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

Document Type

Article

Publication Date

10-1-2020

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.

Identifier

85089773829 (Scopus)

Publication Title

IEEE ASME Transactions on Mechatronics

External Full Text Location

https://doi.org/10.1109/TMECH.2020.2996911

e-ISSN

1941014X

ISSN

10834435

First Page

2155

Last Page

2166

Issue

5

Volume

25

Grant

Z191100006119031

Fund Ref

National Natural Science Foundation of China

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