A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

• Published in: Processes Vol. 9; no. 10; p. 1700
• Main Authors: Awad, Mohammed A., Abd-Elaziz, Hend M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Algorithms; Chromosomes; Empowerment; Heuristic; Information sharing; Job shop scheduling; Job shops; Manufacturers; Manufacturing; Optimization techniques; Particle swarm optimization; Process planning; Scheduling; Scientific papers; Search algorithms; Taxonomy
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr9101700

In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.