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In this paper search heuristics are developed for generic sequencing problems with emphasis on job shop scheduling. The proposed methods integrate problem specific heuristics common to Operations Research and local search approaches from Artificial Intelligence in order to obtain desirable properties from both. The applicability of local search to a wide range of problems, and the incorporation of problem-specific information are both properties of the proposed algorithms. Two methods are proposed, both of which are based on novel definitions of solution spaces and of neighborhoods in these spaces. Applications of the proposed methodology are developed for job shop scheduling problems, and can be easily applied with any scheduling objective. To demonstrate effectiveness, the method is tested on the job shop scheduling problem with the minimum makespan objective. Encouraging results are obtained.local search, search neighborhoods, scheduling, combinatorial optimization

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In this paper we study the weighted tardiness job-shop scheduling problem, taking into consideration the presence of random shop disturbances. A basic thesis of the paper is that global scheduling performance is determined primarily by a subset of the scheduling decisions to be made. By making these decisions in an a priori static fashion, which maintains a global perspective, overall performance efficiency can be achieved. Further, by allowing the remaining decisions to be made dynamically, flexibility can be retained in the schedule to compensate for unforeseen system disturbances. We develop a decomposition method that partitions job operations into an ordered sequence of subsets. This decomposition identifies and resolves a “crucial subset” of scheduling decisions through the use of a branch-and-bound algorithm. We conduct computational experiments that demonstrate the performance of the approach under deterministic cases, and the robustness of the approach under a wide range of processing time perturbations. We show that the performance of the method is superior, particularly for low to medium levels of disturbances.