ACO-based multi-objective scheduling of parallel batch processing machines with advanced process control constraints

• Published in: International journal of advanced manufacturing technology Vol. 44; no. 9-10; pp. 985 - 994
• Main Authors: Li, Li, Qiao, F., Wu, Q. D.
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.10.2009; Springer Nature B.V
• Subjects: Algorithms; Ant colony optimization; Batch processing; CAE) and Design; Computer simulation; Computer-Aided Engineering (CAD; Engineering; Industrial and Production Engineering; Mechanical Engineering; Media Management; Multiple objective analysis; Original Article; Process controls; Production scheduling; Scheduling; Time dependence; Scheduling; Parallel batch processing machines (PBPM); Semiconductor manufacturing; ACO
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-008-1904-8

This research was motivated by a scheduling problem in the dry strip operations of a semiconductor wafer fabrication facility. The machines were modeled as parallel batch processing machines with incompatible job families and dynamic job arrivals, and constraints on the sequence-dependent setup time and the qual-run requirements of advanced process control. The optimization had multiple objectives, the total weighted tardiness (TWT) and makespan, to consider simultaneously. Since the problem is NP-hard, we used an Ant Colony Optimization (ACO) algorithm to achieve a satisfactory solution in a reasonable computation time. A variety of simulation experiments were run to choose ACO parameter values and to demonstrate the performance of the proposed method. The simulation results showed that the proposed ACO algorithm is superior to the common Apparent Tardiness Cost-Batched Apparent Tardiness Cost rule for minimizing the TWT and makespan. The arrival time distribution and the number of jobs strongly affected the ACO algorithm’s performance.