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This article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners’ selection and allocation of the received orders among them. The partners’ selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers’ demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented. Note to Practitioners —We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members’ selection and the orders’ allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers.

The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demand-oriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes.

We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers.

Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market's needs.

We present a design approach for multi-stage Supply Chains (SCs) that allows selecting candidates and assigning them orders under uncertainty. A bargaining game model in its extensive form (i.e., with a time sequencing of moves) and in a fuzzy setting is proposed. The product quantities that each actor requires from the previous SC stage are determined modelling the real behavior of SC stakeholders, which on the one hand act to maximize their own profit, on the other hand cooperate to maximize the overall efficiency of the SC and minimize production costs and lead times. Assignments are determined taking into account stock levels, uncertain production or warehouse capacities, and customers' demand. Thus, the method supports the decision making process providing an agile, cooperative, and resource-efficient design of multi-stage SCs under uncertain parameters. A literature SC is used as a test case to evaluate the effectiveness of the technique.

In this paper, we show how first-order hybrid Petri nets can be efficiently used to model and manage intermodal freight transport terminals. The proposed formalism enables the terminal decision maker to choose the speeds associated with continuous transitions in order to optimize the terminal performance by two alternative control policies: the container flows maximization and the minimization of the residual containers in the storage area. The approach may be used either offline, to take decisions on the terminal resources, or online, to solve congestions/malfunctions. A real case study is modeled and managed by the proposed optimal control policies.

This paper presents a decision support tool for the efficient resource planning and management of intermodal terminals under uncertainty, allowing to address the planning issue under imprecise or uncertain data (e.g., estimates on flows, resource utilization, operating conditions). The procedure consists of three steps: 1) the definition of a Timed Petri Net model of the terminal; 2) the computation of suitable performance indices to evaluate whether the current configuration is able to cope with a foreseen increase in the freight flows; 3) in the case of not satisfactory values of the indices at the previous step, the simulation of alternative planning solutions and the detection of the most efficient one via a cross efficiency fuzzy Data Envelopment Analysis technique. In order to test its effectiveness, the procedure is applied to a real case study. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This paper shows the practical relevance of first-order hybrid Petri nets in the re-design process of low-automated production systems. In particular, we analyze the case study of a bakery producing "pane Carasau", a typical Sardinian bread, whose traditional production plant currently has difficulties in coping with the constant increase in market demand. Through first-order hybrid Petri nets, the current functioning and the operating features and dynamics of the case study are modelled, waste sources and bottlenecks are detected, and alternative re-designed scenarios are implemented and evaluated to identify the most suitable reengineering actions to be developed. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

A procedure for planning and resources' management in intermodal terminals is presented. It integrates Timed Petri Nets (TPNs) and Data Envelopment Analysis (DEA) and consists of three steps: the terminal modeling via TPNs to model the regular behavior; the evaluation of whether the current configuration may cope with increased freight flows; if not, the analysis by cross-efficiency DEA of alternative planning solutions. The procedure provides the decision maker with number, capacity, and schedule of resources to tackle the flows increase. The method is evaluated by a real case study, showing that integrating TPNs and DEA allows taking planning decisions under conflicting requirements. (C) 2017 Elsevier Ltd. All rights reserved.

This paper presents a general modeling framework for Intermodal Freight Transport Terminals (IFTTs). The model allows simulating and evaluating the performance of such key elements of the intermodal transportation chain. Hence, it may be used by the decision maker to identify the IFTT bottlenecks, as well as to test different solutions to improve the IFTT dynamics. The proposed modeling framework is modular and based on timed Petri Nets (PNs), where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported. The model is able to represent the different types of existing IFTTs. Its effectiveness is tested first on an example from the literature and then on a real case study, the railroad inland terminal of a leading Italian intermodal logistics company, showing its ease of application. In the real case study, using the proposed formalism we test the as-is IFTT performance and evaluate alternative possible to-be improvements in order to identify and eliminate emerging criticalities in the terminal dynamics.

In this paper we show how timed Petri nets can be efficiently used to solve problems related to resource planning in intermodal freight transport terminals. In particular, the tackled issues regard the strategic planning of the number of facilities used to transfer the intermodal transport units and the capacity/frequency of the transportation means. A real case study is considered, namely a rail-road terminal located in southern Italy. Monte Carlo simulations based on the timed Petri net model of the terminal are carried out considering various scenarios, including both the regular behavior based on real data, and situations of potential congestion resulting from increase in the commercial flows.