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Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles

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  • Karimi, Behzad
  • Niaki, S.T.A.
  • Haleh, Hassan
  • Naderi, Bahman

Abstract

Reliability of machinery and equipment in flexible manufacturing systems are among the most important issues to reduce production costs and to increase efficiency. This paper investigates the reliability of machinery in job shop production systems, where materials, parts, and other production needs are handled by automated guided vehicles (AGV). The failures time of the parallel machines in a given shop follow either an exponential or a Weibull distribution. As there is no closed-form equation to calculate the reliability of the shop in the Weibull case, a simulation approach is taken in this paper to estimate the reliability. Then, a bi-objective nonlinear optimization model is developed for the problem under investigation to maximize shop reliability as well as to minimize production time, simultaneously. In order to assess the efficacy of the proposed model, some random instances are generated, based on which two meta-heuristic algorithms called non-dominated sorting cuckoo search (NSCS) and multi-objective teaching–learning-based optimization (MOTLBO) are designed. Finally, to evaluate and compare the effectiveness of the proposed solution algorithms, an efficient solution AHP-TOPSIS technique is utilized.

Suggested Citation

  • Karimi, Behzad & Niaki, S.T.A. & Haleh, Hassan & Naderi, Bahman, 2018. "Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 92-104.
    • Handle: RePEc:eee:reensy:v:175:y:2018:i:c:p:92-104
    DOI: 10.1016/j.ress.2018.01.018
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    References listed on IDEAS

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    Cited by:

    1. Ruiying Li & Xiaoyu Tian & Li Yu & Rui Kang, 2019. "A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    2. Yubang Liu & Shouwen Ji & Zengrong Su & Dong Guo, 2019. "Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    3. Behzad Karimi & Seyed Taghi Akhavan Niaki & Amir Hossein Niknamfar & Mahsa Gareh Hassanlu, 2021. "Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm," Journal of Risk and Reliability, , vol. 235(2), pages 306-328, April.

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