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首页> 外文期刊>Journal of industrial and management optimization >RESCHEDULING OPTIMIZATION OF STEELMAKING-CONTINUOUS CASTING PROCESS BASED ON THE LAGRANGIAN HEURISTIC ALGORITHM
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RESCHEDULING OPTIMIZATION OF STEELMAKING-CONTINUOUS CASTING PROCESS BASED ON THE LAGRANGIAN HEURISTIC ALGORITHM

机译:基于拉格朗日启发式算法的炼钢连铸工艺优化

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摘要

This study investigates a challenging problem of rescheduling a hybrid flow shop in the steelmaking-continuous casting (SCC) process, which is a major bottleneck in the production of iron and steel. In consideration of uncertain disturbance during SCC process, we develop a time-indexed formulation to model the SCC rescheduling problem. The performances of the rescheduling problem consider not only the efficiency measure, which includes the total weighted completion time and the total waiting time, but also the stability measure, which refers to the difference in the number of operations processed on different machines for the different stage in the original schedule and revised schedule. With these objectives, this study develops a Lagrangian heuristic algorithm to solve the SCC rescheduling problem. The algorithm could provide a realizable termination criterion without having information about the problem, such as the distance between the initial iterative point and the optimal point. This study relaxes machine capacity constraints to decompose the relaxed problem into charge-level subproblems that can be solved using a polynomial dynamic programming algorithm. A heuristic based on the solution of the relaxed problem is presented for obtaining a feasible reschedule. An improved efficient subgradient algorithm is introduced for solving Lagrangian dual problems. Numerical results for different events and problem scales show that the proposed approach can generate high-quality reschedules within acceptable computational times.
机译:这项研究调查了在炼钢-连铸(SCC)工艺中重新安排混合流水车间的挑战性问题,这是钢铁生产的主要瓶颈。考虑到SCC过程中的不确定干扰,我们开发了一个时间索引公式来建模SCC重新计划问题。重新计划问题的性能不仅考虑效率度量(包括总加权完成时间和总等待时间),还考虑稳定性度量,该稳定性度量指不同阶段在不同机器上处理的操作数量的差异在原始时间表和修订时间表中。基于这些目标,本研究开发了一种拉格朗日启发式算法来解决SCC重新调度问题。该算法可以提供可实现的终止标准,而无需提供有关问题的信息,例如初始迭代点和最佳点之间的距离。这项研究放宽了机器的容量约束,以将松散的问题分解为可以使用多项式动态规划算法解决的电荷级子问题。提出了一种基于松弛问题解决方案的启发式方法,以获取可行的重新计划。提出了一种改进的高效次梯度算法,用于求解拉格朗日对偶问题。不同事件和问题规模的数值结果表明,该方法可以在可接受的计算时间内生成高质量的时间表。

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