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Extending capacity planning by positive lead times and optional overtime, earliness and tardiness for effective master production scheduling

机译:通过提前交货时间和可选的加班时间,提早和拖延来扩展产能计划,以进行有效的主生产计划

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In this paper we consider the problem of improving the Master Production Schedule (MPS) in make-to-order production systems when demand exceeds available resource capacity. Due to the complexity of the problem, in practice solutions are usually obtained manually. We propose an algorithm that offsets production orders guided by tardiness, earliness and overtime penalties. The intermediate tool used to determine resource utilization is Rough Cut Capacity Planning (RCCP) extended by positive lead times and options for overtime, earliness and tardiness. This approach leads to a more realistic resource loading calculation, similar to CRP but without its computational burden. The discrete optimization model is solved by a Genetic Algorithm (GA); within the GA, delays or early deliveries of each order are represented as genes of a chromosome. The method is tested against systematically developed benchmark problems and real industrial data. Improvements over the traditional RCCP procedure and an ERP's embedded routines are demonstrated by the computational results and support the applicability of the proposed approach for real-life make-to-order environments.
机译:在本文中,我们考虑了当需求超过可用资源容量时,按订单生产系统中改进主生产计划(MPS)的问题。由于问题的复杂性,实际上,解决方案通常是手动获得的。我们提出了一种算法,该算法可以根据延误,提前和加班处罚来抵消生产订单。用于确定资源利用率的中间工具是粗加工能力计划(RCCP),扩展了积极的交货时间,并提供了加班,提早和延误的选择。这种方法导致了更现实的资源负载计算,类似于CRP,但没有计算负担。离散优化模型通过遗传算法(GA)求解。在GA中,每个订单的延误或提早交付均表示为染色体的基因。该方法针对系统开发的基准问题和实际工业数据进行了测试。计算结果证明了对传统RCCP程序和ERP嵌入式例程的改进,并支持了所提出的方法在现实的按订单生产环境中的适用性。

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