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A reliability/availability approach to joint production and maintenance scheduling with multiple preventive maintenance services

机译:采用联合预防性维护服务的联合生产和维护计划的可靠性/可用性方法

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摘要

In classical scheduling problems, it is often assumed that the machines are available during the whole planning horizon, while in realistic environments, machines need to be maintained and therefore may become unavailable within production periods. Hence, in this paper we suggest a joint production and maintenance scheduling (JPMS) with multiple preventive maintenance services, in which the reliability/availability approach is employed to model the maintenance aspects of a problem. To cope with the suggested JPMS, a mixed integer nonlinear programming model is developed and then a population-based variable neighbourhood search (PVNS) algorithm is devised for a solution method. In order to enhance the search diversification of basic variable neighbourhood search (VNS), our PVNS uses an epitome-based mechanism in each iteration to transform a group of initial individuals into a new solution, and then multiple trial solutions are generated in the shaking stage for a given solution. At the end of the local search stage, the best obtained solution by all of the trial solutions is recorded and the worst solution in population is replaced with this new solution. The evolution procedure is continued until a predefined number of iterations is violated. To validate the effectiveness and robustness of PVNS, an extensive computational study is implemented and the simulation results reveal that our PVNS performs better than traditional algorithms, especially in large size problems.
机译:在经典的调度问题中,通常会假定机器在整个计划范围内都是可用的,而在现实环境中,机器需要维护,因此在生产周期内可能变得不可用。因此,在本文中,我们建议采用具有多个预防性维护服务的联合生产和维护计划(JPMS),其中采用可靠性/可用性方法来对问题的维护方面进行建模。为了解决建议的JPMS,开发了混合整数非线性规划模型,然后设计了基于种群的变量邻域搜索(PVNS)算法作为求解方法。为了增强基本变量邻域搜索(VNS)的搜索多样性,我们的PVNS在每次迭代中都采用了基于缩影的机制,将一组初始个体转化为新的解决方案,然后在摇动阶段生成了多个试验解决方案对于给定的解决方案。在本地搜索阶段结束时,将记录所有试验解决方案中获得的最佳解决方案,并将人口中最差的解决方案替换为该新解决方案。继续演进过程,直到违反了预定数量的迭代为止。为了验证PVNS的有效性和鲁棒性,我们进行了广泛的计算研究,并且仿真结果表明,我们的PVNS的性能优于传统算法,尤其是在大型问题上。

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