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Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods

Author

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  • Mehrdad Mohammadi
  • Ali Siadat
  • Jean-Yves Dantan
  • Reza Tavakkoli-Moghaddam

Abstract

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is maladjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in maladjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.

Suggested Citation

  • Mehrdad Mohammadi & Ali Siadat & Jean-Yves Dantan & Reza Tavakkoli-Moghaddam, 2015. "Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods," International Journal of Production Research, Taylor & Francis Journals, vol. 53(7), pages 2202-2224, April.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:7:p:2202-2224
    DOI: 10.1080/00207543.2014.980460
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    Citations

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    Cited by:

    1. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pirayesh, Amir & Karimi-Mamaghan, Amir Mohammad & Irani, Hassan, 2020. "Hub-and-spoke network design under congestion: A learning based metaheuristic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    2. 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.
    3. Muhammad Babar Ramzan & Shehreyar Mohsin Qureshi & Sonia Irshad Mari & Muhammad Saad Memon & Mandeep Mittal & Muhammad Imran & Muhammad Waqas Iqbal, 2019. "Effect of Time-Varying Factors on Optimal Combination of Quality Inspectors for Offline Inspection Station," Mathematics, MDPI, vol. 7(1), pages 1-18, January.
    4. Zhalechian, M. & Tavakkoli-Moghaddam, R. & Zahiri, B. & Mohammadi, M., 2016. "Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 182-214.
    5. Marc Janschekowitz & Gita Taherkhani & Sibel A. Alumur & Stefan Nickel, 2023. "An alternative approach to address uncertainty in hub location," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 359-393, June.

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