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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

Karimi, B ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ress.2018.01.018
  3. Publisher: Elsevier Ltd , 2018
  4. Abstract:
  5. 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. © 2018 Elsevier Ltd
  6. Keywords:
  7. AHP-TOPSIS ; Automated guided vehicle ; Flexible manufacturing systems ; Nonlinear optimization ; Automatic guided vehicles ; Automation ; Heuristic algorithms ; Hierarchical systems ; Job shop scheduling ; Machinery ; Manufacture ; Mobile robots ; Nonlinear programming ; Reliability ; Vehicles ; Weibull distribution ; Automated guided vehicles ; MOTLBO ; Non-linear optimization ; NSCS ; Optimization
  8. Source: Reliability Engineering and System Safety ; Volume 175 , July , 2018 , Pages 92-104 ; 09518320 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0951832017308335