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首页> 外文期刊>Reliability Engineering & System Safety >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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Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles

机译：使用自动引导车辆的操作机器的两种故障类型的车间的双目标优化

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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. (C) 2018 Elsevier Ltd. All rights reserved.
机译：柔性制造系统中机械和设备的可靠性是降低生产成本和提高效率的最重要问题。本文研究了车间生产系统中机械的可靠性，在该系统中，材料，零件和其他生产需求由自动导引车（AGV）处理。给定车间中并行机器的故障时间遵循指数分布或Weibull分布。由于在Weibull情况下没有闭合形式的方程来计算车间的可靠性，因此本文采用一种仿真方法来估计可靠性。然后，针对所研究的问题开发了一个双目标非线性优化模型，以同时最大化车间可靠性和最小化生产时间。为了评估所提出模型的有效性，生成了一些随机实例，在此基础上，设计了两种元启发式算法，分别称为非支配杜鹃布谷鸟搜索（NSCS）和多目标基于教学学习的优化（MOTLBO）。最后，为了评估和比较所提出的解决方案算法的有效性，使用了一种有效的解决方案AHP-TOPSIS技术。 （C）2018 Elsevier Ltd.保留所有权利。

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