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A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations.
- Source :
- Processes; Oct2021, Vol. 9 Issue 10, p1700-1700, 1p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- PRODUCTION scheduling
PARTICLE swarm optimization
INDUSTRY 4.0
ALGORITHMS
SCHEDULING
Subjects
Details
- Language :
- English
- ISSN :
- 22279717
- Volume :
- 9
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Processes
- Publication Type :
- Academic Journal
- Accession number :
- 153341416
- Full Text :
- https://doi.org/10.3390/pr9101700