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Production scheduling for continuous manufacturing systems with quality constraints

机译:具有质量限制的连续制造系统的生产计划

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This research is motivated by a real world production scheduling problem in a continuous manufacturing system involving multiple objectives, multiple products and multiple processing lines with various inventory, production and quality constraints. Because of the conflicting objectives, a global optimization approach is considered as not feasible by the plant management. Given a customer demand forecast, two practical heuristic or sequential optimization algorithms are developed to generate daily production schedules for two primary objectives: minimize shipment delays (pull-backward procedure) and minimize average inventory levels (push-forward procedure). A third heuristic algorithm (reduce switch-over procedure) which is based on the current management practice is also developed to serve as a benchmark. A factorial experiment was performed to evaluate the performance of the heuristic procedures and to identify factors that might affect the performance differences among heuristics. Since each heuristic is designed to give priority to one of the three conflicting objectives, none of them is absolutely superior to the other algorithms in all aspects. However, the first two heuristic procedures performed better than the current management practice in shipment delays and average daily inventory. The production schedules generated by the two procedures also satisfy the quality constraints. The experimental results also showed that the performance of the algorithms is significantly affected by product mix, inventory levels, and demand pattern.
机译:这项研究是基于一个连续生产系统中的实际生产调度问题而进行的,该生产系统涉及多个目标,多个产品和具有不同库存,生产和质量限制的多个生产线。由于目标相互矛盾,因此工厂管理人员认为全局优化方法不可行。给定客户需求预测,开发了两种实用的启发式或顺序优化算法,以针对两个主要目标生成每日生产计划:最小化装运延迟(向后拉过程)和最小化平均库存水平(向后推过程)。还开发了基于当前管理实践的第三种启发式算法(减少切换过程)作为基准。进行了析因实验以评估启发式程序的性能,并确定可能影响启发式方法之间性能差异的因素。由于每种启发式方法都旨在优先考虑这三个相互冲突的目标之一,因此在所有方面,它们都不绝对优于其他算法。但是,前两个启发式程序在装运延迟和平均每日库存方面比当前的管理实践要好。这两个过程生成的生产进度表也满足了质量约束。实验结果还表明,算法的性能受产品组合,库存水平和需求模式的显着影响。

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