Abstract
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.
Highlights
The order shifting by scheduling the timeline is analyzed using a genetic algorithm by the development of a system that is capable of detecting the deviations with the help of location-based data acquisition (DAQ)
In the first phase of this paper, the job shop scheduling problem (JSSP) problem is summarized based on the structural framework and scheduling algorithms
Various studies are classified based on solving algorithms
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Several studies have been conducted which analyzed the impact of the flow shop and job shop scheduling problem (JSSP) on manufacturing systems [1,2,3]. The transition from traditional scheduling to SDS faces two major niques with SDS, and the development of new problem-solving techniques required for research challenges: the integration of conventional JSSP scheduling techniques with SDS, SDS. A detailed review of the literature reveals that several studies have viewed the integration of JSSP with Industry. Techhighlighted the the useuse of multi-criteria decision-making techniques along with niques along with mathematical optimization models to analyze the JSSP. Study analyzed in Industry 4.0 for solving JSSP were conducted by Zhang et al [18]
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