A Parameterized Scheme of Metaheuristics to Solve NP-Hard Problems in Data Envelopment Analysis

Abstract

Data Envelopment Analysis (DEA) is a well-known methodology for estimating technical efficiency from a set of inputs and outputs of Decision Making Units (DMUs). This paper is devoted to computational aspects of DEA models when the determination of the least distance to the Pareto-efficient frontier is the goal. Commonly, these models have been addressed in the literature by applying unsatisfactory techniques, based essentially on combinatorial NP-hard problems. Recently, some heuristics have been introduced to solve these situations. This work improves on previous heuristics for the generation of valid solutions. More valid solutions are generated and with lower execution time. A parameterized scheme of metaheuristics is developed to improve the solutions obtained through heuristics. A hyper-heuristic is used over the parameterized scheme. The hyper-heuristic searches in a space of metaheuristics and generates metaheuristics that provide solutions close to the optimum. The method is competitive versus exact methods, and has a lower execution time.