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A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makespan and cost of resources

机译:具有资源依赖的处理时间的多目标流水车间调度:制造时间和资源成本之间的权衡

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In traditional flow shop scheduling problems (FSSPs), the processing times are assumed to be pre-known and fixed parameters while in many practical environments, the processing times can be controlled by consumption of extra resources. In this paper, we propose resource-dependent processing times (RDPT) for permutation FSSP in which the processing time of a job depends on the amount of additional resources assigned to that job. To make a trade-off between makespan and required amount of resources, two conflict objective functions are considered: the minimisation of maximum completion time and the minimisation of total cost of resources. In order to solve the problem considered in this paper, a decomposition approach is suggested that strives to deal with the original model via two sub-problems: (i) sequencing problem; and (ii) resource allocation problem. A hybrid discrete differential evolution (HDDE) algorithm with an effective coordinate directions search and a variable neighbourhood search (VNS) are combined to solve two sub-problems. Furthermore, a statistical procedure is employed to adjust the significant parameters of the proposed HDDE and VNS algorithms. This procedure is based on the stepwise regression (SR) technique. The effectiveness of the suggested hybrid algorithm is investigated through a computational study and obtained results show the good performance of our approach with regard to the other algorithms.
机译:在传统的流水车间调度问题(FSSP)中,假定处理时间是预先已知的固定参数,而在许多实际环境中,可以通过消耗额外的资源来控制处理时间。在本文中,我们提出了置换FSSP的资源依赖处理时间(RDPT),其中作业的处理时间取决于分配给该作业的其他资源的数量。为了在制造时间和所需的资源量之间进行权衡,考虑了两个冲突目标函数:最大完成时间的最小化和资源总成本的最小化。为了解决本文所考虑的问题,提出了一种分解方法,该方法试图通过两个子问题来处理原始模型:(i)排序问题; (ii)资源分配问题。将具有有效坐标方向搜索和可变邻域搜索(VNS)的混合离散差分进化(HDDE)算法结合起来,以解决两个子问题。此外,采用统计程序来调整建议的HDDE和VNS算法的重要参数。此过程基于逐步回归(SR)技术。通过计算研究研究了提出的混合算法的有效性,获得的结果表明我们的方法相对于其他算法具有良好的性能。

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