A bi-objective Optimization Model for a Dynamic Cell Formation Integrated with Machine and Cell Layouts in a Fuzzy Environment

• Published in: Fuzzy information and engineering Vol. 12; no. 2; pp. 204 - 222
• Main Authors: Golmohammadi, Amir-Mohammad, Honarvar, Mahnoobeh, Hosseini-Nasab, Hassan, Tavakkoli-Moghaddam, Reza
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.04.2020; Taylor & Francis Group
• Subjects: Algorithms; cell formation; Cellular manufacturing system; Fuzzy logic; Fuzzy sets; Genetic algorithms; Heuristic methods; hybrid meta-heuristic algorithm; inter/intra-cell layout; Layouts; Linear programming; Mixed integer; Multiple objective analysis; Nonlinear programming; Optimization; Optimization models; Pareto optimization; Pareto-based algorithms
• ISSN: 1616-8658; 1616-8666
• DOI: 10.1080/16168658.2020.1747162

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.