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

Abstract : 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.
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Submitted on : Sunday, March 17, 2019 - 2:13:32 PM
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Faycal Touzout, Lyes Benyoucef. Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches. International Journal of Production Research, Taylor & Francis, 2018, pp.1-17. ⟨10.1080/00207543.2018.1522006⟩. ⟨hal-02070291⟩

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