A bi-objective Optimization Model for a Dynamic Cell Formation Integrated with Machine and Cell Layouts in a Fuzzy Environment. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Golmohammadi, Amir-Mohammad; Honarvar, Mahnoobeh; Hosseini-Nasab, Hassan; Tavakkoli-Moghaddam, Reza
• Is Part Of: Fuzzy information and engineering. Volume 12:Issue 2(2020)
• Page Start: 204
• Page End: 222
• Publication Date: 2020-04-02
• Subjects: Cellular manufacturing system -- cell formation -- inter/intra-cell layout -- Pareto-based algorithms -- hybrid meta-heuristic algorithm; Fuzzy systems -- Periodicals; Intelligent control systems -- Periodicals; 629.89
• DOI: 10.1080/16168658.2020.1747162
• Languages: English
• ISSNs: 1616-8658

Abstract

Abstract : In this paper, a bi-objective optimization model is developed to integrate the cell formation and inter/intra-cell layouts in continuous space by considering fuzzy conditions to minimize the total cost of parts relocations as well as cells reconfigurations. The intra- and inter-cell movements for both parts and machines using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance in a fuzzy environment. To solve the proposed problem as a bi-objective mixed-integer non-linear programming model is NP-hard, four meta-heuristic algorithms based on a multi-objective optimization structure are tackled to address the problem. In this regard, not only Genetic Algorithm (GA), Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed to solve the problem, but also a novel hybrid meta-heuristic algorithm based on the benefits of aforementioned algorithms is developed. Finally, by considering some efficient assessment metrics of Pareto-based algorithms, the results indicate that the proposed hybrid algorithm not only is more appropriate than the exact solver but it also outperforms the performance of individual ones particularly in medium- and large-sized problems.