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

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

Reliability Engineering and System Safety, 2018, vol. 175, issue C, 92-104

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.

Keywords: Flexible manufacturing systems; Automated guided vehicle; Nonlinear optimization; NSCS; MOTLBO; AHP-TOPSIS (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:175:y:2018:i:c:p:92-104

DOI: 10.1016/j.ress.2018.01.018

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