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

2017 ◽  
Vol 30 (4) ◽  
pp. 1809-1830 ◽  
Author(s):  
Jian Zhang ◽  
Guofu Ding ◽  
Yisheng Zou ◽  
Shengfeng Qin ◽  
Jianlin Fu
Keyword(s):  
Industry 4.0 ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling

scholarly journals Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 107 ◽  
Author(s):  
Matheus Leusin ◽  
Enzo Frazzon ◽  
Mauricio Uriona Maldonado ◽  
Mirko Kück ◽  
Michael Freitag
Keyword(s):  
Industry 4.0 ◽  
Data Exchange ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Intelligent System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Multi Agent Systems ◽  
Time Data ◽  
Shop Scheduling

Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

scholarly journals Optimization of the Job Shop Scheduling Problem in Industry 4.0

2020 ◽  
Vol 26 (1) ◽  
pp. 13-18
Author(s):  
Olga Ristić ◽  
Marjan Milošević ◽  
Sandra Milunović-Koprivica ◽  
Milan Vesković ◽  
Veljko Aleksić
Keyword(s):  
Industry 4.0 ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

scholarly journals A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

2021 ◽  
Vol 13 (14) ◽  
pp. 7684
Author(s):  
Raja Awais Liaqait ◽  
Shermeen Hamid ◽  
Salman Sagheer Warsi ◽  
Azfar Khalid
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Intelligent Manufacturing ◽  
Distributed Scheduling ◽  
Lead Times ◽  
Shop Scheduling ◽  
Third Phase ◽  
Customized Production

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation.

Memory Based Lamarckian Evolutionary Algorithm for Job Shop Scheduling Problem

2011 ◽  
Vol 21 (12) ◽  
pp. 3082-3093
Author(s):  
Zhu-Chang XIA ◽  
Fang LIU ◽  
Mao-Guo GONG ◽  
Yu-Tao QI
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling
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