MCDM Filter with Pareto Parallel Implementation in Shared Memory Environment

Abstract

Nowadays, multi-criteria decision-making (MCDM) methods are often used to solve problems involving large data sets, especially with the advent of the big data age. In such a context, the multi-criteria decision-making methods theoretically can be used but technically are not efficient in terms of the treatment time. Indeed, the majority of commercial or even experimental multi-criteria decision support tools always have limits in terms of the number of alternatives and the number of criteria to be retained in the decision-making process, which presents a computational challenge to relieve. This present paper discusses the application of parallel computation to meet this challenge and make the application of MCDM methods possible in the presence of a big number of alternatives and criteria. More precisely, the main objective of this work is to provide a parallel filtering mechanism that can be executed even on accessible personal computers and offering a short and reasonable response time. The introduction of a filter as a first step in the decision-making process consists in retaining, as alternatives to be treated by the MCDM method, and by parallel processing only the Pareto solutions. To achieve this objective, we propose a parallel computing approach deploying the Open MP (Open Multi-Processing) paradigm on a shared memory environment to find Pareto solutions. To prove the effectiveness of the proposed approach for problems with large dimensionality, several numerical examples with different dimensions will be examined