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Pareto Ant Colony Optimization Based Algorithm to Solve Maintenance and Production Scheduling problem in Parallel Machine Case

机译:基于Pareto蚁群优化的并联机器案例中解决维护和生产调度问题的算法

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This article presents a new method based on multi- objective Pareto ant colony optimization to resolve the joint production and maintenance scheduling problem. This method is applied to the problem previously developed in [2] for the parallel machines case. This problem was formulated according to a bi- objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability Models were used to take into account the maintenance aspect in the model. Two genetic algorithms were compared to approximate the Pareto front. Here, we propose a new algorithm based on Pareto ant colony optimization to improve the solutions quality found in the previous study. The goal is to simultaneously determine the best assignment of production tasks to machines by minimizing the makespan as well as the best periods of preventive maintenance (PM) of the machines by minimizing the unavailability of the production system. The experiments carried out show an improvement of the previous results.
机译:本文提出了一种基于多目标帕累托蚁群优化的新方法,以解决联合生产和维护调度问题。该方法应用于以前在[2]中开发的问题的问题,用于并行机器案例。根据双目标方法制定了此问题,以便在生产和维护目标之间找到权衡解决方案。可靠性模型用于考虑模型中的维护方面。比较了两个遗传算法以近似帕累托前线。在这里,我们提出了一种基于帕累托蚁群优化的新算法,提高了先前研究中的解决方案质量。目标是通过最大限度地减少生产系统的不可用即可通过最大限度地降低Mapespan以及通过生产系统的不可用性来同时确定通过最佳预防性维护(PM)的最佳预防性维护(PM)来确定生产任务的最佳分配。进行的实验表明了先前结果的改善。

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