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

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  • Faycal A. Touzout
  • Lyes Benyoucef

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.

Suggested Citation

  • Faycal A. Touzout & Lyes Benyoucef, 2019. "Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2531-2547, April.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:8:p:2531-2547
    DOI: 10.1080/00207543.2018.1522006
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    Cited by:

    1. Andrzej Bożek, 2020. "Energy Cost-Efficient Task Positioning in Manufacturing Systems," Energies, MDPI, vol. 13(19), pages 1-21, September.
    2. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.

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