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A Multifactory Integrated Production and Distribution Scheduling Problem with Parallel Machines and Immediate Shipments Solved by Improved Whale Optimization Algorithm

机译:通过改进的鲸瓦优化算法解决了并联机器和立即出货量的多因素综合生产和分配调度问题

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摘要

This paper discusses the integrated scheduling of production and distribution operations in a multifactory supply chain with maketo- order production system. For the production side of the supply chain, we considered distributed parallel-established factories with identical parallel machines available at each factory. We assumed that the factories could produce all customers' orders with different production rates and costs. For the distribution side of the supply chain, we considered a limited number of homogeneous vehicles that immediately distribute the finalized orders to the customers. Then, a mixed-integer nonlinear programming model is developed to determine the detailed scheduling of production and distribution that minimizes the total costs of the supply chain including production, distribution, and late delivery costs. To solve the real-world scale problems, we developed a new whale optimization algorithm (WOA). Moreover, we conducted computational experiments by generating several test problems to evaluate the proposed algorithm. Statistical analysis showed that the proposed algorithm has better performance than traditional WOA for different scales of the problem. Moreover, it confirms the capability of the improved whale optimization algorithm (IWOA) to solve the medium-scale instances; however, the results indicate the better performance of genetic algorithm (GA) for the large-scale instances.
机译:本文讨论了使用Maketo-Order生产系统的多因素供应链中生产和分配操作的综合调度。对于供应链的生产方面,我们考虑了各自工厂可提供的具有相同平行机的分布式并行建立的工厂。我们认为,工厂可以生产不同的生产率和成本的所有客户的订单。对于供应链的分配侧,我们认为有限数量的均匀车辆,立即将最终订单分发给客户。然后,开发了一种混合整数非线性编程模型,以确定生产和分配的详细调度,从而最大限度地降低供应链的总成本,包括生产,分配和延迟交付成本。为了解决真实世界的规模问题,我们开发了一种新的鲸鱼优化算法(WOA)。此外,我们通过产生几个测试问题来进行计算实验以评估所提出的算法。统计分析表明,该算法比传统的WOA具有更好的性能,用于不同的问题的不同尺度。此外,它证实了改进的鲸料优化算法(IWOA)来解决中型实例的能力;然而,结果表明遗传算法(GA)对于大型实例的性能更好。

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