Fabio Sciancalepore

A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of Decision Making Units under uncertainty

Abstract The paper presents a novel cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique for evaluating different elements (Decision Making Units or DMUs) under uncertainty. In order to evaluate the performance of several DMUs while dealing with uncertain input and output data, the presented technique employs triangular fuzzy numbers. A fuzzy triangular efficiency is associated to each DMU through a cross evaluation obtained by a compromise between suitably chosen objectives. Results are then defuzzified to provide a ranking of the DMUs. The proposed method is applied to the performance evaluation of healthcare systems in a region of Southern Italy. The DMU data uncertainty derives from ongoing reforms and the reported assessment is conducted firstly in order to evaluate and rank the efficiency of the considered healthcare systems, and subsequently to assess the evolution of the performance of one of the most affected among these DMUs by the reform plans. The case study demonstrates the model ease of application, its discriminative power among DMUs when compared to a more classical fuzzy DEA approach, and the usefulness in planning and validating targeted reforms in the case of healthcare systems.

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross-efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross-efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria.

A fuzzy programming approach for the strategic design of distribution networks

The paper addresses the optimal design of the last branch of the supply chain, i.e., the Distribution Network (DN). We extend a deterministic optimization model previously proposed by the authors, using fuzzy numbers and fuzzy logic to take into account uncertainty in the DN model and in the DN design. Hence, a procedure employing digraph modeling and fuzzy mixed integer linear programming is presented to select the optimal DN configuration. To show the method effectiveness, the optimization model is applied to a case study.

Strategic design and multi-objective optimisation of distribution networks based on genetic algorithms

The paper addresses the optimal design of distribution networks (DNs). Considering a distributed system composed of stages connected by material links labelled with suitable performance indices, a procedure employing multi-objective genetic algorithms (MOGAs) is presented to select the optimal DN configuration. The paper enhances a deterministic procedure for DN strategic configuration by employing MOGACOP, a real-valued chromosome MOGA that can be applied to the case of constrained nonlinear function. The main MOGA characteristics are the presence of three populations: two reference sets of individuals satisfying all constraints, namely, a set of Pareto optimal individuals (frontier population) and a set of individuals covering the previous population (archive population), together with a search set which, on the contrary, includes individuals that are allowed to not satisfy all constraints (laboratory population). MOGACOP allows solving the DN design nonlinear problem, which exhibits a multi-objective function that varies linearly only with some variables and nonlinearly with the remaining variables. The proposed MOGA application allows finding a Pareto frontier of optimal solutions, which is compared with the frontier obtained by solving the same problem with Integer Linear Programming (ILP), where piecewise constant contributions are linearly approximated. The two found curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimisation and the ILP models are applied under structural constraints to a case study describing the distribution chain of a large enterprise of southern Italy producing consumer goods.

A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market

Abstract This paper presents a simulation model based on the Nash equilibrium notion for the auction based day ahead electricity generation market. The presented model enhances a previous formalism proposed in the related literature by employing empirical data distributions of the market clearing price as registered by the market authority (e.g. the Independent System Operator). The model is effective when power suppliers with different generation capacities are considered, differently from the starting model that unrealistically assumes equal capacities. The proposed approach aims at evaluating the electricity market competitiveness with regard to the bidder strategies in order to prevent their anticompetitive actions. The framework is applied to a real data set regarding the Italian electricity market to enlighten its effectiveness in different scenarios, varying the number and capacity of participating bidders. The model can be employed as a basis for a decision support tool both for market participants (to define their optimal bidding strategy) and regulators (to avoid collusive strategies).

A periodic event scheduling approach for offline timetable optimization of regional railways

We address the train timetabling problem for regional rails by a cyclic scheduling approach. We revisit a mixed integer linear programming model for offline timetable optimization and enhance it using a discrete event formulation and taking into account single-track stations that typically characterize local rails. The model can be applied to regional railways that are increasingly gaining significance due to the social pressure for sustainable mobility. The approach is successfully applied to a large portion of a real Southern Italy railway network, obtaining a timetable that enhances the passengers service level.

A hierarchical optimization technique for the strategic design of distribution networks

The paper addresses the optimal design of the last supply chain branch, i.e., the Distribution Network (DN), starting from manufacturers till the retailers. It considers a distributed system composed of different stages connected by material links labeled with suitable performance indices. A hierarchical procedure employing direct graph (digraph) modeling, mixed integer linear programming, and the Analytic Hierarchy Process (AHP) is presented to select the optimal DN configuration. More in detail, a first-level DN optimization problem taking into account the definition and evaluation of the distribution chain performance provides a set of Pareto optimal solutions defined by digraph modeling. A second level DN optimization using the AHP method selects, on the basis of further criteria, the DN configuration from the Pareto face alternatives. To show the method effectiveness, the optimization model is applied to a case study describing an Italian regional healthcare drug DN. The problem solution by the proposed design method allows improving the DN flexibility and performance.

A novel fuzzy Data Envelopment Analysis methodology for performance evaluation in a two-stage supply chain

The paper presents a novel fuzzy Data Envelopment Analysis (DEA) technique for supplier selection under uncertainty. Uncertain input and output data characterizing suppliers are estimated through triangular fuzzy numbers. The resulting fuzzy triangular efficiency is determined by using only a set of weights, derived as a compromise between objectives. The obtained results are defuzzified and compared in order to rank suppliers. The presented method is applied for the evaluation of a set of candidate suppliers of an SME located in Southern Italy, showing the ease of application and the discriminative power between different suppliers under uncertain data.

A real time traffic management model for regional railway networks under disturbances

We address real time traffic management under disturbances of regional rails with a centralized traffic control system. We solve the rescheduling problem by revisiting a finite time horizon mixed integer linear programming model from the related literature. First, we adapt the framework to regional networks, in which stations are close and the network is mainly constituted by single tracks; second, to solve train conflicts that may occur in the rescheduled timetable after the chosen time horizon, we enhance the model by an iterative heuristic algorithm that solves such conflicts. The presented approach is applied to a real data set related to a large portion of a regional network in Southern Italy, showing its effectiveness in providing a physically realizable rescheduled solution in a very short computational time.

A model for the strategic design of Distribution Networks

The paper addresses the optimal design of the last branch of the supply chain, i.e., the Distribution Network (DN), starting from manufacturers till the retailers. Considering a distributed system composed of different stages connected by material links labeled with suitable performance indices, a procedure employing digraph modeling and mixed integer linear programming is presented to select the (sub)optimal DN configuration. The optimization model is applied under structural constraints to a case study describing the distribution chain of a large enterprise of southern Italy producing consumer goods. The problem solution provides different structures allowing the improvement of the DN flexibility and performance.