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

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

SUBMITTER: Gonzalez M 

PROVIDER: S-EPMC8802783 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature