INTEGRATING TARGET ANALYSIS AND TABU SEARCH FOR IMPROVED SCHEDULING SYSTEMS

This paper explores the integration of the Artificial Intelligence/Ope rations Research approach known as target analysis with tabu search to create a more effective system for machine scheduling. Target analysi s is designed to give heuristic and optimization procedures the abilit y to learn what rules are best for solving particular classes of probl ems. The authors focus on the development of rules that depend on memo ry functions to incorporate diversifying elements in a tabu search met hod which is tailored to find optimal or near optimal solutions for a class of single machine scheduling problems with delay penalties and s etup costs.