Applying constraint satisfaction techniques to job shop scheduling

In this paper, the authors investigate the applicability of a constraint satisfaction problem solving (CSP) model, recently developed for deadline scheduling, to more commonly studied problems of schedule optimization. The present hypothesis is twofold: (1) that CSP scheduling techniques provide a basis for developing high-performance approximate solution procedures in optimization contexts, and (2) that the representational assumptions underlying CSP models allow these procedures to naturally accommodate the idiosyncratic constraints that complicate most real-world applications. The authors focus specifically on the objective criterion of makespan minimization, which has received the most attention within the job shop scheduling literature. They define an extended solution procedure somewhat unconventionally by reformulating the makespan problem as one of solving a series of different but related deadline scheduling problems, and embedding a simple CSP procedure as the subproblem solver. The authors first present the results of an empirical evaluation of our procedure performed on a range of previously studied benchmark problems. The present procedure is found to provide strong cost/performance, producing solutions competitive with those obtained using recently reported shifting bottleneck search procedures at reduced computational expense. To demonstrate generality, the authors also consider application of the present procedure to a more complicated, multi-product hoist scheduling problem. With only minor adjustments, the procedure is found to significantly outperform previously published procedures for solving this problem across a range of input assumptions.