Review of job shop scheduling research and its new perspectives under Industry 4.0

Jian Zhang (), Guofu Ding (), Yisheng Zou (), Shengfeng Qin () and Jianlin Fu ()
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Jian Zhang: Southwest Jiaotong University
Guofu Ding: Southwest Jiaotong University
Yisheng Zou: Southwest Jiaotong University
Shengfeng Qin: Northumbria University
Jianlin Fu: Southwest Jiaotong University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, No 19, 1809-1830

Abstract: Abstract Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

Keywords: JSP scheduling; Artificial intelligence; Smart factory; Smart distributed scheduling (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (32)

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DOI: 10.1007/s10845-017-1350-2

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