Marco Falagario

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 hierarchical model for optimal supplier selection in multiple sourcing contexts

This paper addresses a crucial objective of the strategic purchasing function in supply chains, i.e. optimal supplier selection. We present a hierarchical extension of the data envelopment analysis (DEA), the most widespread method for supplier rating in the literature, for application in a multiple sourcing strategy context. The proposed hierarchical technique is based on three levels. First, a modified DEA approach is used to evaluate the efficiency of each supplier according to some criteria proposed by the buyer. Second, the well known technique for order preference by similarities to ideal solution (TOPSIS) is applied to rank the maximally efficient suppliers given by the previous step. Third and finally, a linear programming problem is stated and solved to find the quantities to order from each maximally efficient supplier in the multiple sourcing context. The presented approach is able to straightforwardly discern between efficient and inefficient partners, avoid the confusion between efficient and effective suppliers and split the supply in a multiple sourcing context. The hierarchical model is applied to the supply of a C class component to show its robustness and effectiveness, while comparing it with the DEA and TOPSIS approaches.

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 Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals

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.

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 decision support system framework for purchasing management in supply chains

Purpose – This paper aims to propose the framework of a decision support system (DSS) to select the optimal number of suppliers that are candidate to join a supply chain network.Design/methodology/approach – The DSS bases the decision on the cost evaluation of the transaction among the buyer and the potentially available suppliers by way of a Monte Carlo approach. In particular, the presented DSS includes a statistical module and the DSS core. The former module estimates (in a probabilistic way) the exchange performance indices, i.e. total cost of the transaction, purchasing price and additional costs of purchasing, while the latter module implements the transaction evolution making use of a simulation model. The DSS is tested by way of a case study, namely the supply of a customized product by a general contractor in the construction industry.Findings – The obtained DSS results are validated with the actual data of the purchasing, and confirm the underlying model suitability and the DSS effectiveness for...

An integrated approach for warehouse analysis and optimization

The paper focuses on the analysis and optimization of production warehouses.We present an approach integrating several tools for implementing lean in the warehouse operation.The approach integrates the Unified Modeling Language, Value Stream Mapping and the Genba Shikumi methodology.An Italian interior design producer is employed as a case study.The application of the approach to the company leads to an innovative proposal for the warehouse automation and optimization. The paper focuses on the analysis and optimization of production warehouses, proposing a novel approach to reduce inefficiencies which employs three lean manufacturing tools in an integrated and iterative framework. The proposed approach integrates the Unified Modeling Language (UML) - providing a detailed description of the warehouse logistics - the Value Stream Mapping (VSM) tool - identifying non-value adding activities - and a mathematical formulation of the so-called Genba Shikumi philosophy - ranking such system anomalies and assessing how they affect the warehouse. The subsequent reapplication of the VSM produces a complete picture of the reengineered warehouse, and using the UML tool allows describing in detail the updated system. By applying the presented methodology to the warehouse of an Italian interior design producer, we show that it represents a useful tool to systematically and dynamically improve the warehouse management. Indeed, the application of the approach to the company leads to an innovative proposal for the warehouse analysis and optimization: a warehouse management system that leads to increased profitability and quality as well as to reduced errors.

A Decision Support System for Optimizing Operations at Intermodal Railroad Terminals

In this paper, we present a decision support tool to optimize two of the most critical activities in intermodal railroad container terminals, in an iterative and integrated framework devoted to the terminal profit improvement. First, the model allows optimizing the freight trains composition, maximizing the company profit, while respecting physical and economic constraints, and placing in the train head/tail containers prosecuting to subsequent destinations. Hence, based on the resulting train composition, the decision support system allows optimizing the containers allocation in the terminal storage yard, in order to maximize the filling level while respecting physical constraints. The model is successfully tested on a real case study, the inland railroad terminal of a leading Italian intermodal logistics company.

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).