Surrogate-Assisted Symbiotic Organisms Search Algorithm for Parallel Batch Processor Scheduling

Abstract

Parallel batch processor scheduling with dynamic job arrival is complex and challenging in semiconductor manufacturing. In order to get its reliable and high-performance schedule in a reasonable time, this work decomposes this scheduling problem into two-stage solution strategy: a batch forming subproblem and a batch scheduling subproblem. The batch formation is made by a heuristic rule. Then, a surrogate-assisted symbiotic organisms search algorithm with a new encoding mechanism is utilized to search for the optimal batch schedule, which integrates a surrogate model and a parameter control scheme. The surrogate model, which can predict the sequencing result instead of time-consuming true fitness evaluation, is used to reduce the computational burden greatly. In this article, a parameter control scheme based on reinforcement learning is proposed to balance the global and local search of symbiotic organisms search algorithm, as a guide for searching an assignment scheme. Finally, the experimental results demonstrate that the proposed algorithm can significantly improve the quality of a solution and save computational time via parameter control scheme and surrogate model.