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 effificient 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 fifiltering mechanism that can be executed even on accessible personal computers and offering a short and reasonable response time. The introduction of a fifilter as a fifirst 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 fifind Pareto solutions. To prove the effectiveness of the proposed approach for problems with large dimensionality, several numerical examples with different dimensions will be examined.

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

“…Several solutions have been proposed to cope with the increase in data flow and the large number of alternatives to be processed. In [11][12][13], screening techniques were proposed to eliminate, from the beginning of the decision-making process, the alternatives deemed irrelevant. Screening makes it possible to reduce the size of the decision matrix, but in the case of a ranking process, the filtered alternatives will not appear in the final list, which contains the sorted alternatives.…”