Combination of genetic algorithm and shifting bottleneck heuristics to solve a job shop scheduling problem in wood processing company
1 Department of Industrial & Systems Engineering, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
2 Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam.
3 VNU Center for Education Accreditation, Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(01), 037–047.
Article DOI: 10.30574/wjaets.2024.11.1.0017
Publication history:
Received on 02 December 2023; revised on 17 January 2024; accepted on 20 January 2024
Abstract:
The scheduling problem has always been the concern of most companies in the flexible Job shop or Job shop environments because of its complexity. It is really a challenge for researchers to solve or find out the optimum solution. The lead time production at Teak Woods line in a wood processing company, a case study, faced with about 80% of orders which could not be done on time, although its capacity meet requirements. Therefore, it is very useful and necessary to set up a production scheduling model in order to reduce production time and synchronize the product’s parts through efficient modulation in an optimum sequence of jobs on the shopfloor. A combination of Genetic Algorithm and Shifting Bottleneck Heuristics was used to find out high quality solutions. It shows that the productivity increased from 2.32 m3/day to 4.3 m3/day (up 98.27%); Processing time was reduced from 3248.6 minutes to 1352.1 minutes (down 58.38%); The sync time decreased from 7554.8 minutes to 350.8 minutes (down 95.36%).
Keywords:
Genetic Algorithm; Shifting Bottleneck Heuristics; Combination of Genetic Algorithm and Shifting Bottleneck Heuristics; Job shop Scheduling Problem; Wood Processing Company.
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