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

Sustainability

Azfar Khalid¹  Raja Awais Liaqait²  Shermeen Hamid²  Salman Sagheer Warsi² 

[1] Department of Engineering, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK;Department of Mechanical Engineering, Capital University of Science & Technology, Islamabad 44000, Pakistan;

关键词: smart distributed system;    job shop scheduling;    Industry 4.0;    smart factory;    flexible job shop scheduling;   

DOI  :  10.3390/su13147684

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown