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Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries

机译:调度钢铁铸造热处理炉的启发式算法

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This paper addresses a research problem of scheduling parallel, non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. We were led to this problem through a real-world application involving the scheduling of heat-treatment operations of steel casting. The scheduling of furnaces for heat-treatment of castings is of considerable interest as a large proportion of the total production time is the processing times of these operations. In view of the computational intractability of this type of problem, a few heuristic algorithms have been designed for maximizing the utilization of heat-treatment furnaces of steel casting manufacturing. Extensive computational experiments were carried out to compare the performance of the heuristics with the estimated optimal value (using the Weibull technique) and for relative effectiveness among the heuristics. Further, the computational experiments show that the heuristic algorithms proposed in this paper are capable of obtaining near (statistically estimated) optimal utilization of heat-treatment furnaces and are also capable of solving any large size real-life problems with a relatively low computational effort.
机译:本文针对存在动态作业到达,作业系列不兼容和作业尺寸不相同的情况下调度并行,不同批次处理器的研究问题。我们通过涉及铸钢热处理操作调度的实际应用程序导致了这个问题。计划对铸件进行热处理的熔炉的时间表引起了极大的兴趣,因为总生产时间的很大一部分是这些工序的加工时间。考虑到此类问题的计算难点,已设计了一些启发式算法,以最大限度地利用铸钢制造的热处理炉。进行了大量的计算实验,以比较启发式算法的性能与估计的最佳值(使用Weibull技术)以及启发式算法之间的相对有效性。此外,计算实验表明,本文提出的启发式算法能够获得(统计上估计的)热处理炉最佳利用率,并且还能够以相对较低的计算量解决任何大尺寸的实际问题。

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