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A bi-objective robust inspection planning model in a multi-stage serial production system

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  • M. Mohammadi
  • J.-Y. Dantan
  • A. Siadat
  • R. Tavakkoli-Moghaddam

Abstract

In this paper, we present a bi-objective mixed-integer linear programming (BOMILP) model for planning an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system. The model involves two inter-related decisions: (1) which quality characteristics need what kind of inspections (i.e. which-what decision) and (2) when the inspection of these characteristics should be performed (i.e. when decision). These decisions require a trade-off between the cost of manufacturing (i.e. production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e. production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France.

Suggested Citation

  • M. Mohammadi & J.-Y. Dantan & A. Siadat & R. Tavakkoli-Moghaddam, 2018. "A bi-objective robust inspection planning model in a multi-stage serial production system," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1432-1457, February.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:4:p:1432-1457
    DOI: 10.1080/00207543.2017.1363425
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    Cited by:

    1. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Jula, Payman & Pirayesh, Amir & Ahmadi, Hadi, 2020. "A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty," European Journal of Operational Research, Elsevier, vol. 285(2), pages 513-537.

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