Robotics Institute Technical Report CMU-RI-TR-95-03

Applying Constraint Satisfaction Techniques to Job Shop Scheduling

Cheng-Chung Cheng and Stephen F. Smith

The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213, USA

Abstract

In this paper, we investigate the applicability of a constraint satisfaction problem solving (CSP) model, recently developed for deadline scheduling, to more commonly studied problems of schedule optimization. Our hypothesis is two-fold: (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. 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 first present the 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.
The research reported in this paper has been sponsored in part by the National Aeronautics and Space Administration, under contract NCC 2-531, by the Advanced Research Projects Agency under contract F30602-90-C-0119 and the CMU Robotics Institute. The authors can be reached through email at sfs@cs.cmu.edu.
Full paper in Postscript