An application of genetic algorithms to lot-streaming flow shop scheduling
An application of genetic algorithms to lot-streaming flow shop scheduling
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DOI：
10.1080/07408170208928911
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发表时间：
2002-09
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影响因子：
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通讯作者：
Suk-Hun Yoon;J. A. Ventura
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文献类型：
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作者：
Suk-Hun Yoon;J. A. Ventura
针对一种批量流水车间调度问题提出了一种混合遗传算法（HGA）。在该问题中，一个工件（批量）被拆分成若干较小的子批量，以便连续的工序能够重叠。目标是使工件完工时间与交货期的加权绝对偏差均值最小化。这种性能指标已被证明是非正则的，并且需要在具有间歇性空闲时间的调度方案中进行搜索以找到最优解。对于给定的工件顺序，提出了一个线性规划（LP）公式来获取最优的子批量完工时间。线性规划解的目标函数值被用于引导混合遗传算法朝着最佳顺序搜索。将混合遗传算法的性能与成对互换方法的性能进行了比较。
A Hybrid Genetic Algorithm (HGA) approach is proposed for a lot-streaming flow shop scheduling problem, in which a job (lot) is split into a number of smaller sublots so that successive operations can be overlapped. The objective is the minimization of the mean weighted absolute deviation of job completion times from due dates. This performance criterion has been shown to be non-regular and requires a search among schedules with intermittent idle times to find an optimal solution. For a given job sequence, a Linear Programming (LP) formulation is presented to obtain optimal sublot completion times. Objective function values of LP solutions are used to guide the HGA's search toward the best sequence. The performance of the HGA approach is compared with that of a pairwise interchange method.