Cheng-Chung Cheng and Stephen F. Smith, Carnegie Mellon University
In this paper, we consider the application a constraint satisfaction problem solving (CSP) framework recently developed for deadline scheduling to more commonly studied problems of schedule optimization. Our hypothesis is two-fold: (1) that CSP scheduling techniques can 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. We focus specifically on the objective criterion of makespan minimization, which has received the most attention within the job shop scheduling literature. We 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. We summarize results of an empirical evaluation of our procedure performed on a range of previously studied benchmark problems. Our 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, we also consider application of our procedure to a more complicated, multi-product hoist scheduling problem. With only minor adjustments, our procedure is found to significantly outperform previously published procedures for solving this problem across a range of input assumptions.