Guided local search with shifting bottleneck for job shop scheduling
Guided local search with shifting bottleneck for job shop scheduling
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DOI：
10.1287/mnsc.44.2.262
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发表时间：
1998-02-01
影响因子：
5.4
通讯作者：
Vazacopoulos, A
中科院分区：
文献类型：
--
作者：
Balas, E;Vazacopoulos, A
许多最近开发的本地搜索程序的作业车间调度使用交换的操作，嵌入在模拟退火或禁忌搜索框架。我们开发了一个新的可变深度搜索过程，GLS（引导本地搜索），基于交换方案，并使用新的概念的邻域树。邻域的结构属性被用来引导搜索有前途的方向。虽然这个过程成功地与其他人竞争，甚至作为一个独立的，一个混合的过程，嵌入GLS到一个转移瓶颈框架，并利用两个邻域结构之间的差异被证明是特别有效的。我们报告广泛的计算测试的所有问题，从文献中。
Many recently developed local search procedures for job shop scheduling use interchange of operations, embedded in a simulated annealing or tabu search framework. We develop a new variable depth search procedure, GLS (Guided Local Search), based on an interchange scheme and using the new concept of neighborhood trees. Structural properties of the neighborhood are used to guide the search in promising directions. While this procedure competes successfully with others even as a stand-alone, a hybrid procedure that embeds GLS into a Shifting Bottleneck framework and takes advantage of the differences between the two neighborhood structures proves to be particularly efficient. We report extensive computational testing on all the problems available from the literature.