A hyper-matheuristic approach for solving mixed integer linear optimization models in the context of data envelopment analysis

Author:

Gonzalez Martin1,López-Espín Jose J.1ORCID,Aparicio Juan1,Talbi El-Ghazali2

Affiliation:

1. Center of Operations Research, Universidad Miguel Hernández de Elche, Elche, Spain

2. INRIA, Université des Sciences et Technologies de Lille (Lille I), Lille, France

Abstract

Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems in any context. In this paper, we focus our attention on MILPs in the specific framework of Data Envelopment Analysis (DEA), where the determination of a score of technical efficiency of a set of Decision Making Units (DMUs) is one of the main objectives. In particular, we propose a new hyper-matheuristic grounded on a MILP-based decomposition in which the optimization problem is divided into two hierarchical subproblems. The new approach decomposes the model into discrete and continuous variables, treating each subproblem through different optimization methods. In particular, metaheuristics are used for dealing with the discrete variables, whereas exact methods are used for the set of continuous variables. The metaheuristics use an indirect representation that encodes an incomplete solution for the problem, whereas the exact method is applied to decode the solution and generate a complete solution. The experimental results, based on simulated data in the context of Data Envelopment Analysis, show that the solutions obtained through the new approach outperform those found by solving the problem globally using a metaheuristic method. Finally, regarding the new hyper-matheuristic scheme, the best algorithm selection is found for a set of cooperative metaheuristics ans exact optimization algorithms.

Funder

Spanish Ministry of Science and Innovation and the State Research Agency

Spanish Ministry for Economy and Competitiveness

State Research Agency

European Regional Development Fund

Publisher

PeerJ

Subject

General Computer Science

Reference38 articles.

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