (e.g. automotive industry). Customer orders for a single product arrive randomly in the VOB (see Fig. 1 and Fig. 2), modelled through the Poisson process with exponentially distributed arrival intervals λ. Customer orders are associated with an order time o j and a random committed due date d j that is bound on the lower end by the minimum OTD time of the supply network and its double on the upper end. These limits are largely in accordance with an empirical investigation at the case partner, where they were found to be 4 and 12 weeks respectively [START_REF] Köber | Hybrid Production Strategy between Make-to-Order and Make-to-Stock -A Case Study at a Manufacturer of Agricultural Machinery with Volatile and Seasonal Demand[END_REF].

Fig. 1. Industrial case study

The SC consists of M suppliers (1 ≤ m ≤ M) that each supplies one station at the MMAL with a component family that comprises N products (1 ≤ n ≤ N) (Fig. 2). When ordering, a customer can select a specific component n of each family to customize his product. The sum of components ordered by a customer constitutes his individual product configuration that is sourced JIS. Each supplier m has a specific OTD time ζ m that is subject to random variation due to disturbances. New orders that are not yet sequenced are part of the VOB (see Fig. 2) where orders are sorted by due date d j from latest to earliest. A one week production schedule is devised weekly with component orders being sent to the suppliers. Within the scheduled sequence of J customer orders (1 ≤ j ≤ J), each order j is associated with a fixed assembly sequence position s j and thus, a release date r j when final assembly is scheduled to start.

Fig. 2. MMAL model with JIS component deliveries

The newly scheduled component orders then successively enter the frozen zones of the M suppliers, depending on r j and the supplier-specific lead time ζ m (see Fig. 2). Once an order enters the frozen zone of supplier m, the production of component n mj has begun. Due to this supply chain setup, the mixed model assembly line (MMAL) assembles a total of n m product variants. Using the α | β | γ notation a Fm | p mj =p j | ∑T j / J production system is modelled. It is a proportionate flow shop problem (PFSP) with m work stations in series; the processing times p mj of order j on station m are identical and equal to p j ; the objective function is to minimize the average order-related delivery delay. We assume the MMAL to be a black box where processing times are deterministic and known. A resequencing within the MMAL is excluded (i.e. no mixed-bank buffers [START_REF] Meissner | Controlling Just-in-Sequence Flow-Production[END_REF]). Orders that cannot be assembled due to missing components are moved to the rework area. Completed orders are shipped to the customer and the order-specific tardiness is recorded (if completion time c j > d j ).

4

The multiple permutable subsequences concept

Although providing planning stability, a long frozen zone of e.g. 4 weeks leads to an increase of the probability that disturbances affect supplier sequences. The mismatch between real and planned state of ordered components results in delayed or failed deliveries [START_REF] Heinecke | Modeling the Basic Cause-Effect Relationship between Supply Chain Events and Performance[END_REF]. Because components are customer-specific, in either case the current sequence position s j of an order j cannot be hold. Fig. 3 illustrates removal strategies.

Fig. 3. Removal strategies for affected customer orders

The case partner currently moves affected orders to the end of the sequence and advances all others (third strategy in Fig. 3), which has several implications. First, schedule changes have to be communicated to all JIS suppliers, who in turn have to provide the components of advanced orders faster than their frozen zone, which is not always possible and compromises product quality (rush jobs). In urgent cases, emergency transports are devised. Second, the rescheduling policy avoids an empty tact but decreases pearl chain stability since the removal of one order alters the position of the others. This leads to suboptimal due date adherence and confusion on the shop floor. The latter refers to difficulties in aligning the material flow of the right components with the correct machines. Third, high instability with missing or wrong material coupled with a continuously moving assembly line results in rework. While assembly takes a few days, rework can take weeks, further jeopardizing due dates.

To avoid these drawbacks, it is assumed that customer orders are only moved back in the sequence. Thus, strategies two and three in Fig. 3 are invalidated and only 'rework' and 'empty tact' remain. The rework strategy reflects today's supply networks that lack real-time monitoring systems. Components that are delayed or failed are only noticed at the OEM when assembly and component sequences cannot be aligned. The unfinished product then moves to the rework area after assembly. Through a continuously monitored supply chain costly rework is avoided because orders can be removed from the sequence when components are affected by an event. This approach, however, poses the question where the removed order is reinserted into the sequence. To this end, we propose the multiple permutable subsequences approach that is based on the insight that the frozen zone is divided into several component sequences that differ in length due to individual OTD times of JIS suppliers (Fig. 4).

Fig. 4. Trade-off between schedule optimality and nervousness during rescheduling

Fig. 2 and Fig. 4 (starting with s j = 13) show that within the totally permutable subsequence (TPS) π 0 orders are easily rescheduled because components are not in production. Hence, the TPS is reduced whenever an order enters the frozen zone of the supplier with the longest OTD, marking the beginning of the partially permutable subsequences (PPS) π m . Thus, while one component is affected by an event, other components that are already in production would still be delivered on time, which a rescheduling strategy needs to consider. Fig. 4 shows three strategies based on the individual order step size for reinserting an order when it is in the PPS: step, hop and leap. The step strategy moves every order, starting with the delayed order, one sequence position back, resulting in high nervousness but low cumulated delivery delay. In contrast, the hop strategy removes orders based on a criterion (e.g., due date) to balance indicators. Lastly, the leap strategy removes an order from its current position and moves it to the back of the affected component sequence (e.g., from s j = 8 to s j = 13 in Fig. 4). All order sequence positions behind the reinserted order are then increased by one. The resulting empty position (e.g., s j = 8 in Fig. 4) can be filled if another order further down in the sequence is delayed or failed.

Evaluation

The model with the rework and leap strategies was implemented into Plant Simulation from Siemens PLM Software. The MMAL is supplied by 3 JIS suppliers (drive, engine, and cabin) with a respective lead time of 18, 12, and 6 days and an on-time delivery reliability of 86, 91 and 97% respectively. Each supplier offers 4 different component versions that are randomly chosen by the customer, whose orders arrive according to the Poisson process in mean intervals of 30 minutes. The MMAL assembles 64 different variants and runs at a tact time of 30 minutes. If during assembly customer-specific components are missing, the order is moved to the rework area. The simulation ran for 100 days, including a calibration phase of around 30 days. Fig. 5 compares the performance of the rework and leap strategies. As outlined earlier, the foremost objective is to have products that do not require rework after assembly. The leap strategy fulfils this requirement by increasing the assembly volume by 23.6% (i.e. products that do not need rework) while virtually eliminating rework that is due to missing components. Through the delay of the product assembly for orders where components are missing, however, the overall production volume is decreased by 3.2% over the simulation time. Furthermore, a total of 765 sequence positions remained empty due to the order removal, which reduces the utilization of the assembly line. The comparison between rework and leap for this indicator is misleading, however, because work stations that lack the correct component are also idle when the rework strategy is applied. Thus, the difference in utilization between the two strategies is less than Fig. 5 suggests. The final indicator is the number of sequence positions that were filled through the application of the leap strategy. These 270 positions became empty through a problem in the supply chain for an order but were filled at a later point in time through the inserting of another order further downstream. As predicted in Fig. 4 and shown in Fig. 5, the leap strategy is associated with a poor performance in regard to the average product delivery delay. It rises from about 4 hours for the rework strategy to more than 20 hours when the leap strategy is applied. Modern BTO production systems are characterized by low inventories for main components that are sourced JIS. The respective component sequences merge seamlessly on the assembly line into a customer-specific product. Trends like global sourcing and lean management, however, have virtually eliminated the scope for variation in the supply processes. Thus, small and large events alike cripple the synchronization of the individual component sequences. A SCEM system identifies these disturbances early through a comprehensive real-time monitoring system and suggests counter-measures.

To repair the affected production sequence, this paper presents the multiple permutable subsequences concept that divides the frozen zone into several subsequences of diminishing rescheduling flexibility. The evaluation for one strategy ('leap') showed that through the early removal of affected customer orders from the sequence costly rework due to missing JIS components is avoided. Since the strategy moves an order to the end of the partially permutable subsequence, however, the average product delivery delay is worse when compared to the status quo. Thus, future work focuses on the implementation and evaluation of other strategies that increase performance.

Fig. 5 .

 5 Fig. 5. Comparison of the rework and leap strategies

Acknowledgements. Part of this work was funded by the Federal Ministry for Economics and Technology (BMWi) under the reference number 01MA10014 'RAN -RFID-based Automotive Network'. Refer to www.autoran.de/en for further details.


Eva Coscia
email: eva.coscia@txtgroup.com

Michele Sesana
email: michele.sesana@txtgroup.com

Rosanna Fornasiero
email: rosanna.fornasiero@itia.cnr.it

Support to order management and collaborative production of customised garments for specific target groups

Keywords: supply chain management; business document exchange, customized order, partner search

This paper describes a solution for the creation and management of orders and other business documents, necessary for the production and delivery of customised garments. These documents are managed along a supply network that can be quickly set up with the support of a tool for the identification of best partners for the production of the ordered items. The management of the documents leverages on three elements: the adoption of a standard for the interoperability of the business information among different services used in the network; the usage of a tool for quickly and automatically check the documents' contents applying business rules on top of them; a middleware layer that provides a fast way of exchanging the checked documents. The set-up of the supply chain is supported by the Partner Search module which is in charge of managing and improving the manufacturer's knowledge about its potential partners. The solution is validated within an industrial pilot focused on the production of customised knitted items for obese people.

Introduction

This paper presents a new approach, developed in the context of the CoReNet1 project, for the creation of orders and for the production of customized items for specific groups of consumers that have functional and healthy requirements, such as, for example, obese people. The production of a single lot or of small series in a short time and with acceptable prices is a complex task for fashion companies (especially SMEs) that require a support during all the phases of this process, from market analysis and collection design, to item production, delivery and validation by the final consumer.

The solution presented in this paper supports companies in three business processes: product configuration during the sales process, management of customized orders and other business documents exchanged along the supply chain during production process and the quick set up a of the supply chain for the order fulfilment. These aspects emerged as important steps especially for companies willing to change from standard orders to customized orders where many different parameters have to be verified before production starts.

The usage of standards ensures interoperability of the business documents exchanged between the tools used in the various phases and allows applying checking rules on the information shared by the involved actors along the supply chain. Furthermore, the research of the best partners supporting the manufacturer in the production of the customised items is empowered by a tool for the creation and maintenance of profiles of potential partners providing the partners themselves with the possibility to update their profile with information on their production availability.

The general Reference Model and a specific instantiation: the knitting production business case

Companies in the Textile, Clothing and Footwear Industry (TCFI), facing the challenge of producing single items or small series of customized items, need support in order to re-design their processes and take advantage of implementing new ICT tools to support them. The CoReNet Reference Model [START_REF] Fornasiero | A Reference Model for Customer-Oriented and Eco-Friendly Networks for Healthy Fashionable Goods[END_REF] is based on the formalization of the most important business processes requiring collaboration amongst different partners (from product conception and design, to production and delivery), and provides also guidelines along three important dimensions namely organizational changes, ICT and implementation of innovative tools supporting, and knowledge management. Within this work, the new approach to network management has been validated within an industrial Pilot, called the Knitted Garments Pilot (KGP), where an important fashion company plays the role of the manufacturer. This company, specialized in the production of high-quality fashionable garments, is interested in extending its business to the design and production of garments for obese people, allowing them to order personalized (in sizes and in aesthetical aspects) items. According to the CoRM formalization and the business requirements collected with the company, the new approach proposed for the KGP aims to manage the networks both upstream towards the suppliers and downstream towards the customers and it is based on:

• Organizational revision of the business processes: the sales process is reengineered to support a new relationship with the customer to ease the formalization of her/his specific needs and to manage the order collection and the product delivery. The relationship with the third parties involved in different working phases need to be based on a more collaborative approach where the third parties can share in a formal way their availability to produce.

• Definition of new ICT tools to support the different phases of sales and order management processes: innovative tools have to be based on integrated provision of advanced services and interoperability with the existing legacies also through the adoption of existing standards for the documents exchanged along the network.

• Change in the knowledge management: the company needs to enlarge the knowledge base on customer requirements and formalize all the different features of the order with specific and dedicated services. This new knowledge base needs to be used during the design phase, in the shop and during production by different actors.

In the specific case of obese people as well as in other target groups like diabetics, elderly etc they have specific requirements for shopping clothes, which are related not only to their sizes, but which require taking a number of measures with the support of dedicated shop assistants or tailors and the production of specific items as well as the need of having fabrics, styles and patterns specific for them. These models and patterns are not always fully available in the shops, therefore the support of an on-line gallery that allows browsing on a number of dedicated items with 3D rendering and possibility of selecting variants would be very useful.

Background

In literature, there are many different approaches for the order management as well as for the partner search process. These approaches need to be adapated to the needs of customized products. For what concerns order management, while in [START_REF] Wiendahl | Fast and Reliable Order Management Design Using a Qualitative Approach IFIP International Federation for Information Processing[END_REF] it is proposed an approach for the qualitative evaluation of the turbulence of the market in [START_REF] Park | A Framework for Enhancing Responsiveness in Sales Order Processing System Using Web Services and Ubiquitous Computing Technologies[END_REF] it is explained how to increase degree of responsiveness with a new architecture based on Web Services and Ubiquitous Computing technologies for reliable real-time information. The TCFI sector, also analysed in [START_REF] Gessa | A bottom-up approach to build a B2B sectorial standard: the case of Moda-ML/TexSpin[END_REF][START_REF] Sabbata | Standard creation and adoption for SME networks[END_REF], is populated by a number of small or micro enterprises, with limited resources for the adoption of innovative IT solutions, and requires the adoption of order management solutions based on exchange of documents based on SMTP or HTTP rather than on a fully SOA architecture for a step-by-step approach to integration of different modules.

The exchange of business documents along the supply chain for the production of textile and garments need to be formalized in the ModaML/TexSpin standard [5, [START_REF] Gessa | A bottom-up approach to build a B2B sectorial standard: the case of Moda-ML/TexSpin[END_REF][START_REF] Sabbata | Standard creation and adoption for SME networks[END_REF] that has been included in the results of the eBIZ process [4]; till now the ordering, production and delivery of customized garments, however, is not fully covered by these standards and this work aims to contribute to improve them.

For what concerns the use of different criteria to select partners for supply networks they have been characterized in works developed by [START_REF] Grudzewski | Virtual Scorecard as a Decision-making Tool in Creating Virtual Organisation[END_REF] and [START_REF] Seifert | Predictive Performance Measurement in Virtual Organization, Emerging Solutions for Future Manufacturing Systems[END_REF] who introduced the utilization of performance measures, usually known as KPIs, to better compare and select the suitable partners for every specific VO (that can be considered a particular type of supply network). These approaches emphasize the importance of selecting partners using a well-defined set of criteria, based on common attributes of each company. Taking into account the improvement of organizations that apply methodologies to measure their performance [START_REF] Lambert | Supply Chain Metrics[END_REF] as well as the improvement of collaborative networks that are establishing inter-organizational Performance Measurement Systems [START_REF] Gunasekaran | Performance measurement and costing system in new enterprise[END_REF], the KPIs can be used as criteria to suggest supply network partners. This strategy has been used also by [START_REF] Jarimo | Partner Selection with Network Interdependencies: An Application[END_REF] and [START_REF] Crispim | Multiple Criteria Partner Selection in Virtual Enterprises[END_REF] who introduced other criteria besides KPIs, like trustworthiness and risk values.

[18] also proposed the utilization of KPIs related to collaboration and commitment which are also revised and used in this paper. In literature there are many different KPIs to be used for different scope and applying different perspective to the partners evaluation according to the scope of the network to be created. No shared ontology for modeling KPIs, nor taxonomy of supplies and supply chain roles are available if not restricted to specific sector/application requirements. For the scope of this work, some important KPIs have been selected from literature and from real case studies and have been formalized in the Partner Profile.

From customized order creation to collaborative production

In Figure 1, the processes from the order generation to the delivery of the customised items are depicted and the tools supporting the flow are highlighted with a light dotted grey circle. In particular the following processes are taken into consideration:

• sales process with product configuration

• network configuration process based on partner search

• order management based on standardized document format. The sales process: from product configuration to order dispatching

The sales process is mainly supported by the introduction of an innovative Configuration user interface to be used by the shop assistant and consists of 3 main parts:

• A configurator used by the company to prepare an on-line collection and to define the customisable parameters made available to the target group of consumers • A service to quickly get measures of obese people, which allows calculating a set of 12 body measures starting from 3 simple measures. • An online order preparation and submission tool.

Using a rich graphical user interface, a consumer with the support of the shop assistant can browse a catalogue of items, select and configure them both aesthetically (for example by changing colour, accessories like buttons etc…) and functionally by entering his own measures and specific needs (like larger sizes, functional fabrics and adapted fashion options).

The order is created by a consumer with the support of a shop assistant using the online configurator and is complete of sensible data (related to the consumer and to the prepayment amount); then the order is automatically checked by the system (to avoid errors or incompleteness) and then dispatched to the producer that, with the support of other tools of the pilot, can identify the best suppliers.

4.2

The network configuration process: The Partner Search tool

The network configuration process runs when new 3 rd parties are needed for knitting or sewing the product requested by the customized order. The process is supported by a Partner Search tool which is based on available capability of each registered potential partner and on KPIs based on their past performance in case they have already collaborate [START_REF] Rotondi | Semantics and KPIs to improve partners selection for small series production[END_REF]. The selection of partners is based on criteria related to the product features as from the received order but also on criteria to measure the performance of the partner in terms of quality, time, and price. With the Partner Search tool, it is possible to execute multi dimensional searches in order to retrieve partners that provide the best matches towards a set of identified KPIs. These KPIs can be chosen time by time according to the needs of the specific customized order to be produced giving different importance to capabilities, time, cost and quality.

In the specific case of the KGP one of the key criteria for the quick and successful selection of a supplier is the availability of (pre-configured) knitting machines. Therefore the tool can be used at operational level as a collaborative tool since it allows the suppliers to update their profiles with information on availability (in a given time frame) of their machines for the production of specific items. If necessary, the producer exchanges other information with the selected suppliers to plan and execute the production, until the item is physically delivered to consumer at the shop.

4.3

The order management process and use of data exchange standards

The Workflow Manager (WfM) tool seamlessly supports the correct flow of information along a designed process that involves different actors. It applies rules checking the format and the content of the Business Documents and dispatches them to the correct receivers, also providing feedback on errors and anomalies, when necessary. This allows companies to save time when receiving many different orders for customised items, supporting them in dispatching the correct pieces of information to the correct partners. The WfM architecture is actually based on an Enterprise Service Bus (ESB) that takes care of: managing the exchange of information with external applications/services (on-line configurator, ERP etc.), decoupling the specific way the information is transferred from an external application/service to the ESB and vice versa.

The flow of information generated by the creation of a customized order is usually more complex than the one generated by a standard order, given the amount of data and details to be managed after order collection till the delivery of the item to the final customer. This flow of information needs to be conveyed to the proper actors and tools in a way that is perfectly understandable and manageable by all of them in the shortest possible time. In the case of the TCFI sector, the most natural choice was to evaluate the adoption of the interoperability architecture proposed by eBiz TCF 2 [4] project, that encompasses the Moda-ML3 standard [5] for Textile and Clothing business documents. The eBIZ processes and documents fitting KGP have been identified and analysed, realizing that eBIZ does not provide a full coverage of the process underlying the creation and management of orders for customized garments. Therefore a set of eBIZ/Moda-ML documents that support the exchange of information for the on-demand production of customized knitted items has been conceived and will end up with the release of a proposal for an extension of the standard, submitted to the ongoing eBIZ CEN workshop4 .

Conclusions

The KGP scenario presented in this paper describes innovative services to support SMEs in the application of advanced tools for enhanced sales process and collaborative production process. Beyond the benefits deriving by the adoption of a standard for the information exchange, it is important to remark the most innovative aspects emerged by the adoption of the proposed tools in the KGP scenario, are: • Management of a "customised order" which is presently not supported by the standards in the TCFI sector: This work is a contribution to define a standard format for this kind of order for the Clothing sector, which can be applied to the ordering of different garments, not only knitted items.

• Creation of the order based on collaboration of different actors: the part of the order for "customisation" definition (aesthetical preferences and measures) is done in the on-line configurator and then completed with sensible data in the shop by means of the ERP company system. The management of the order is fully supported by tools integrated by the WfM. • Possibility to model through business rules, defined and executed in the WfM properties on the structure and on the content of the documents that are not modelled by the standard.

• Definition and maintenance of information on possible production partners, not only derived from the administrative data but also from their past performances or provided directly, in a collaborative way, by the partners itself.

The integrated solutions that have been designed in order to optimize the automatic exchange of structured information, allow companies to reduces the overall time from the order generation to the production and delivery of a customized product as demonstrated in the KGP. The advantages of the proposed services are that each business partner involved in the process can express rules on the expected contents of the exchanged documents (i.e. presence of a pre-payment description in the order is necessary for its acceptance by the producer) and can specify actions to be triggered (i.e. the order is rejected and a rejection notification is sent to the shop). The advantages of the Partner Search tool are that each partner can collaboratively share its own available capability and the manufacturer can manage in a formal way all partner profiles according to reliable and updated performance measurement. This has demonstrated to increase quality of the partners selected both in terms of reliability and in terms of delivery time.

There are several potential extensions of the solution, among which to provide tool coverage also to support the manufacturer in the selection and publication of garments in the on-line configurator (for example, by providing a virtualisation of the items on obese morphotypes) and check applicability of the solutions to different kind of garments, in particular for those requiring more complex supply chains to be produced. Moreover the proposed solution has been conceived to be applicable to business environments beyond the ordering and production of knitted items and addressing different target groups (not only obese people). For example, the format of the standard documents is general enough to be applied to other kinds of clothes also requiring different sets of personalised measures and the partner profile can be applied as well to other kind of production.

Figure 1 :

 1 Figure 1: Tool coverage on the knitted garment Pilot

FP7 NMP

CoreNet (Customer-ORiented and Eco-friendly NETworks for healthy fashionable goods) project website: http://www.corenet-project.eu

eBiz TCF is a project launched in January 2008 by the European Commission to boost ebusiness processes in the Textile/Clothing and Footwear (TCF) Industries website: http://ebiz-tcf.eu/

[START_REF] Rotondi | Semantics and KPIs to improve partners selection for small series production[END_REF] Moda-ML is an initiative that aims to publicly offer tools and format specifications for the data exchange in networks of Textile/Clothing enterprises;website: http://www.moda-ml.org

/ 4 eBIZ workshop : http://www.cen.eu/cen/News/PressReleases/Pages/PR-2012-01-23_eBiz.aspx

Acknowledgement

This work has been partly funded by the European Commission through FP7 NMP CoreNet (Customer-ORiented and Eco-friendly NETworks for healthy fashionable goods) project (Grant Agreement No. 260169).


Paolo Pedrazzoli
email: paolo.pedrazzoli@supsi.ch

Marino Alge
email: marino.alge@supsi.ch

Andrea Bettoni
email: andrea.bettoni@supsi.ch

Luca Canetta
email: luca.canetta@supsi.ch

Modeling and Simulation Tool for Sustainable MC Supply Chain Design and Assessment

Keywords: supply chain, simulation tools, mass customization, sustainability

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

Introduction

Mass customization is nowadays an established paradigm that companies, belonging to many different sectors, apply in order to differentiate their proposal and to allow a better fitting of their products with customers' needs. However its implementation is not yet a simple and straightforward process as it deeply depends on the specific characteristics of the reality where it is put into practice. This asks for methodologies to be used as guidelines and specific tools that support the integrated productprocess-supply chain design brought forth by mass customization. Unfortunately such comprehensive approaches are, to these days, still lacking [START_REF] Castellano | Product-processes-supply chain structures alignment for mass customization scenarios[END_REF].

At the same time, the mere evaluation of economic performances is no more suitable in the current global economy. As the concept of sustainability is getting more and more consideration along with customers growing awareness about it, social and environmental issues related to the company business need to be taken into account towards greener and fairer products and supply chains. In the next future, such topics will increasingly set the governments' agenda and drive their decisionssome are already in place -in a shape that could considerably impact on the companies' busi-ness. However companies still struggle in finding adequate assessment frameworks for measuring sustainability performance of their supply chains [START_REF] Hassini | A literature review and a case study of sustainable supply chains with a focus on metrics[END_REF].

To this end, within the S-MC-S project, that is co-funded by the European Commission, a collaborative software environment, we will refer to as the S-MC-S System, has been created. This system enables a collaborative and holistic design of a mass customized stable solution space [START_REF] Piller | Mass Customization: Reflections on the State of the Concept[END_REF] providing a twofold benefit: on the one hand, it allows different design tools covering different compartments of the whole project to share data thus providing support in the handling of the huge and heterogeneous information related to a mass customized solution space; on the other hand, it allows the gathering and formalization of the data needed to carry out a sustainability assessment of the designed solution all along its lifecycle. This paper will present the conceived environment starting from an overview of the software architecture and Shared Data Model of the proposed system. In the second part, the development of a specific tool for the modeling and simulation of a supply chain for mass customized production will be described. Such tool is meant to overcome the weaknesses of the traditional approaches and to deliver a tool capable to cope with the complex logics of Lot Size One production and delivery driven by mass customization.

Shared Data Model

As previously said, the first step for laying the foundations of an integrated environment has been the definition of a Shared Data Model [START_REF] Piller | Mass Customization: Reflections on the State of the Concept[END_REF] where all the involved tools can store and retrieve the relevant information concerning the solution space. This description covers the three main aspects of a product in a lifecycle perspective. First of all the product nature, both in its hierarchical structure and its physical properties. Then the production process which encompasses the whole life of a product from its raw materials extraction down to its end of life treatment. Finally the supply chain, which describes all the involved actors and the carried out transportations of resources which have a role in the product life.

This collaborative description of the product is then used to perform a detailed assessment of its sustainability level. The assessment performed at the design stage of a product enables an iterative feedback between the design choices and the assessmentresulting KPIs based on a sustainable strategy of the company. Detection of problems and investigation of alternatives at this stage is crucial since late recognition and handling of these issues prevents from any effective countermeasures implementation.

In order to enable design of the solution space, a data model has been developed. This data model becomes the common language platform on which all data-providing agents exchange information with the underlining system, thus allowing interaction between them and ensuring consistency of correlated data. Inside the data model five macro areas can be identified as shown in the high-level class diagram of Fig. 1:  core data entities: contains the project root node and provides elementary building blocks like resources and facilities along with their possible specialized entities as well as descriptive bridges for the other areas;

 product area: models both the resources that compose the product physical structure and how customization choices change the physical structure of the product;  process area: models the operations which are connected by flows of materials as well as their input and transformed output;  supply chain area: models the facilities involved in the solution space and the transportations in between them;  assessment properties area: models assessment properties, representing the characteristics of the data entities used by the Assessment Engine for its calculations.

System Architecture

The second building block of the S-MC-S System is its conceived architecture that is meant to structure the interactions amongst the various design and assessment tools relying on the above described data model. This architecture is based on a clientserver model where the server, embodied by the Assessment Engine in Fig. 2, offers specific services and functionalities aimed to implement the full potential of the Shared Data Model platform and of a centralized assessment computation. The server, first of all, handles the persistency of all solution space data, lifting the burden of the developing effort of client specific persistency and providing a centralized repository of shared data on which clients can work collaboratively. The server also exposes locking mechanisms upon shared data to avoid data concurrent modification and to ensure the overall consistency. Moreover the server does not only provide services to handle shared data but it can be also triggered to perform the assessment computation based on the selected assessment model and to return the detailed assessment results back to the calling design tool.

By proposing this architecture the effort to develop a client able to take advantage of the S-MC-S services or even to adapt an existing design tool to it is minimized thus fostering the adoption of the S-MC-S System. This is the case of the presented supply chain design tool, whose detailed architecture is shown in Fig. 2, where the server part is just sketched for completeness.

Supply chain modeling tool

Within the context of sustainable production of mass customized goods, as it has been aforementioned, the need for better coordination of the actors involved in the supply chain is recognized as one of the key success factors towards the paradigm adoption. Selection of proper suppliers becomes essential in order to handle the required flexibility needed to sustain the several choices offered to the final customer in an order-driven production. At the same time the ability to predict the system capability to satisfy the incoming orders within the allowed lead time is mandatory if an efficient supply chain is to be set up.

However, the existing supply chain modeling tools don't cope with such a level of detail. Here the customer order is no more an abstract concept representing a product lot to be delivered in time, being aggregated with other customer orders and managed in an undifferentiated way. It has to be represented as a single entity requiring a specific customization of the offered good. To this purpose a tool meant to support modeling and simulation of a mass customization oriented supply chain is here presented.

The definition of the supply chain attains two different levels, logical and geographical supported by related editing panels shown in Fig. 3. The first one allows to model into a graph structure even complex supply chains by defining actors, the macro-processes they carry out and the input/output resources for each one. The second one allows to intuitively geo-position the facilities and to retrieve distances and routes between them for sustainability impact calculation.

Fig. 3. Screenshot of the supply chain modeling interface

Simulation of the Supply Chain behavior

The simulation feature of the supply chain tool acts as a decision support platform that enable, at low cost and robust simulation environment, analysis of what-if situations in order to compare alternative supply chains [START_REF] Chang | Supply Chain Modeling Using Simulation[END_REF]. Furthermore it allows to detect problematic situations like bottlenecks, starving conditions, bullwhip effects under various simulation parameterizations.

This feature exploits a Discrete Event Simulation engine that, upon description of the stochastically variable events occurring in the supply chain, computes its behavior. The tool is provided with an animation feature that allows to graphically verify that the modeled solution is properly working.

One innovative aspect of this supply chain simulation tool is the enhanced description of the product customization aspect. This means that the product samples generated as demand of the various simulated markets are indeed product instances configured from the solution space according to the user-defined product mix. These configurations vary the nature of the different macro-processes applied on each single configured product resulting in different input resources requirements and different output resources productions, as well as different capacity loads and consequent lead times.

For each simulated facility, different strategies and methods can be defined and resulting behaviors simulated. At factory level, production capabilities are triggered by events generated either following a pull or push strategy thus embodying plan of make-to-order, assembly-to-order or make-to-stock productions. At suppliers level, supply agreements which drive the supplier behavior can be postulated for each of the replenished items. Moreover, at markets level, orders generation is driven by its parameterized statistical distribution aimed at representing the expected product mix. The features described above allow to model and simulate typical mass-customization supply chains supporting definition of different decoupling points for the handled resources. This enables proper representation of the different strategies used in order to achieve mass customized goods where standard parts are usually provided make-tostock whereas highly customized components are produced or supplied only upon reception of customers orders. 4, the simulation results are summarized into an overall supply chain score though they can also be browsed for each facility and for each stock and process linked to it. In this way, it is easier to identify critical processes or facilities and study them deeper into details. Information like order lead time, stock level and stock related costs are available for each resource and the capacity utilization of each production line can also be analyzed.

Sustainability Assessment of the Supply Chain

Another evaluation layer proposed as support for suppliers selection is the sustainability one. Being connected to the multi-tool S-MC-S System environment, the presented supply chain design tool can exploit the holistic definition of the solution space held in the Shared Data Model in order to calculate its sustainability performances.

Indeed the tool itself completes the information necessary for carrying out the assessment that lie at supply chain level as, for instance, the distances of the covered transportations and the used means or the energy mix used by the facilities based on their geo-position. On the other hand, reuse of detailed definition of the product and production process, defined in other tools, allows to rapidly perform a reliable and complete assessment of the lifecycle sustainability impact of the product solution space.

The outcome [START_REF] Canetta | Customization and Manufacturing Sustainability: General considerations and footwear investigation[END_REF] consists in the estimation of a total of 40 sustainability indicators (17 environmental, 10 economic and 13 social).

Conclusions

European Industry is developing methods and enabling technologies towards a customer-oriented and sustainable manufacturing as a key answer to global competitive pressure. This trend has been also understood by policy maker at the European Commission (e.g. as per the "Factory of the Future" multi-annual road-map [START_REF]European Commission: Factories of the Future PPP Strategic Multi-Annual Roadmap[END_REF]), and embraced by funding programmes for industrial research (FP7 and Horizon 2020 [8]).

The challenges related with customization lay with the need to efficiently deal with adjustable product physical configuration (whose options must be clearly identified

Fig. 1 .

 1 Fig. 1. UML Class Diagram of the Shared Data Model

Fig. 2 .

 2 Fig. 2. UML Component Diagram of the Supply Chain Tool

Fig. 4 .

 4 Fig. 4. Screenshot of a sample simulation result (order lead time)

Fig. 5 .

 5 Fig. 5. Screenshot of the displaying of the sustainability assessment results

http://cordis.europa.eu

and limited), with the complexity of producing and handling customized Lot Size One orders, and with the need to cope with sustainability requirements.

The research work here presented focuses on the development of a decision support tool meant to cope with the definition of a proper supply chain for sustainable mass customized products, where current tools fall short. This is done in the framework of the S-MC-S project, that promotes the development of a software environment for the collaborative design of a sustainable mass customized solution space that takes into account product, process and supply chain.

The tool developed uses a client-server architecture and exploits a Shared Data Model that facilitates the integration of many different design tools coming from several providers. The tool empowers a better coordination of the actors involved in the supply chain recognized in the context of sustainable production of mass customized goods, which was one of the limiting factors of mass customization successful implementation. Indeed it supports, through an adherent to reality simulation, the selection of proper suppliers capable to handle the required flexibility in an order-driven production and, at the same time, it fosters the ability to predict the system capability to satisfy the incoming orders within the allowed lead time.

Information

Service,


Matteo Mario Savino
email: matteo.savino@unisannio.it

Antonio Mazza
email: antonio.mazza@unisannio.it

Agent Based Resources Allocation in Job Shop with Reentrant Features: A Benchmarking Analysis

Keywords: Re-entrant Job Shop, Multi-agent system, Resources Allocation Management, Production Scheduling

Job shop production can be characterized by some unplanned event like re-entrant jobs at a certain stage of the production process. In this case a dynamic allocation management of the resources involved (machines, transporters, etc) can be key factor of success in optimizing some production parameter. The present work studies a multiple objective job shop in which the constraint that a workpiece visits a machine only once is relaxed. The production shop floor has been structured with a Multi Agent System (MAS) able to front dynamically these type of events. A benchmarking analysis is provided to compare the solutions found with other two main dynamic MAS-based systems present in literature.

Introduction

Flexibility and re-configurability are crucial issues for modern manufacturing system to meet the demand of continuously changing market and customers' requirements. Reconfigurable manufacturing methodologies were proposed in recent years and considered as an effective approach to achieve flexibility and efficiency of manufacturing systems and related services [START_REF] Savino | Integrated system for maintenance and safety management through FMECA principles and fuzzy inference engine[END_REF]. In order to implement reconfigurability, intelligent manufacturing philosophies, such as Multi Agent Manufacturing, have been used. In such type of approaches the system is reconfigured through a cooperation and negotiation of distributed and autonomous intelligent entities. Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. In the Technoware organization to which firms are aiming [START_REF] Ouzrout | PLM maturity model: A multicriteria assessment in southern Italy companies[END_REF] this aspect is particularly attractive to create software operating in distributed and open environments in which problems are essentially of dynamic and stochastic nature. In such context the traditional methods or algorithms like Branch and Bound (BB) and Constrained Based Propagation (CBP) might not be very useful. The aim of the present work is to propose an approach dealing with a dynamic management of a job shop production system featured with re-entrant job possibilities. The cooperation among specific resource agents and a dedicated scheduler agent has made possible the realization of an flexible approach, which effectiveness has been benchmarked with other recent system present in literature. In the work, we consider a variation of the classical job shop scheduling problem, the reentrant job shop problem, in which it is relaxed the restriction that each job visits a machine only once in the shop floor. In this context the management of this type of event has been made with the aim to obtain a new local reschedule of the required resources.

Background

Resources allocation management involves several tasks related to the allocation of jobs, or even workers on machines, over a time period to perform a certain management tasks [START_REF] Groover | Automation, production systems, and computer integrated manufacturing[END_REF][START_REF] Neubert | Flow shop operator scheduling through constraint satisfaction and constraint optimisation techniques[END_REF]. It is a decision-making process that plays an important role in most manufacturing and service industries [START_REF] Pinedo | Planning and scheduling in manufacturing and services[END_REF]. The most common manufacturing system worldwide is the job shop, which layout is associated with the production of small volumes/large variety products and operates in make-to-order environment. A specific job shop problem is the Re-entrant job shop, originally proposed by Kumar (1993) and considered as the third class of manufacturing system distinguished from job shop and flow shop [START_REF] Wang | A Multi-Agent Architecture for Re-entrant Manufacturing Line[END_REF]. The re-entrant job shop scheduling problem extends the classical model by relaxing the restriction that each job visits a machine only once [START_REF] Zoghby | Modeling the reentrant job shop scheduling problem with setups for metaheuristic searches[END_REF]. In this context [START_REF] Fattahi | A hybrid algorithm to solve the problem of re-entrant manufacturing system scheduling[END_REF] proposed a bi-objective algorithm which uses the simulated annealing algorithm for the re-entrant manufacturing systems, in order to maximize the production rate of the system and minimize the work in process (NP-Hard class). Wang and Lin, in 2009, proposed to solve the re-entrant job shop system as an ant colony system, developing an agent based simulation platform. [START_REF] Vinod | Development and analysis of scheduling decision rules for a dynamic flexible job shop production system: a simulation study[END_REF] focused on a simulation-based experimental study of scheduling decision rules, while [START_REF] Zhou | Simulating the generic job shop as a multi-agent system[END_REF] proposed a model combining Discrete Event System (DES) and Multi-Agent System (MAS) to simulate a real-time job shop working as a test bed to study the performance of control rules and algorithms in dynamic job shop scheduling. MAS are characterized by the presence of two or more agents which interact and work together to perform some set of tasks. An agent may be a piece of software or a hardware that can perceive any changes in its environment and act upon through actuators, as for example in the Microgrids [START_REF] Logenthiran | Multi-Agent System (MAS) for Short-Term Generation Scheduling of a Microgrid[END_REF]. MAS have been used to handle both externally and internally driven events disruptive for the planned allocation of manufacturing resources. Tan et al. (2008) developed a MAS based Dynamic Resource Allocation Management (DRAM) System in which each agent is specialized in a sub domain. The system was tested and demonstrated its efficiency in case of unforeseen events like work-pieces delays or machines breakdown, but it was not tested against the problem of re-entrant jobs. According to [START_REF] Deng | Scheduling of Re-entrant Line Based on Swarm Intelligence[END_REF], re-entrant line is considered as one of the most complex manufacturing processes distinguished from job shop and flow shop. To front the problem they proposed a new model of constructing swarm intelligence on multiagent system by applying an ant colony scheduling algorithm (ACSA). Wang and Lin (2009) presented a simulation model of a re-entrant line which is based on MAS and HLA (High Level Architecture) in which a new model of constructing an ant algorithm on multi-agent system is proposed to find a satisfactory scheduling scheme.

The work considers the shop floor of a firm producing power generators, with a job shop layout in which jobs need to be reworked after some external processing. Fig. 1 gives the layout of a part of the shop floor with a bridge crane as transportation resource. CA receives new manufacturing orders and order changes from the ERP system. For orders where the quantity has been reduced, it reports the state of completion and quantities of the WIP that are no longer required by the order. CA performs the following actions: 1. Generating a JA for each manufacturing order; 2. Passing relevant manufacturing order information needed by the JA to manage the completion of the manufacturing order; 2. Cancelling a JA when the manufacturing order is completed. Each JA represents a single manufacturing order receiving the following information: 1. Manufacturing order ID and due date; 2. Product quantity; 3. A listing of the process steps and their corresponding manufacturing lead times. JA submits a request for the first process in the manufacturing routing of the product to the SF, which publishes the job or service requests. RAs of equipment and transporters that are in operation requests for the job specifications from the SDF. Upon receiving the job specifications, each RA will evaluate if it can handle the job request. In the case of a process job, RAs of process equipment prepare their bids and submit the request to the JA. After the JA has awarded the process job, it will now submit a service request for transportation to move the raw materials or Work in Process (WiP) to the process work centre of the process RA that has just been awarded with the process job, to the SF. RAs of equipment and transporters will request for the job specifications from the SF. Upon receiving the job specifications, each RA will evaluate if it can handle the job request. In this case, as the request is for transportation service, the RAs of process equipment will ascertain and realize that they are not suitable for the job. RAs of transporters will prepare and submit their bids. JA, upon receiving the bids, asks the SF to remove its request, evaluate the bids, and award the job to the RA that has submitted the most favorable bid. When a re-entrant job occurs, the JA is informed by the RA. The JA decides if the current process step should stay with the process equipment until the equipment is serviced and brought back to operation to continue, or to initiate a re-allocation of the current process step to other available equipment. When a process equipment or transporter is switched on to start operation, the RA is created. The RA is destroyed when the process equipment or transporter is switched off. A job that is awarded to the RA of either a process equipment or transporter by a JA, will be communicated to the sub-system controller for execution. The sub-system controller essentially receives job instructions from the RA and periodically sends the job status and the health condition to the RA. Negotiations are summarized in Fig. 3.

Fig. 3. Negotiations

The Scheduler Agent (SA) is created to perform a scheduling rule defined according to a specific need of the firm. SA is used to give a precedence to the re-entrant job by associating a flag to each operation to indicate a manufacturing operation previously performed. Each RA sends its waiting queue of tasks to the SA, which arranges the list according to the scheduling rule assigned. Once applied the scheduling algorithm, the SA answers with the updated order of tasks to be executed on the machine. The full list of tasks of each machine is periodically collected and arranged by the SA. The list is dispatched by the machine creating in the meantime a parallel queue of waiting jobs to be ordered. In this way, it is avoided an undefined waiting of tasks characterized by heavy processing time. In the experimental campaign described in the next section, due to the features of the firm, the scheduling rule which has been applied is the shortest task first. The SA is able to collaborate and communicate with the equipments by the use of its two behaviors, named ReceiveQueue (RQ) and ApproveQueue (AQ). RQ receives the request made from the machine containing the list of tasks present in queue which must be ordered according to a scheduling rule. AQ and RQ can be used to establish the priority rule related to the re-entrant jobs, reducing the delay caused by new re-works. Once the list has been ordered, the SA sends the list to the equipment which made the request in order to allow tasks processing.

The experimental campaign

In order to test the system an experimental campaign has been conducted with five main scenarios. In this paper, due to space constraints, we present the most interesting one related to the presence of two re-entrant jobs and a fault on a transporter. The results obtained by the demonstrator are compared applying the DRAM of Luo et al (2009), and the Centralized Scheduling Optimization (CSP) function of [START_REF] Neubert | Flow shop operator scheduling through constraint satisfaction and constraint optimisation techniques[END_REF]. The shop floor is composed of five machines typologies (M 1 …M 5 ) for the execution of six jobs. Each job task P i is associated to the corresponding M i Machine. Errore. L'origine riferimento non è stata trovata.a and 1b shows a comparison between the three systems (SA, DRAM and CSP) for this first case. Process routing, and results concerning Makespan and Average Tardiness are reported for each system. SA scheduler provides good results in terms of WIP level by placing in an intermediate situation and reducing the WIP obtained by the application of DRAM scheduler. A further analysis has been conducted in order to evaluate Makespan distribution obtained by the application of the three schedulers. Fifteen simulation runs have been conducted in order to perform this analysis. The average and the standard deviation of the 15 samples are shown in Tab. 3. In spite of the small difference obtained for the average value, SA and CSP allow to obtain an important result concerning the standard deviation which is now reduced of around 50%. This result indicates that the use of SA and CSP schedulers introduce an uniformity of execution times, i.e. a lower time dispersion. In Fig. 8 we show the trend of execution times by subdividing Makespan range into five classes and associating to each of them the occurrences fitting into the same class. The use of SA and CSP reduce time dispersion and allow to thicken values within some grouping classes.

Tab. 1a. Benchmarking -Routings

Tab. 3. Makespan -Average and standard deviation

The time necessary to complete the negotiation phase is usually very low (few seconds). The simulations conducted allowed to note that in a negotiation phase, when we have minor products but more jobs, this time takes only the 3% on the total time, instead when we have more products but minor jobs it is slightly higher, with a value maximum equal to 9.5%.

Conclusions

In this paper a MAS model has been applied for the job shop scheduling in a dynamic manufacturing system with the problem of re-entrant job, in order to give to the system the possibility to react to the events occurring in the real scenario. The model has been created first of all by identifying the agents involved in the system. In the systems developed a scheduler Agent determines the order for the execution of requests requiring the of a specific resource. An analysis on the results of a case study has been conducted to test the behavior of the model under specified conditions and a combination of events. The results showed a good functionality of the system, highlighted by the improvement the main performance indicator, represented the max tardiness, WiP and Makespan. Good results have been obtained also considering job routing, especially if compared with the other two algorithm present in literature which with the benchmarking has been conducted.

Fig. 1 .

 1 Fig. 1. The shop floor The software agents communicate each other by a Service Oriented Architecture (SOA) and though the Contract Net Protocol (CNP) in order to perform the allocation of the jobs (Pasatcha and Sunat, 2008). CNP plays an important role in the dynamic characteristics of the agent-based system integration framework, providing a mechanism for agents to interact dynamically (Yun et al., 2009). CNP consists of two types of decision makers: i) managers and ii) contractors. A manager seeks a contractor to complete a task. It sends a request to all eligible contractors for getting bids of the task. After receiving the bids from the contractors, the manager awards the task to the best contractor based on certain criteria (Lau et al., 2006). The present Job allocation system uses the following five types of software agents: Job Agent (JA); Scheduler Agent (SA); Coordinator Agent (CA); Resource Agent (RA); Service Facilitator (SF). Fig. 2 shows the system the relationship between the JA, SA, CA, RA and SF.

Fig. 2 .

 2 Fig. 2. System architecture

An additional analysis has been focused on the evaluation of the WiP by considering the average size of each buffer which characterizes a machine. In this case the WiP value represents the number of tasks waiting to be processed in the buffer queue of the machine (Luo et al, 2009).

Tab. 2b. Benchmarking -Delay, Makespan, Tardiness Tab. 3 shows the results obtained with respect to the buffer of Machine 1.

Tab. 3. Average WIP comparison

Buffer number Average WIP [# of tasks]

The introduction of the SA allows to reduce of a considerable amount the average WIP with respect to DRAM system: the reduction is around 13%. The scheduling rule aims to give execution priority to those tasks characterized by a low completion time, avoiding an increasing of the tasks in queue. We note also that the centralized structure of CSP allows to obtain a better result by reducing WiP of around 3% with respect to the modified version of DRAM. Fig. 4 shows the trend of the WIP with respect to the execution time of all the jobs for Machine 1.


Hendro Wicaksono
email: wicaksono@fzi.de

Kiril Aleksandrov
email: aleksandrov@fzi.de

Sven Rogalski
email: sven.rogalski@kit.edu

Jivka Ovtcharova
email: jivka.ovtcharova@kit.edu

An ICT Supported Holistic Approach for Qualitative and Quantitative Energy Efficiency Evaluation in Manufacturing Company

The global climate change and the rising of energy prices force manufacturing companies to regulate their energy usage. A suitable step to achieve that is the introduction of energy management. This paper presents an ICT based holistic approach to help manufacturing companies in the implementation of energy management system. It consists of methods to support quantitative and qualitative energy efficiency evaluation of their operations. The approach uses an ontological knowledge base containing the structures and rules representing best practices as reference of energy efficiency to support the qualitative evaluation. In the approach, we also develop measurement figures called Energy Performance Indices (EPI) to determine the energy efficiency degrees in different organizational parts of the company. The paper also describes the application of the approach in a small medium sized manufacturer.

Introduction

The global climate change and the rapid growth of industrialization, have led to a significant increase of energy demand that results to constantly increasing of electricity, gas, and oil prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. Hence, energy efficiency in accordance with the economization of production costs is an important competitive factor in the energyintensive industry. A common solution to achieve this is through a corporate energy management. Energy management defines the sum of all processes and measures to ensure minimal energy consumption by a given demand including the implementation of organization, information structure, and tools [START_REF] Kahlenborn | DIN EN 16001: Energy Management Systems in Practice -A Guide for Companies and Organisations[END_REF]. The standard ISO 50001 describes the requirements for energy management systems in industrial companies. However, most of the manufacturing companies face problems in implementing the energy management standards, due to the distributed and unstructured energy related information within the company. The information is difficult to be accessed by all stakeholders in the company. Best practices to avoid energy wasting are only known by some employees. This is due to the knowledge gap and insufficient access methods to the knowledge.

To overcome this problem, this paper introduces an approach utilizing information and communication technology that supports a holistic evaluation of energy efficiency both qualitatively and quantitatively. Qualitative evaluation focuses on the criteria that can be observed but not measured using figures, such as energy wasting situations, or energy efficient best practices. The qualitative evaluation is performed by employing a knowledge base, which is accessible by all of personals and systems in the company. The best practices and knowledge about energy efficient and energy wasting activities are standardized, formalized and stored in the knowledge base. Researchers have been applying ontologies as knowledge representation in various domains to solve the shared understanding problems among people and even software. In this paper, the knowledge base is represented with OWL (Web Ontology Language)1 combined with SWRL2 . Quantitative evaluation evaluates energy efficiency using measurement figures. Therefore it is possible to determine the energy efficiency degree in different organizational parts or processes in company. The figure is called Energy Performance Indices (EPI). In this paper, energy performance indicators that are able to measure the energy efficiency in different level of company organizations, is proposed.

Literature Overview

Researchers have developed knowledge based approach since end of 80s to manage information from different sources to support the decision making, in order to improve the efficiency, flexibility, and reliability of manufacturing processes [2][3]. Knowledge and communication models were developed to relate different aspects in manufacturing and to allow coordination of different components in the manufacturing organization [4] [5]. For the last ten years, researchers have been applying ontologies as knowledge representation in various domains, including manufacturing. Ontology has been used as a solution to the shared understanding problems among people and even software [START_REF] Noy | Ontology Development 101: A Guide to Creating Your First Ontology[END_REF]. It has also been proven to harmonize the knowledge gap between customers and manufacturers during the requirement elicitation phase [START_REF] Wicaksono | Ontology-driven Requirements Elicitation in Product Configuration Systems[END_REF] and to solve the semantic ambiguities [START_REF] Valiente | Applying an ontology approach to IT service management for business-IT integration[END_REF]. Different approaches to create ontologies in manufacturing domain were developed to model the processes, product, and resources as well as their relation in production [9] [10]. In energy management domain, to improve the energy efficiency especially in households, analysis of energy consumption of different appliances from various manufacturers and technologies is performed with the help of ontology as the integration model [11] [12]. Furthermore, ontology combined with rules is also used to represent the knowledge base of an intelligent energy management system that monitors and controls the energy consumption in a household [START_REF] Wicaksono | Knowledge-based Intelligent Energy Management Using Building Automation System[END_REF]. Until now, there is still a lack of knowledge based approach to sup-port the evaluation tasks of energy management in manufacturing. This paper introduces a novel method using ontology and rules represented knowledge to formalize and to structure the knowledge and the best practices as references to regulate the energy consumption in manufacturing. By using metrics as the performance indicators, an assessment for the state and condition of complex systems is easier and faster, due to their simple representations. The figures to measure energy efficiency are classified into different categories, such as absolute, relative, classification, and relational figures [START_REF] Kals | Betriebliches Energiemanagement -Eine Einführung[END_REF]. This paper focuses on relational figures, since they describe the energy consumption based on cause-effect relation, for instance, energy consumption per a produced product piece. German Engineer Association (VDI) introduces different technical figures related to energy evaluation [START_REF]Verein Deutscher Ingenieure (VDI): Energetic characteristics: definitionstermsmethodology[END_REF]. The energy requirements relative to the amount of products depend very much on the type and quality of the products. An approach to fix this problem is by converting the output product values into a particular unit [START_REF] Müller | Energieeffiziente Fabriken planen und betreiben[END_REF]. Some metric systems have been developed to evaluate the energy efficiency for entire industry sectors, such as EPI from Energy Star and ODEX [START_REF] Boyd | Measuring Improvement in the Energy Performance of the U[END_REF] [18] [START_REF] Tanaka | Assessing Measures of Energy Efficiency Performance and their Application in Industry[END_REF]. However, there is still lack of figures that are able to measure energy efficiency of different organization levels in a single manufacturing company, such as of a machine, of a production line, and of the whole factory. This paper proposes a method that addresses this problem.

Overview of the Solution

In this paper, we propose an ICT framework that supports the implementation of energy management in manufacturing companies consisting of both tools for quantitative and qualitative energy efficiency evaluation. Fig. 1 illustrates the overview of the framework. The information about products, production processes, and resources are collected from different IT systems of the company, such as ERP, MES (Manufacturing Execution System). The energy related data are gathered from Energy Monitoring and Data Acquisition (EMDA) System. Those different data are aggregated and analyzed, in order to have the relation between products, production processes, and resources e.g., machines, surrounding factors (i.e., temperatures), and energy consumptions.

The data analysis to create the relations could be done semi-automatically with the help of algorithms, such as data mining. Rules representing the energy usage patterns are deduced from the data mining result. The rules allow the identification of energy usage anomalies or inefficiencies. Knowledge engineer of the company could also models the common best practices of energy management with rules. These rules are stored in the form of a knowledge base as a reference of energy efficiency qualitative evaluations. The collected data are used to assign the values of the mathematical model that calculates the EPI. The simulation and optimization module is responsible to help the production planners to perform production planning that consider not only the costs but also energy efficiency. The module contains an optimization algorithm for finding the optimal production schedule that takes the rules as the constraints and the maximum EPI as the objective function. Qualitative Evaluation using Knowledge Base

In our work, we develop OWL ontology for manufacturing energy management.

There exists PLM ontology that models the structure and relationships among products, processes, and resources [9] [20]. In our work, we consider knowledge elements representing the energy management with an additional related knowledge, such as ancillary, transport or intra-logistics and energy conversion facilities. Equation ( 1)

gives an example of a SWRL rule that models energy wasting practice in a stainless steel manufacturer. It describes, if an oven is active during a heat treatment process and the heating system is located in the same zone is still turned on, then it is considered as an energy inefficiency condition. The rule is created manually, since it represents the common knowledge.

Oven(?o)˄ HeatTreatment(?ht) ˄ operates(?o,?ht)˄ isActive(?ht, true)˄ HeatingSystem(?hs) ˄ hasState(?hs, true) ˄ Zone(?z) ˄ isLo-catedIn(?o,?z) ˄ isLocatedIn(?hs,?z)→ AncillaryFacilityEnergyWasting(?hs)

Fig. 2 illustrates a decision tree resulted from the data mining that extracts the relation between product properties and the energy consumption in forging process. The tree represents the common and normal situation that the forging process of products, having material type M1 and outer diameter greater than 115 cm, consumes energy between 175 and 268 kW. The tree is then transformed into SWRL rules, for example into rule (2), to detect the energy inefficiency or anomaly states. The rule implies, if the forging process on presser machine consumes energy more than 268 kW, then there is something wrong in the process causing energy inefficiency.

Product(?p)˄ hasMaterialType(?p, "M1") ˄ hasOuterDimeter(?p,?od) ˄ swrlb:greaterThan(?od,115) ˄ Presser3000T(?ps) ˄ Forging (?f) ˄ operates(?ps,?f) ˄ produces(?o,?p) ˄ hasEnergyConsumption(?ps,?e) ˄ swrlb:greaterThan(?ps,268) → ProductionFacilityEnergyWasting(?ps)

(2) Fig. 2. Decision tree of data mining result By using the knowledge, the energy wasting can be detected, hence energy efficiency can be evaluated. By assigning the variables with current states in the company, running a rule engine on the SWRL rules and using SQWRL 3 to query the knowledge base as shown in (3), the inferred information can be retrieved from the rules. The retrieved information can be further processed by using the company's messaging or alerting system.

AncillaryFacilityEnergyWasting(?h)→ sqwrl:select(?h) ProductionFacilityEnergyWasting (?h)→ sqwrl:select(?h)

Energy Performance Indices (EPI) for the Quantitative Evaluation

To develop energy efficiency metric for a single manufacturing company, first, it is important to define the system boundaries and a model that covers the different organization levels of production. In our work we develop UPNT, which is based on UPN model [START_REF] Fünfgeld | Energiekosten im Betrieb[END_REF], and considers the machine, production line and factory level. The basic elements of the UPNT model are energy conversion (U), production (P), ancillary (N), and transport (T) facilities. The production and transport facilities are modeled as subclasses of ontological class ManufacturingResource, the energy conversion as EnergySource, and ancillary facility as BuildingElement in ontology. Fig. 3 illustrates the boundaries of different manufacturing organization levels. It can be seen, that the inputs of a production facilities are made up from direct and an indirect ones. The direct inputs are from utility companies or conversion facilities. The indirect inputs are provided due to the fact that the production facility receives the outputs of ancillary and transportation facility. Therefore the energy cost of a production facility is also ascribed with the output of the connected ancillary and transport facilities. Equation (2) calculates the EPI for factory level. The calculations for the machine and production line levels are also performed similarly. The calculation method is applied in a stainless steel manufacturer. Table 1 shows examples of the calculation results in machine and production line levels. Theoretically, the EPI values should be positive rational numbers. Less than one value means that energy costs exceed the supply and therefore should not occur. Otherwise, it would be an alarm signal, because the process is extremely inefficient. Higher values are conceivable when a process generates a lot of values and /or require very little energy. A value greater than one does not automatically make a positive contribution margin, as there are other costs in addition to the energy costs that must be met. However, this is not the objective of the EPIs. Rather, they should provide a comparison of different alternatives in the choice of equipment to be used.

Summary

This paper presents an approach that employs ICT method to support the holistic evaluation of energy efficiency in the manufacturing on both quantitative and qualitative ways. The knowledge base provides the structured and formalized knowledge as references of best practices in the company related to energy efficiency. By using the data mining, the previously unknown relations among products, manufacturing processes, facilities, resources and energy consumption are revealed and incorporated into knowledge base. The ontology represented knowledge base provides formalized knowledge that allows common understanding among employees and software in the company. By using developed EPI, the energy consumption's performance of different production facilities and production lines is measured quantitatively. Therefore, it helps the managers and production planners to plan their production not only by considering the costs, but also by considering the energy efficiency. Manufacturers can benefit from this approach by receiving an evaluation tool that enables them, despite the ever-intensifying competition with higher energy costs and scarce natural resource and an increase of production of complexity, to compete with competitors. The approach also improves the awareness of the stakeholders to make decision by strongly taking considerations whether their decision affects the energy efficiency in the company.

Fig. 1 .

 1 Fig. 1. Overview of the approach

Fig. 3 .

 3 Fig. 3. Energy flows and system boundaries for EPI calculation

  (EPI = 21,0), Drop hammer (EPI = 21,9), Ringrolling (EPI = 15,5) 19,5 L2: Oven (EPI = 49,6), Presser (EPI = 11,4), Ring-roller (EPI = 18,9), Rolling-machine (EPI = 21,7) 19,3 L3: Oven (EPI = 30, 3), Drop hammer (EPI = 21,0), Rolling machine (EPI = 20,9), 21,3

Table 1 .

 1 EPI calculation results in stainless steel production

http://www.w3.org/TR/2004/REC-owl-ref-20040210/

http://www.daml.org/rules/proposal/

http://protege.cim3.net/cgi-bin/wiki.pl?SQWRL