INTEGRATING TARGET ANALYSIS AND TABU SEARCH FOR IMPROVED SCHEDULING SYSTEMS

This paper explores the integration of the Artificial Intelligence/Operations Research approach known as target analysis with tabu search to create a more effective system for machine scheduling. Target analysis is designed to give heuristic and optimization procedures the ability to learn what rules are best for solving particular classes of problems. The authors focus on the development of rules that depend on memory functions to incorporate diversifying elements in a tabu search method which is tailored to find optimal or near optimal solutions for a class of single machine scheduling problems with delay penalties and setup costs.