Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
提出一种新的变深度搜索过程GLS，利用邻域树概念引导搜索，并将其嵌入移动瓶颈框架，形成高效混合算法，在文献中所有问题上测试表现优异。
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.