Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

• Published in: International journal of production research Vol. 57; no. 8; pp. 2531 - 2547
• Main Authors: Touzout, Faycal A., Benyoucef, Lyes
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 18.04.2019; Taylor & Francis LLC
• Subjects: AMOSA; Classification; Completion time; Computer simulation; Evolutionary algorithms; Genetic algorithms; Greenhouse effect; Greenhouse gases; Integer programming; Iterative methods; Linear programming; Manufacturing; multi-objective integer linear programming; Multiple objective analysis; NSGA-II; process plan generation; Production costs; reconfigurable manufacturing system (RMS); Reconfiguration; Simulated annealing; Sorting algorithms; Sustainability
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2018.1522006

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.