1,720,990 research outputs found
A fuzzy technique for supply chain network design with quantity discounts
No full textThis paper proposes a hierarchical technique for Supply Chain Network (SCN) efficiency maximisation under uncertainty composed of three steps. The first step extends a previous fuzzy cross-efficiency Data Envelopment Analysis approach, originally intended for suppliers’ selection, in order to evaluate and rank all the actors in each SCN stage under conflicting nondeterministic criteria. Afterwards, a fuzzy linear integer programming model is stated and solved for each pair of subsequent SCN stages to determine the quantities required from each stakeholder to maximise the overall SCN efficiency while satisfying the estimated demand and respecting the nodes capacity. Finally, a heuristics is applied to limit the exchange of small quantities in the SCN, in which the trade is not economically convenient according to quantity discounts. An illustrative example from the literature shows the technique effectiveness
A Game-theoretical Design Technique for Multi-stage Supply Chains under Uncertainty
No full textWe 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
A cross efficiency fuzzy Data Envelopment Analysis technique for supplier evaluation under uncertainty
No full textWe present a novel cross efficiency fuzzy Data Analysis (DEA) technique for supplier selection under uncertainty. In order to deal with uncertain input and output suppliers data, triangular fuzzy numbers are employed. A fuzzy triangular efficiency is associated to each supplier through a cross evaluation by a compromise between objectives. The results are defuzzified and a supplier ranking is determined. The method is applied to the evaluation of a set of candidate suppliers of an Italian SME, showing the ease of application and discriminative power among suppliers
Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario
No full textThe COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity.Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions.The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion
Integrated Network Design of Agile Resource-Efficient Supply Chains Under Uncertainty
No full textWe present a novel method for supply chain network (SCN) design under uncertainty that jointly solves the candidate selection, the order allocation, and the transportation mode selection problems. In the proposed method, four steps are executed in cascade. First, a cross-efficiency fuzzy data envelopment analysis technique ranks the candidates of each SCN stage in a multiobjective perspective and under uncertain data. Second, a fuzzy linear integer programming model determines the supplies required from each actor by those belonging to the subsequent SCN stage. This step determines the best compromise between candidates' efficiencies, estimated costs, and delivery time, considering stock levels and uncertain capacity of actors, while satisfying customers' uncertain demand. The third step evaluates the efficiency of the transportation alternatives under uncertain data to optimally plan the transport chain. Finally, the fourth step measures the performance of the designed SCN. The method provides as a result an integrated, agile, and resource-efficient design of the SCN under uncertainty. Its application to a case study shows it is effective in selecting the SCN partners, assigning the corresponding order quantities, and delivering them to customers. Validation is obtained by comparison with well-known approaches and statistical analysis
An improved technique for train load planning at intermodal rail-road terminals
No full textThis paper presents a train load planning technique for intermodal rail-road terminals. The proposed method aims at maximizing the train commercial value while respecting priority, physical, financial, and prosecution constraints (i.e., taking into account containers that prosecute their trip after the first destination). The approach consists of two phases: 1) modifying a previous approach by some of the authors, a linear integer programming problem is solved to maximize the train commercial value, keeping into account urgencies and priorities; 2) hence, a heuristics is used to take into account prosecuting containers and reduce the number of wagons to be re-handled. The technique is tested on a real case study and compared with the previous strategy proposed by some of the authors to show its effectiveness and ease of application
Integrated supplier selection and order allocation under uncertainty in agile supply chains
No full textThis paper focuses on the supplier selection problem and the subsequent order allocation, extending an approach originally proposed by some of the authors for supplier ranking under uncertainty. The novel method integrates the cross-efficiency Data Envelopment Analysis and the fuzzy set theory to obtain a ranking of suppliers under nondeterministic evaluation criteria. Subsequently, a fuzzy integer linear programming model allows determining the quantities to require from each supplier as a compromise between the suppliers' efficiency, procurement costs, and time required to fulfill the order, while respecting the suppliers' capacity and satisfying the customers' demand. The case study of an SME manufacturer shows the technique effectiveness
An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem
No full textThe slime mould algorithm (SMA) has recently been introduced to solve continuous engineering problems, which has been employed to solve a wide range of various problems due to its good performance. This paper presents an enhanced binary SMA for solving the 0–1 knapsack problem at different scales. In the presented binary SMA, eight different transfer functions have been used and evaluated. The transfer function, which has performed better than others, has been proposed for the subsequent experiments. The Bitwise and Gaussian mutation operators are used to enhance the performance of the proposed binary SMA. Furthermore, a penalty function and a repair algorithm are used to handle infeasible solutions. The proposed method’s performance was evaluated statistically on 63 standard datasets with different scales. The obtained results from the proposed method were compared with ten state-of-the-art methods. The results indicated the superiority of the proposed methods
Intermodal terminal planning by Petri Nets and Data Envelopment Analysis
No full textA procedure for planning and resourcesâ 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
A Timed Petri Nets Model for Intermodal Freight Transport Terminals
No full textThis paper presents a general modelling framework for intermodal freight transport terminals. The model allows evaluating the operational performance of such transportation systems, assessing the efficiency level of the terminal and identifying its bottlenecks by suitable performance indices. Moreover, it allows evaluating different solutions to the identified criticalities. The proposed framework is modular and based on timed Petri nets: 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. A simulation of a case study shows the model effectiveness
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