A GENETIC ALGORITHM FOR SCHEDULING OF MULTIPRODUCT BATCH PROCESSES

During the last decade the methods for solving optimal scheduling prob lems have been improved. But it is still hard to find out the optimal or very near optimal solution for large size batch process scheduling problems. Ku and Karimi (1991) developed a simulated annealing (SA) me thod for solving scheduling problems and showed that SA offers good pe rformance but the control parameters of SA must be tuned when the prob lem constraints are changed. In this work, we develop a genetic algori thm (GA) for effectively solving large-size scheduling problems. The a pplication of GA to multi-product batch process scheduling problems wi th several intermediate storage policies is treated. Particular form o f GA is shown to be suitable for this class and scheduling problems wi thout tuning of algorithm parameters for different problem parameter s ets. We solved various size of problems for the minimization of makesp an with unlimited intermediate storage (UIS) and zero wait (ZW) storag e policies to test the performance of GA. GA is shown to be superior t o heuristic of SA-based search methods. (C) 1998 Elsevier Science Ltd. All rights reserved.