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Assembly lines with fractional tasks increase the throughput with a better workload balancing among the stations. The possibility to share tasks between two consecutive stations can also support the design of assembly lines with redundancies of the tasks. The redundancy of the tasks allows reacting to short failures or variations in processing time. This paper proposes a framework to support the design and control of redundancy assembly lines. The first problem concerns a modified mixed-integer linear programming model used for the fractional allocation assembly line problem to design redundancy assembly lines. Then, this research proposes control policies to manage the allocation of the tasks shared between two consecutive stations. Finally, the simulation evaluates the performance of the proposed methods to handle short failures and uncertain processing times. The numerical results show the potential improvements of the proposed methods with a limited number of redundancies. The main improvements concern the reduction of throughput time and work in process.

In many production applications, plants that produce multiple products with random demands share the required items among suppliers. The decision of how to allocate requests between suppliers to achieve the desired level of customer service is relevant to the efficiency of the production network. The literature highlighted how the long chain has the same level of performance as the full flexible network. This research proposes a decision model based on the game theory model to improve the performance of the production network. The model uses the Gale-Shapley algorithm with low computational complexity to share the demand among the suppliers. A simulation environment allows the evaluation of the proposed model in different conditions, and the model is compared to the dedicated, full flexibility, and long chain models. The numerical results show how the proposed model improves the efficiency of the production environment by keeping the number of connections with the supplier closer to the long chain model.

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.

Workload Control (WLC) seeks to align capacity with demand, where capacity is typically assumed to be restricted by a single constraint – machine capacity. In practice however, shops are often restricted by dual resource constraints: labor and machines. This study therefore uses simulation to investigate the performance of WLC in Dual Resource Constrained (DRC) highvariety shops with fully interchangeable labor. By considering several environmental factors and different labor assignment and dispatching rules, it is demonstrated that the order release function of WLC maintains its positive impact on performance in a DRC shop under different staffing levels. The positive effect of considering labor availability at release, as proposed in previous research, could not however be confirmed. Thus, the original release method can be applied if labor is fully interchangeable. In terms of labor assignment, we show that a distinct assignment pattern that differs between upstream and downstream stations improves performance if the routing is directed. Meanwhile, dispatching plays a less important role but creates important interaction effects with the assignment rule. Finally, the results suggest that increasing the service rate is a better response to the reduction in capacity that results from labor absenteeism than lowering the input frequency of work

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.

Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. “when” and “where” rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a ‘couple’ made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to “when” and “where” rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined.

The classical models for designing production lines follow the objective of balancing the line so as to improve the throughput [...]

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.

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 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.

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.

The proposed research concerns the capacity investment decisions in a co-opetitive network. The proposed model takes into account a certain degree of information sharing: specifically, the standard real option methodology is combined with a fuzzy engine. The fuzzy engine collects the information of the network in order to support the capacity investment decision. In particular, the environment concerns the capacity expansion issue for independent plants operating in a co-opetitive network. The proposed approach is compared with a case characterized by no information sharing among the plants. An opportune simulation environment (based on the JAVA package) has been developed in order to test the proposed approach in several environmental conditions. The simulation results highlight the benefits of the proposed approach: the strong reduction of the capacity investment value and a more specific identification regarding the market conditions in which this goal is relevant.

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.

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.

This study faces the problem of how to evaluate order allocation strategies in a multiple suppliers-manufacturers environment within a dynamic industry cluster that is an environment in which suppliers adopt a policy based on their performance to select the clusters in which to operate. Therefore, the allocation strategy performed by the manufacturers is crucial to keep a long-term supply chain partnership with a specific industry cluster. We use simulation to evaluate order allocation strategies under different market conditions. We show how an allocation strategy that takes into account the whole supply chain’s perspective leads to a sustainable development of the clusters of suppliers.

International audience; Cellular manufacturing systems are used when both production volume and product variety are at medium level. The fluctuations of volume and mix can reduce drastically the performance of classical cellular manufacturing systems. Therefore, several configurations have been proposed in literature as virtual manufacturing cells, fractal cells and remainder cells. This paper investigates the cell loading approaches in a manufacturing system composed of dedicated cells and a remainder cell. The remainder cell consists of machines able to manufacture all part families. The loading decision concerns the allocation of the parts to the remainder cell, instead of the dedicated cell. A simulation environment based on Rockwell ARENA® has been developed to test the proposed approaches. The performance measures are evaluated in a very dynamic environment characterized by volume oscillations, mix fluctuations and machine failures. A classical cellular manufacturing system is used as a benchmark for the performance measures analyzed. The simulation results show that the proposed policies lead to better performance when market fluctuations occur.

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.

Today’s turbulent markets are facing unpredictable and sudden variations in demand. In this context, the Holonic Production System (HPS) seems to be able to overcome the operational and economic problems of traditional production systems. The HPS’ ability to adapt and react to business environment changes, whilst maintaining systemic synergies and coordination, leverage on its network organizational structure, assuring both flexibility and profitability. In this paper we study HPS experimentally, modeling holon-firms as agents. In our simulation, holon-firms interact both with each other and with the external environment without predetermined hierarchies and following their own aims and internal decision rules with a negotiation-based control system. The Multi Agent System Approach we propose aims to evaluate and test the performance of the HPS to adjust to changes in market demand by simulating variations in holon-firms’ capacity and reconfiguration costs in real time in a distributed enterprise network. Hence we demonstrate that, through a collaborative negotiation approach, the HPS results in a better adaptability and improved network responsiveness.

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]