Guided Local Search with Shifting Bottleneck for Job Shop scheduling

摘要

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.