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A dynamic scheduling approach for optimizing the material handling operations in a robotic cell

机译:一种动态调度方法,用于优化机器人单元中的物料搬运操作

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This paper investigates a real-time dynamic job-shop scheduling problem in a robotic cell, in which multiple jobs enter into the cell with unexpected arriving rates. Different from classical flow-shop and job-shop scheduling problems, the jobs' transportation handled by a robot must be considered. Another characteristic is that the jobs' processing times are not constant values but confined in time-window constraints. To efficiently solve this problem in real time, the original schedule is restricted to zero changes. The problem is formulated as a sophisticated Mixed Integer Programming (MIP) model in which the new jobs' processing and transportation operations are inserted into the available time intervals of the original schedule. To strengthen the MIP model, speed up constraints are added by taking advantage of specific relationships between the available time intervals arranged for a job's processing and transportation operations. Furthermore, an exact iterative algorithm is proposed, which starts with a relaxed solution of the MIP model and iteratively adds essential robot handling capacity constraints back to the relaxed MIP model until an optimal solution is found. Computational results validate effectiveness and efficiency of the strengthened MIP model and the iterative algorithm. (C) 2018 Published by Elsevier Ltd.
机译:本文研究了机器人单元中的实时动态作业车间调度问题,其中多个作业以意外的到达率进入该单元。与传统的流水车间和作业车间调度问题不同,必须考虑由机器人处理的作业运输。另一个特征是作业的处理时间不是恒定值,而是受限于时间窗口约束。为了实时有效地解决此问题,原始计划限制为零更改。该问题被公式化为复杂的混合整数编程(MIP)模型,其中新作业的处理和运输操作被插入到原始计划的可用时间间隔中。为了增强MIP模型,通过利用为作业的处理和运输操作安排的可用时间间隔之间的特定关系来增加加速约束。此外,提出了一种精确的迭代算法,该算法从MIP模型的松弛解决方案开始,然后将基本的机器人处理能力约束迭代添加回松弛的MIP模型,直到找到最佳解决方案。计算结果验证了增强型MIP模型和迭代算法的有效性和效率。 (C)2018由Elsevier Ltd.发布

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