A genetic algorithm to integrate a comprehensive dynamic cellular manufacturing system with aggregate planning decisions. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Kia, Reza
• Is Part Of: International journal of management science and engineering management. Volume 15:Issue 2(2020)
• Page Start: 138
• Page End: 154
• Publication Date: 2020-04-02
• Subjects: Dynamic cellular manufacturing system -- mixed-integer nonlinear programming -- aggregate planning -- genetic algorithm; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2019.1655674
• Languages: English
• ISSNs: 1750-9661

Abstract

ABSTRACT: In this paper, to design a Cellular Manufacturing System (CMS) under a dynamic situation and make Aggregate Planning (AP) decisions simultaneously, a mixed-integer nonlinear programming (MINLP) model is designed. The distinctive features of the comprehensive Dynamic CMS (DCMS) model under consideration are: i) an extensive coverage of significant manufacturing characteristics in designing a DCMS in addition to the main strategies of AP, ii) integration of cost elements addressing structural, operational and planning issues in the design of DCMS, and iii) capable of developing better DCMS design decisions by incorporating more detailed and realistic parameters when compared to the literature. An illustrative numerical example is solved by CPLEX 12.6 to demonstrate the achievements obtained by the integrated model. Since the proposed model belongs to NP-hard category, a Genetic Algorithm (GA) improved by an elaborately designed matrix-based chromosome representation to represent all decision variables, as well as a sequential procedure generating initial solutions is developed. Several test problems either generated randomly or taken from the literature with various sizes are solved and the results are compared with the solutions gained using CPLEX solver. The comparisons results show that the designed GA is capable of evolving optimal or near-optimal solutions with relative gap less than 1% in a computationally satisfactory manner. Abbreviation: DCMS: Dynamic CMS; AP: Aggregate Planning; GA: Genetic Algorithm; MINLP: Mixed-integer nonlinear programming