Akademik Veri Yönetim Sistemi
Computers and Industrial Engineering, cilt.208, 2025 (SCI-Expanded)
In this study, a metaheuristic-based optimization approach is proposed in order to enhance applicability and effectiveness of a classical Multiple Criteria Decision Making method (MCDM) that is known as permutation method. The permutation method offers several advantages, such as robustness against the rank-reversal phenomenon and the ability to bypass normalization and aggregation of alternative scores across criteria, enabling it to effectively manage diverse data types. These advantages stem from its ability to directly compare alternatives within a given criteria set. However, the standard permutation method requires evaluating all possible permutations, which is computationally intensive and does not consider the magnitude of differences between alternatives in satisfying the criteria. The proposed approach introduces a new technique for calculating the ranking values of permutations by considering the magnitude of differences between alternatives. It also employs a parallel Weighted Superposition Attraction (WSA) algorithm to efficiently search for permutations, addressing these difficulties and identifying the optimal permutation of alternatives in MCDM problems. The proposed approach is evaluated on a range of real-world case studies, including agile methods assessment, laptop computer selection and fuzzy personnel selection, as well as on large-scale randomly generated problem instances. To demonstrate its effectiveness and validity, the method is also benchmarked against several well-established MCDM techniques and widely recognized metaheuristic algorithms.