@article{46395db73f014b1ca3f8ed75f8a5b431,
title = "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. ",
keywords = "Parallel batch processor scheduling, reinforcement learning (RL), surrogate model, symbolic organisms search algorithm",
author = "Cao, {Zheng Cai} and Lin, {Cheng Ran} and Zhou, {Meng Chu} and Zhang, {Jia Qi}",
note = "Funding Information: This work was supported in part by the Beijing Leading Talents Program under Grant Z191100006119031, in part by the National Natural Science Foundation of China under Grant 61802013, in part by the State Key Laboratory of Synthetical Automation for Process Industries under Grant PAL-N201804, and in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University under Grant RG-21-135-38. Funding Information: Manuscript received December 30, 2019; revised March 4, 2020; accepted May 17, 2020. Date of publication May 22, 2020; date of current version October 14, 2020. This work was supported in part by the Beijing Leading Talents Program under Grant Z191100006119031, in part by the National Natural Science Foundation of China under Grant 61802013, in part by the State Key Laboratory of Synthetical Automation for Process Industries under Grant PAL-N201804, and in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University under Grant RG-21-135-38. Recommended by Technical Editor H. Ding. (Corresponding authors: ZhengCai Cao; MengChu Zhou.) ZhengCai Cao is with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China, and also with the State Key Laboratory for Manufacturing Systems Engineering, Xi{\textquoteright}an Jiaotong University, Xi{\textquoteright}an 710049, China (e-mail: giftczc@163.com). Publisher Copyright: {\textcopyright} 1996-2012 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = oct,
doi = "10.1109/TMECH.2020.2996911",
language = "English (US)",
volume = "25",
pages = "2155--2166",
journal = "IEEE/ASME Transactions on Mechatronics",
issn = "1083-4435",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",
}