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In recent years, the production of renewable energy has increased continuously to reduce fossil fuel consumption and CO2 emissions and to increase energy efficiency. The challenge of industries is to integrate renewable energy systems into the existing power system of manufacturing industries. In the energy flexibility approach, the manufacturing energy demand is aligned with renewable energy availability, to improve the use of the renewable energy source. This paper aims to investigate a manufacturing system supplied by a photovoltaic plant coupled with a battery storage system. A basic storage model has been developed and implemented to pursue this issue. The model is applied in the simplified case of a manufacturing system composed of a cutting numerical control machine, which can adapt the cutting speed to align the power needed to the power supplied by the photovoltaic plant. However, the model can be extended to realistic production cases characterized by complex systems as long as the time evolution of energy consumptions are known in detail. The introduction of battery storage allows reducing the cutting speed fluctuations, improving the cutting life derived from the fatigue effect. This solution reduces the costs of the machine and improves the forecasting of the means needed for the manufacturing system. Finally, a detailed analysis of greenhouse gas reduction is discussed.

PurposeThis paper proposes a model based on minority game (MG) theory to support the decision-making regarding the efficient allocation and exploitation of resources/services among the partners of a cloud manufacturing (CMfg) system. CMfg system is a new manufacturing paradigm to share manufacturing capabilities and resources on a cloud platform. The use of a decision model to organize and manage the resources and services provided by the autonomous participants of a CMfg has crucial relevance for the system's effectiveness and efficiency.Design/methodology/approachThis research proposes a noncooperation model based on MG theory. The MG is designed to make decisions on the use of resources/services among the partners of CMfg with private information. A simulation environment was developed to test the efficiency of the proposed decision model. Moreover, an ideal decision model with complete information among the partners was used as a benchmark model.FindingsThe simulation results show how the application of the proposed MG model outperforms the MG model usually proposed in the literature. In particular, the proposed decision model based on private information has an efficiency closer to the ideal model with complete information among the partners of a CMfg.Originality/valueThis paper advances knowledge about the application of MG in the field of CMfg system. The proposed decision-making model based on MG is a promising approach to help enterprises, and especially small and medium enterprises, to participate in CMfg initiatives and to develop their business.

Purpose The purpose of this paper is to propose a framework for the analysis of students’ ratings of teaching quality in higher education and the disclosure of risky issues undermining the quality of teaching and courses that require attention for continuous improvement. The framework integrates two decision-based methods: the standardized u-control chart and the ABC analysis using fuzzy weights. The control chart, using the students’ ratings, allows the identification of those courses requiring an improvement of teaching quality in the short-medium term. While the ABC analysis uses fuzzy weights to deal with the vagueness and uncertainty of students’ teaching evaluations and provides a risk map of the potential areas of teaching performances improvement in the long term. The proposed framework allows the identification of teaching and course quality aspects that need corrective actions in response to students’ criticisms in accordance with different levels of priority. Design/methodology/approach This study adopts two methods, commonly used in industrial applications, i.e. the u-control chart and ABC analysis. Combining the results of a literature review on teaching evaluation and the application of these two methods as building blocks for the assessment, a framework to detect potential risks reducing teaching quality in higher education is proposed. The application of the framework is shown through an action-based case study developed in an Italian public university. Findings The study proposes a framework that combines two methods, i.e. u-control chart and ABC analysis with fuzzy weights, to support the assessment of teaching and course quality. The framework is proposed as an assessment approach of the teaching performance in higher education with the purpose to continuously improve the quality of teaching and courses both in the short, medium and long term. Originality/value The study provides an original contribution to the understanding of how to analyze students’ evaluation of teaching performance in order to take proper and timely decisions on corrective actions in response to the need of continuously improving the level of teaching and course quality.

Starting from the application of the Business Process Management to support the Higher Education Management System at University of Basilicata, this chapter focuses on the new developments of state regulation that require implementing a strategic performance system to measure, evaluate, and improve system performance in terms of efficiency, effectiveness, quality, outcome, and customer satisfaction. More specifically, the Italian legislative decree n°150/2009 imposes the implementation of a so-called “performance cycle” in all public organizations. One of the pillars of the “performance cycle” is the process management and the definition of the process map. Then, the Business Process Management developed to support the Higher Education Management Systems can be extended to the entire organization to support and manage the integrated cycle of performance of the University. This allows for developing an adequate risk mapping and assessment.

The buffer allocation problem is an NP-hard combinatorial optimization problem, and it is an important design problem in manufacturing systems. The research proposed in this paper concerns a product line consisting of n unreliable machines with n − 1 buffers and a preventive maintenance policy. The focus of the research is to obtain a better trade-off between the buffer level and the preventive maintenance actions. This paper proposes a dynamic control of the buffers' level and the interval between two consecutive preventive actions. The set of the parameter of the proposed policy allows choosing the reduction in the costs or the increment of the throughput rate. A simulation model is developed to test the proposed model to the solution proposed in the literature. The proposed policy leads to better results in terms of total costs reduction keeping high production rate, while the design of a fixed level of buffer works better for lower production rates required.

Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

The companies need to rapid response to new product introduction, mix and demand changes to stay competitive. A reconfigurable manufacturing system can quickly react to changes in products and market. The control method to reconfigure the machines of a reconfigurable manufacturing system is crucial for the performance level. This paper proposes a reconfiguration decision-making method based on a Game-Theory algorithm, and in particular the Gale-Shapley model. A periodic review strategy is used to create two sets: one set of machine over-loaded and one set under-loaded. The Gale-Shapley model forms a coupled of over-loaded and under-loaded machines. The reconfiguration concerns the under-loaded machine of the coupled adding also the task performed by the over-loaded machine. This paper presents a simulation environment developed to evaluate the proposed method and highlight the main topics. The simulation results highlight how the game-theory approach developed improves all the performance measures with controlled number of machines’ reconfigurations.

Manufacturing systems need to be able to work under the dynamic and uncertain production environment. Machine and routing flexibility combined with preventive maintenance actions can improve the performance of the manufacturing systems under dynamic conditions. This paper evaluates different levels of machine and routing flexibility combined with different degrees of preventive maintenance policy. The performance measures considered are throughput, work in process and throughput. The performance measures are compared with a system without any flexibility and no preventive maintenance actions. Different levels of flexibility and preventive maintenance actions are examined under a simulation environment. The simulation results highlight more important factors for the performance measures and the best combination of the factors to improve the performance.

The flexibility of manufacturing systems is one of the key factors to stay competitive when the companies face increasingly frequent market changes due to the rapid introduction of new products and constantly varying product demand. Flexible manufacturing systems are characterised by high investment costs, and often offer more flexibility that what is really needed. The emergent paradigm of Focused Flexible Manufacturing System concerns hybrid systems composed both of general purpose and dedicated resources. The research proposed in this paper concerns a genetic algorithm to design hybrid manufacturing systems composed of dedicated, general purpose flexible and reconfigurable machines. It has been considered a production mix with the customer demand, and the manufacturing operations required. The proposed model determines the machines to acquire among dedicated, flexible and reconfigurable machines and the production allocation over the planning horizon. Moreover, the combination of the genetic algorithm with the Monte Carlo simulation allows to include the market uncertainty in the decision model. A simulation environment has been developed to integrate the genetic algorithm and Monte Carlo approach.

The climate mitigation and the reduction of energy cost in manufacturing processes drive to expand the electricity generation from renewable sources. Nonetheless, intermittency of renewable energies, especially solar and wind energy, represents one on the main challenge, typically overcome by the installation of electricity storage systems. This issue can be addressed by a new and original approach, consisting in energy-flexibility of the production, in which manufacturing parameters are selected to optimize and to align production planning to the renewable energy availability. The paper deals with a time dependent theoretical and numerical model developed to calculate the time evolution of the electric power required by a manufacturing system, self-consistently coupled with a renewable plant. The aim of the model is to align the power required by the manufacturing system with the renewable energy supply in order to obtain the maximum monthly profit. The model has been applied to a single work center powered by the electric grid and by a photo-voltaic system, performing the machining process over one year of production. The model includes the tool cost, the stocked units, the energy cost and the penalty for the unsatisfied demand. The maximum profit has been calculated with a hourly adaption of manufacturing parameters, i.e. the cutting speed, to the renewable time dependent power profile. The model presents general features and can be applied when production processes are fully characterized. In order to find the maximum profit, the model, inherently nonlinear, has been solved by recurring to the Trust-Region Method. Different scenarios characterized by fluctuations of product demand are considered in order to investigate the sensitivity of the manufacturing system to the uncertainty of the forecast demand. The influence of the photo-voltaic supply has been investigated, comparing results obtained in the case of manufacturing systems powered only by the electric grid. Numerical results show how the proposed method allows to select an optimized production planning, reducing the energy costs and CO2 emissions and finding the maximum profit with the best compromise between the market demand and energy costs.

The research concerns a make to order manufacturing environment and two classes of customers who submit orders: core customers long-term partnership and short-term customers. The orders submitted by the short-term customers depend on the price and service level proposed by the firm. This research proposes a pricing policy based on a fuzzy tool to set the price for short-term customers in production networks. The fuzzy tool captures the state of the manufacturing system in terms of workload of the manufacturing system, reliability and demand fluctuations. The fuzzy tool developed supports the cooperation mechanism among the enterprises of the production network. A discrete event simulation environment is used to test the proposed approach in a static and dynamic environment. The numerical results are compared to the case without cooperation among the firms within the production network. The simulation results show how the proposed approach outperforms the other policies in all conditions tested. The combination of fuzzy dynamic price and cooperation mechanism improves the performance with low movements of orders among the partners of the network; this means that the transportation costs have a low impact.

A manufacturing system composed by several stages in serial system is considered. Each stage can produce several types of products from a semi-finished product. At each stage, a production control strategy is performed to release MTO and MTS orders. The MTS orders try to reduce the lead times and increase the service level for the customers. This research proposes MTS control policies observing the customer demand with higher service level reducing the stock level of the buffers at each stage. A simulation environment based on multi-domain methodology has been developed to test the proposed approach compared to policies proposed in literature. The numerical results are obtained for different levels of customer demand, fluctuations of the product type requested, and the reliability of the production system. The main results show how the proposed approach leads to better results both for service level and reduce the MTS level in all conditions tested.

In today’s competitive market, customer demand is characterized by high fluctuations and it is more difficult to forecast. Therefore, the classical cellular manufacturing systems need to be revised in order to stay competitive. Among the different approaches to support the cellular manufacturing systems, the concept of virtual cellular manufacturing systems can improve the performance in turbulent environment. This paper proposes two policies to support the problem of the reconfiguration of machines in a virtual cellular manufacturing system. The policies proposed are tested developing a multi-domain simulation environment in ExtendSim®. The experiments conducted show how the proposed approach improves the performance of the manufacturing systems especially when some disturbances occur as demand fluctuations, machine failures and processing time are uncertain.

The scheduling problem is typically investigated in the literature considering the job-processing times fixed and constant values. In real manufacturing systems, the job-processing times can change due to several factors such as machine and cutting tool deterioration and maintenance issues. This article studies the integration of the deteriorating processing time problem in a multi-agent architecture that supports the scheduling activities in a job-shop manufacturing system. The main problem addressed in this research concerns the policy to restore the workstations to their initial state. It is observed that the state of the workstations and the performance measures of the manufacturing system enables the decision of when to perform the restore activity. It is proposed a policy based on a variable threshold level to decide on the restoration of the workstations. A simulation environment is developed to test the proposed approach to investigate several performance measures. The simulation results highlight how the proposed policy allows to keep a high level of performance reducing the number of restore activities.

This paper proposes a Framework for Capacity Sharing in Cloud Manufacturing FCSCM able to support the capacity sharing issue among independent firms. The success of geographical distributed plants depends strongly on the use of opportune tools to integrate their resources and demand forecast in order to gather a specific production objective. The framework proposed is based on two different tools: a cooperative game algorithm, based on the Gale–Shapley model, and a fuzzy engine. The capacity allocation policy takes into account the utility functions of the involved firms. It is shown how the capacity allocation policy proposed induces all firms to report truthfully their information about their requirements. A discrete event simulation environment has been developed to test the proposed FCSCM. The numerical results show the drastic reduction of unsatisfied capacity obtained by the model of cooperation implemented in this work.

In today’s competitive environment, customer demand has become increasingly diversified and customised. In order to stay competitive, the manufacturing companies need to adopt models that are able to rapidly adapt to market changes. The reconfigurable manufacturing system emerges from the scientific literature as a relevant manufacturing paradigm to meet changes and uncertainties in a dynamic manufacturing environment. This research proposes a cellular manufacturing system (CMS) with reconfigurable machines to handle the turbulent market conditions. The approach proposed concerns three mathematical models to support the design, reconfiguration and scheduling of the manufacturing system. The three linked models are developed in LINGO® package. The main optimisation model concerns the design of the CMS, considering product demand not deterministic but described by several discrete probability scenarios. Then, an optimisation sub-model has been developed to manage the reconfiguration at fixed periods. An all...

This paper proposes a Monte Carlo approach to make the capacity investment decision. The environment concerns the capacity expansion issue for independent plants operating in a collaborative network. The plants that compose the network can share capacity through a negotiation protocol in order to balance under and over utilisation due to the customer demand. The plants apply the capacity expansion process following a periodic review policy. The generic plant makes the decision using a Monte Carlo approach that takes into account the information of the plant and the information of the collaboration with the plants of the network. The Monte Carlo simulator provides the information to each plant to support the capacity investment decision. The proposed approach is compared with a case characterised by information sharing among the plants proposed in literature and a case without information sharing. A simulation environment based on the JAVA package has been developed in order to test the approach in several...

In this paper, we analyze a model of capacity sharing for a set of independent firms, geographically distributed, that often have to implement an opportune tool to integrate their resources and demand forecasts in order to gather a specific production objective. We formulate the problem as a cooperative game and identify a capacity sharing solution using the Gale–Shapley model. The allocation rule takes into account the utility functions of the involved firms, and we show how the capacity allocation rule can be designed to induce all firms to report truthfully their information. Moreover, we show that, under this allocation rule, truth telling is a dominant strategy, with each firm reporting truthfully its private information, regardless of the reporting decisions of other firms. Moreover, the proposed research develops a distributed approach able to facilitate the capacity sharing process by using a multi-agent architecture; then a discrete simulation environment has been developed to compare the proposed approach with a centralized one. Several simulation scenarios were conducted to analyze the performances' trends in various environment conditions.

Enterprises need to adopt new business paradigms in order to make a rapid response to market changes and improve the competitiveness. The development of information and communication technology allows the support of new business paradigms such as extended enterprises, virtual organisations, regional clusters, etc. The research proposed concerns the study of a dynamic network in which the partners change in short term periods. The model proposed supports the enterprises in the decisions to participate or exit in a network of enterprises. The model is based on the definition of a set of rules that operate with local information to take the decisions. The local information is the output of the collaboration process; this means that the approach proposed integrates the collaboration methodology and the decision model. This environment is related to independent plants that cooperate with reduced information sharing. A simulation environment is developed to test the approach proposed. The simulation results sho...

In recent years, most universities are facing with the problem of quality management in higher-education systems and institutions. The Countries that have signed the Bologna Declaration are enforced to implement an internal system for quality assurance. One of the key element of a quality management system is the process modelling with delineation of responsibility, authority, procedures and plans. This chapter describes the use of the Business Process Management to support the higher Education Management System at University of Basilicata. The case study developed concerns the following steps: visualizing, measuring, analysing by a simulation environment, and improving the management system. The original contribution of the research concerns the development of a simulation environment using BIZAGI modeller based on BPMN notation in order to analyse and improve the performance of the case study investigated. Moreover, the use of a software based on BPMN can improve quality (error rate reduction), efficiency (saving of time) and agility (implementation of processes of changes).

The research presented concerns the policy to manage a job shop in which the machines have controllable processing times. A controllable processing time means that it can be reduced processing time by using additional resources. The model proposed is based on a multi-agent architecture that supports the manufacturing system. The policy proposed concerns the evaluation of the workload of the resources. It is necessary to define the following issues for the controllable time process of a resource: the condition of start and the duration of the process time reduction. Two approaches are proposed to assign the resources to the machines. The first approach concerns the reduction of the processing time one machine at time, while the second approach distributes the additional resources proportionally among the machines. A simulation environment is developed to test the proposed approach in several dynamic conditions. The simulation results show that the control of the processing times proposed allows to improve significantly the performance.

Nowadays the ability to provide outpatient services with exceptional quality is paramount to long-term survival of hospitals, as the revenues from outpatient services are predicted to equal or exceed inpatient revenues in the near future. Identifying the relative weight of different dimensions of healthcare quality service which concur together to determine outpatients satisfaction is very important, as it can help healthcare managers to allocate resources more efficiently and identify managerial actions able to guarantee higher levels of patients' satisfaction. This study proposes the use of Artificial Neural Network (ANN) as a knowledge discovery technique for identifying the service quality factors that are important to outpatient. An ANN model is developed on data from a panel of outpatients of public healthcare services.

Manufacturing systems are subject to a degradation process that leads to machine failure if no action is taken. Machine failures reduce the performance of the manufacturing system with loss of profits. The research proposed here concerns the evaluation of the manufacturing system performance in dynamic conditions when different maintenance policies are implemented in a multi-machine manufacturing system controlled by multi-agent-architecture. There are two extreme maintenance policies that can be applied: no preventive maintenance, where action is taken on the failure state, and intensive preventive maintenance, which can eliminate unforeseen failures, but at a high cost. Dynamic policy maintenance is proposed to reduce the number of maintenance operations of the preventive policy. A discrete simulation environment has been developed in order to investigate the performance measures and the indexes of the costs of maintenance policies. The simulations have been conducted for several levels of mix, product ...

Market changes, uncertainties and unpredictability characterize the market environment nowadays. The possibility to cooperate in a network allows companies to stay competitive. This paper proposes a game theory coordination mechanism for a network of independent plants; the approach is based on the OWEN's theorem properly simulated in order to investigate different performances in various environmental conditions. To support the network and face the capacity sharing issue, a Multi-Agent Architecture has been specifically developed. The simulation results show that the proposed mechanism could be considered a valid alternative to the more usual negotiation approaches in several market conditions. The plants gain considerable benefits by participating in a network supported by the cooperative mechanism proposed.

This paper investigates the performance of classical cellular manufacturing systems compared to other two systems proposed in literature: fractal cells and remainder cells. This paper proposes three strategies to control a cellular manufacturing system with remainder cell and how to configure and control a fractal manufacturing system. The performance measures of the manufacturing systems are analyzed when several unforeseen events occur as: machine breakdowns, production mix changes, demand fluctuations, and processing time variability. A simulation environment developed by Arena® package was used to investigate the three manufacturing system configurations. The performance measures investigated are: throughput, throughput times of the parts, work in process, manufacturing utilization, and due date performance (tardiness). The simulation results show how the fractal and remainder cells can be a valid alternative to cellular manufacturing systems in a very dynamic environment.

The e-marketplace in Business To Business applications allows enterprises to make economic transactions in a virtual marketplace. Often Small and Medium Enterprises (SMEs) do not participate to this potential opportunity because of some distrust as: the benefits of e-marketplace participation are not perceived clearly; SMEs doubt that e-marketplace fits the products offered. The possibility to evaluate the real value added by an e-marketplace can be a driving force to adopting e-marketplace for SMEs. The research proposes negotiation policies, customer's tactics and coalition tools as a value added services in e-marketplaces. The e-marketplace scenario is a private neutral linear owned by a third part with catalogue based procurement actions. A Multi Agent System methodology is used to implement the architecture of the e-marketplace. Furthermore, the author proposes the use of discrete-event simulation to test the suggested methodologies and to evaluate the economic value of adopting the proposed negotiation and coalition tools in e-marketplaces. The simulations are conducted in a very dynamic environment and a proper statistical analysis is performed. The simulation results show that the proposed methodologies lead to benefits both for suppliers and customers.

The research proposes an agent based approach, properly linked to production planning activities, able to support negotiation in catalogue based e-marketplace. In this context, production planning tools allow the creation of a link between commercialization and manufacturing activities, providing a better service for all the involved agents. At the same time negotiation tools take into account buyers’ and sellers’ identities and goals, providing better global satisfaction. A discrete event simulation environment has been properly developed. The simulation results show how to obtain an “e-marketplace mapping” in order to evaluate the real value added in a dynamic environment. This information can support Small and Medium Enterprises in e-marketplace adoption decision making process.

Despite the positive expectations and although they are nowadays considered the most spread Business to Business (B2B) solution, e-marketplaces are still far from representing a real tool for the extended enterprise: as the dramatic down-turn in the e-commerce sector demonstrated, e-business initiatives require solid business models that clearly relate the services provided to the overall profitability of the company. In this paper, we take a particular but quite general e-marketplace business model as our point of departure and use that model to motivate the development of algorithms to support management of trade among buyers and sellers. Specifically, e-marketplaces profitability can be increased by an integration of production planning, negotiation and coalition support tools. Production planning tools allow to create a link between commercialization and production activities, supplying a better service for customer, negotiation tools allow to make transactions taking into account both buyers' and sellers' goals and, finally, coalition represent the proposed course of action for small and medium suppliers not able to fully respond to the customer requests. This paper presents an innovative approach, based on multi-agent system, and a concerning simulation test-bed conducted to demonstrate, in a quantitative way, the advantages arising by adopting the proposed approach.

Multisite factories geographically distributed often have to implement an opportune tool to integrate their resources and demand forecasts in order to gather a specific production objective. The proposed research develops a distributed approach, for a network of independent enterprises, able to facilitate the capacity process by using a multiagent architecture and a cooperative protocol. The last one is based on game theory and, in particular, on Nash bargaining solution. Moreover, a discrete simulation environment has been developed to compare the proposed approach with the one in which no cooperation among enterprises exist. Several simulation scenarios were conducted to analyze the performances’ trends in various environment conditions. The goal has been the evaluation of the unallocated capacity, the unsatisfied demand, the profits generated by the network, the distribution of the transactions among the plants, and the number of activated links among plants. The simulation results show that the proposed approach leads to a better performance indexes and more relevant benefits when the dynamicity of the environment growths.

During a manufacturing operation, exceptions may occur dynamically and unpredictably. Their occurrence may lead to the degradation of system performance or, in the worst case scenario, may interrupt the production process. The research proposes a multi-agent architecture for the capacity reconfiguration problem in a reconfigurable manufacturing system (RMS). A policy to manage capacity exchange among manufacturing lines based on due date performance is proposed. The Multi-Agent architecture was built in a simulation environment developed in ARENA® package and it is compared with a dedicated manufacturing and flexible manufacturing systems. The simulations are conducted in several demand scenarios to test the approach in a static and dynamic context. The simulation environment developed can support the planner to decide among the different manufacturing systems by the evaluation of the manufacturing performance. The simulation results, in dynamic environment, showed that the proposed approach leads to similar performance to flexible manufacturing system.

Reconfigurable Enterprises (REs) represent production networks made of different and geographically dispersed plants that, in case of unpredictable market changes, can be reconfigured in order to gather a specific production objective. However, REs effectiveness and efficiency depend on the extent to which global performance is obtained. Basically two approaches are available to reach coordination: centralised or decentralised planning tools. In this paper, the authors propose a novel cooperative game theoretical approach for distributed production planning at high level of a RE; the proposed approach has been benchmarked in front of both a distributed one, based on negotiation, and a centralized one.

During a manufacturing operation, exceptions may occur dynamically and unpredictably. Their occurrence may lead to the degradation of system performance or, in the worst case scenario may interrupt the production process by causing errors in the schedule plan. This paper classifies three families of exceptions: (1) out-of-order events such as machine breakdowns, (2) operational out-of-ordinary events such as rush orders and (3) deteriorations of manufacturing resource performance such as reductions of machines' utilization. In all cases, in order to maintain an adequate level of system performance, it is necessary to detect exceptions, to diagnose them quickly and to recover them by taking corrective actions to avoid fault propagations. Decisions concerning how to deal with exceptions, i.e. which strategy to implement, depend on the manufacturing environment (dedicated line, flexible system, reconfigurable system or a mix of them) and the advantages arising from using a certain exception handling policy vary from one production system to another. The activity of reconfiguring manufacturing resources has been demonstrated to be a powerful operation strategy to handle machine breakdowns. This paper extends the concept of 'reconfiguration for exception handling' to other families of exceptions and proposes reconfiguration for their recovery. The reconfiguration process is handled by an agent-based control system that implements four negotiation processes among manufacturing resource agents.

Distributed production networks are considered organizational structures able to match agility and efficiency necessary to compete in the global market. Performances of such organization structures heavily depend on the ability of the network actors of coordinating their activities. The research proposes to model and design coordination problems within production network by using the Multiple Agent Technology. In particular, the paper proposes new strategies for coordinating production-planning activities within production networks. Such models have been developed and tested by using a proper simulation environment developed by using open source code and architecture. The results of the research can be located at two levels: (a) concerning the specific coordination problem addressed, the research provides some insights to make decisions about the choice of coordination approaches to be used in distributed production planning problems; (b) at more strategic level, the paper shows how Agent Technology and discrete event simulation can be used to build up efficient coordination structures for production networks.

The research concerns a make to order manufacturing environment and two classes of customers who submit orders. The core customers have a contract with the firm at fixed services level (price and due date). The short-term customers submit the orders based on the price set by the firm. In this paper, it is proposed a pricing policy based on fuzzy logic to set the price for the short-term customers. The fuzzy approach captures the state of the manufacturing systems in terms of congestion in order to set the price. The policy proposed is compared to a fixed price, and to a state-dependent policy based on three levels of price. A discrete event simulation environment is used to test the proposed approach in a static, dynamic environment and considering the reliability of the manufacturing system. The simulation results show how the proposed approach outperforms the other policies in all conditions tested.

Today marketplaces are characterized by a high degree of dynamism where companies need to operate by matching agility and efficiency. Supply Chain Management has become the crucial key to competitive advantage and traditional tools for enterprise management, such as ERP or MRP II, need to gain capabilities to allow inter-organizations relationships. The research presented in here aims at developing an Extended Agent Based ERP for enterprises relationship coordination. In particular, we refer to a vertical supply chain where suppliers operate under Assembly to Order (ATO) modalities; furthermore, suppliers are organised in a “buyer centric e-marketplace” by the assembler. In such a context, through the use of Multi Agent System (MAS) technology, the coordination of the ERP systems is approached by considering the development of an electronic negotiation system. The simulations conducted highlight the functionality of the cooperation among the ERP systems and the improvement of the performance both for customers and suppliers.

The research proposes two strategies to release the parts in a job shop environment to handle the customer order-scheduling problem. The first strategy is based on the evaluation of a dynamic conwip level to take the decision. The second strategy tries to anticipate the production of components of a generic order when the utilization of the manufacturing system is low. In this strategy, a fuzzy approach is proposed to decide how many components to release. A simulation environment has been developed to test the proposed approaches. Two benchmark models are used to compare the performance measures: no order release strategy and classical conwip. Moreover, the simulations are conducted in a very dynamic environment. The simulation results show how the fuzzy approach leads to the better results in all conditions tested with a relevant reduction of the inventory level.

The automated negotiation performed by a software agent is investigated in order to improve the benefits compared to a humane face-to-face negotiation. The profitability of e-business applications can be increased by the support of automated negotiation tools. This research proposes a set of learning methodologies to support both the suppliers’ and customers’ negotiation activities. The learning methodologies are based on Q-learning technique, which is able to evaluate the utility of the actions without a model of the environment. The context regards one-to-many negotiation and multi-issues (volume, price, and due date). A simulation environment is developed to test the proposed methodologies and evaluate the benefits compared to a negotiation approach without learning support tool. The simulations are conducted in several market conditions, and a proper statistical analysis is performed. The simulation results show that the proposed methodologies lead to benefits both for suppliers and customers when both the opponents adopt the learning approach.

The market fluctuations can reduce drastically the performance of manufacturing systems. Among several configurations, the configuration with a reminder machine/cell in a manufacturing system has been recently studied. The remainder machine is able to perform all the manufacturing operations needed in the manufacturing system. This paper investigates the effect of a hybrid preventive/corrective maintenance policy in a manufacturing system with reminder machine. The maintenance policy proposed is a trade off between the preventive and corrective approach. A simulation environment developed by Rockwell ARENA is proposed to test the proposed approach. The maintenance policy is tested in static and very dynamic environment. The performance measures are compared to a model with pure corrective policy. The simulation results show that the proposed maintenance policy with the use of reminder machine can improve significantly the performance of manufacturing systems.

The research proposed concerns the development of a multi-agent scheduling approach able to support manufacturing systems in different dynamic conditions. The negotiation protocol defined budget approach is based on a financial asset that each part obtains when it is released into the manufacturing system for processing. The part spends the budget to perform the manufacturing operations by the workstations; the virtual market in which part agent and workstation agents coordinate the decentralized system. A fuzzy tool is proposed to assign the budget to each part based on the objectives pursued. A simulation environment based on Rockwell ARENA® platform has been developed in order to test the proposed approach. The simulations are used to compare the proposed approach with classical dynamical scheduling approaches proposed in literature. The results show how the proposed approach leads to better results, and it can be selective among the different priority of the parts.

E-procurement transactions in Business to Business (B2B) environment showed a constant and positive trend in the last years. The most popular methodologies to support these tools are all related to dedicated protocols able to facilitate the agreements among customers and suppliers. This chapter proposes a Multi Agent Architecture integrated with several multi-attribute auction mechanisms specifically designed to support the e-procurement processes. Moreover, differently from other cases proposed in literature, the suppliers’ proposal formulation is strongly influenced by their production plans. A simulative environment has been developed in order to evaluate different performances: the customer and suppliers’ utilities, the profit distribution among the involved agents and the time necessary to reach an agreement. The mentioned approaches are compared with a negotiation process. The simulation results highlight the weakness and strength points of each auction protocol and why they can be considered as a relevant tool in B2B environments.

In recent years, manufacturing companies have entered a new era in which all manufacturing enterprises must compete in a global economy. To stay competitive, companies must use production systems that only produce their goods with high productivity, but also allow rapid response to market changes and customers’ needs. The emerging new paradigm of inter-firm relations involving both cooperative and competitive elements, called co-opetition, seems well face this issue. The chapter proposes a multi agent architecture to support different coordination policy in an electronic co-opetitive network in which plants are willing to exchange productive capacity. An innovative approach based on cooperative game theory is proposed in this research and its performance is compared with the prevalent negotiation approach. A discrete event simulation environment has been developed in order to evaluate the related performances. The case in which no relation exists among plants has been considered as a benchmark. The obtained results show that the proposed approach outperforms the negotiation mechanism form many point of view.

E-business applications require solid business models to support the manufacturing enterprises involved and gain real benefits from these applications. E-marketplaces profitability can be increased by an integration of multi-agent paradigm, production planning, negotiation and coalition support tools. This chapter introduces the concept of e-marketplaces and the adoption in Business-to-Business environment. Then, the state of the scientific literature is discussed for the several tools proposed in the literature. The chapter discusses the needs of innovative tools to support the e-marketplaces in order to increase the added value by the e-marketplaces. Finally, the motivations are discussed and the organization of the book is described.

This chapter presents the Agent-Based Architecture developed to support the “added value tools” in a neutral linear e-marketplace. The architecture will be described from three points of view. The first prospective is concerning the interaction between the e-marketplace and its external actors, by using case diagram formalism. The second description is about the functional context of the market described by the use of IDEF0 formalism. Finally, the last prospective, by the use of the UML activity diagrams, shows the dynamic of the MAS interactions. The design activities of the multi-agent architecture presented in this chapter allow to support the development of the simulation environment and the real application of the e-marketplace.

The emergence of institutional or informal networks, formed by cluster groups appears to be a major new feature of the contemporary industrial economy. The focus of this chapter is the development of a Multi Agent Architecture to support a network of enterprises that collaborate in a co-opetition relationships environment. The research concerns the investigation of a life cycle of the network in which the partners change dynamically. In particular, the enterprises that participate in the network can exit or continue to participate, while the enterprises that operate outside the network can evaluate to participate in the network. A simulation environment is developed to implement and test the proposed Multi Agent Architecture. The simulation tool allows to evaluate the proposed approach in a co-opetitive network during the operational activities. The simulation results show that the proposed approach is a very promising tool to support the plant’s participation decision.

International audience; Job shop scheduling problem is a NP-hard problem, therefore the objective is to create a schedule that satisfies all the constraints while taking as little overall time as possible. The paper concerns the job shop scheduling problem in cellular manufacturing systems; the schedule is created by a pheromone based approach. The proposed approach is carried out by a Multi Agent Architecture and it is compared with a coordination approach proposed in literature used as a benchmark. A simulation environment developed in ARENA® package was used to implement the approaches and evaluate the performance measures. The performance measures investigated are: throughput time, throughput, Work In Process, machines average utilization and tardiness. Several scenarios are considered: from static to very dynamic conditions for internal and external exceptions of the manufacturing system. The simulation results highlighted that the performance of the proposed approach are comparable with the benchmark when the customer demand has a high fluctuation and the manufacturing system is less dynamic.

The research proposed in this paper concerns three main issues of the network: architecture, coordination mechanism and the local plant decisions. The first issue is the development of a multi-agent architecture able to model the decentralised structure of the production networks. The second issue regards the coordination strategies to manage the relations among the independent manufacturing plants at the global level. The latter issue concerns the strategies used by the plant to formulate the proposal at the local level. The main objective of the research is to provide some useful information to make decisions about the choice of coordination approaches to be used in distributed production planning problems at the global and local level. The proposed approaches have been developed and tested by using a proper simulation environment. The experiments have been conducted under different conditions of workload and market price fluctuations; the results of the simulations provide the information necessary to select the suitable coordination and protocol mechanism in a distributed production-planning problem. [Received 24 November 2013; Revised 17 March 2014; Accepted 12 April 2014]

In order to test the functionality of the proposed agent based distributed architecture for manufacturing e-marketplaces, a proper simulation environment has been developed. Such an environment has been used to test the functionality of all the features discussed in the previous chapters of this book, but also to understand the real value that some added value services, such as re-planning and negotiation, can bring to the participants in a virtual district. This chapter discusses the above issues; in particular, the first part is dedicated to show all of the implementation phases; in the second part of the chapter a numerical example able to show the full functionality of all the applications, described in the previous chapters, will be analyzed; finally, in the third part, a set of experimental results will be discussed in order to understand the economic value of real added services in manufacturing e-marketplaces.