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A hybrid local search algorithm for scheduling real-world job shops with batch-wise pending due dates

机译：一种混合的本地搜索算法，用于按批处理的待定到期日调度实际的车间

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摘要

This paper aims at solving a real-world job shop scheduling problem with two characteristics, i.e., the existence of pending due dates and job batches. Due date quotation is an important decision process for contemporary companies that adopt the MTO (make to order) strategy. Although the assignment of due dates is usually performed separately with production scheduling, there exist strong interactions between the two tasks. Therefore, we integrate these two decisions into one optimization model. Meanwhile, each order placed by the customer defines a batch of jobs, for which the same due date should be set. Thus, the completion times of these jobs should be close to one another in order to reduce waiting time and cost. For this purpose, we propose a dispatching rule to synchronize their manufacturing progresses. A two-stage local search algorithm based on the PMBGA (probabilistic model-building genetic algorithm) and parameter perturbation is proposed to solve the integrated scheduling problem and its superiority is revealed by the applications to a real-world mechanical factory.
机译：本文旨在解决具有两个特征的现实世界中的作业车间调度问题，即存在待定的到期日和作业批次。对于采用MTO（按订单生产）策略的当代公司，到期日报价是重要的决策过程。尽管通常将到期日的分配与生产计划分开执行，但是两个任务之间存在很强的交互作用。因此，我们将这两个决策整合到一个优化模型中。同时，客户下的每个订单都定义了一批作业，应为其设置相同的截止日期。因此，这些作业的完成时间应彼此接近，以减少等待时间和成本。为此，我们提出了一个调度规则以同步其制造进度。提出了一种基于PMBGA（概率模型建立遗传算法）和参数摄动的两阶段局部搜索算法，解决了集成调度问题，并通过在实际机械厂中的应用展示了其优越性。

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