An Effective Heuristic Algorithm for Flexible Flow Shop Scheduling Problems with Parallel Batch Processing
DOI: 10.23977/msom.2023.040109 | Downloads: 52 | Views: 558
Author(s)
Safiye Turgay 1, Abdulkadir Aydın 1
Affiliation(s)
1 Department of Industrial Engineering, Sakarya University, Sakarya, Turkey
Corresponding Author
Safiye TurgayABSTRACT
In this study, a firm's scheduling problem optimized using the genetic algorithm method and it aimed to reach the schedule that gives the smallest time in the production schedules. Considering the scheduling of the solenoid part produced by the company, a schedule with a shorter production time than the current production time of the part obtained and the production times of the company were improved. A genetic algorithm developed to solve the parallel batch processing problems. The developed genetic algorithm is an effective heuristic algorithm for the flexible flow type problem. Parameter optimization study carried out to improve the solution performance of genetic algorithms. Genetic operators examined in detail and compared with each other, and the most appropriate parameter set was determined because of research and experiments. The best parameters found for each problem with suggested algorithm. In order to reach the optimum solution of the part to produce in the scheduling problem, chromosomes created and sequence sizes randomly assigned. These assigned dimensions are in ascending order and converted to actual rows. Then, the total production times were determined by generating solutions sequentially from the generated chromosomes.
KEYWORDS
Genetic Algorithm, Flexible Flow Shop, Scheduling, OptimizationCITE THIS PAPER
Safiye Turgay, Abdulkadir Aydın, An Effective Heuristic Algorithm for Flexible Flow Shop Scheduling Problems with Parallel Batch Processing. Manufacturing and Service Operations Management (2023) Vol. 4: 62-70. DOI: http://dx.doi.org/10.23977/msom.2023.040109.
REFERENCES
[1] Baker K. R., Trietsch D. (2009). Principles of Sequencing and Scheduling. New Jersey: Wiley, 2009.
[2] French S. (1982). Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop, Chichester, UK, Ellis Horwood.
[3] Herrmann J. (2007). The legacy of Taylor, Gantt, and Johnson: How to improve production scheduling, The Institute for Systems Research, 1-12.
[4] Pinedo M.L.(2008). Scheduling: Theory, Algorithms and Systems (3rd Ed.). New York: Springer.
[5] Ribas I., Leisten R., Framiñan J.M.(2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective, Computers & Operations Research, Vol. 37, Issue 8, pp. 1439-1454.
[6] Gen M., Cheng R. (1997) Genetic Algorithms & Engineering Design. John Wiley & Sons, Inc., New York
[7] Wang H.(2005). Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions, Expert Systems, 22(2), 78-85.
[8] Shiau D., Cheng S.C., Huang Y.M.(2008). Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm, Expert Systems with Applications, Volume 34, Issue 2, pp. 1133-1143.
[9] Allahverdi A., Ng C. T., Cheng T. C., Mikhail E., Kovalyov Y.(2008). A survey of scheduling problems with setup times or costs,European Journal of Operational Research, Volume 187, Issue 3, pp. 985-1032.
[10] Yaurima V., Burtseva L., Tchernykh (2009). A. Hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints and limited buffers, Computers & Industrial Engineering, Vol. 56, Issue 4, pp. 1452-1463
[11] Aydın A., Turgay S. (2021). Using the Genetic Algorithm to Solution of Flexible Flow Shop Scheduling Problems with Alternative Routings, Proceedings of 11h International Symposium on Intelligent Manufacturing and Service Systems, Conflicting Between Technology and Humanity in Future Society and Mind, IMSS’21Sakarya University - Sakarya/Turkey (Virtual + Onsite), 27-29 May 2021, Sakarya, TURKEY.
[12] Dai M, Tang D, Zheng K, Cai Q. (2013). An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem. Advances in Mechanical Engineering, 5. doi:10.1155/2013/124903.
[13] Li Z. Liu J., Chen Q., Mao N., Wang X. (2015). Approximation algorithms for the three-stage flexible flow shop problem with mid group constraint, Expert Systems with Applications, Vol. 42, Issue 7, pp. 3571-3584.
[14] Koçer E., Turgay S. (2021). Genetic Algorithm Based Approach to Flexible Workhop Problem, Proceedings of 11h International Symposium on Intelligent Manufacturing and Service Systems, Conflicting Between Technology amd Humanity in Future Society and Mind, IMSS’21Sakarya University - Sakarya/Turkey (Virtual + Onsite), 27-29 May 2021, Sakarya, TURKEY.
[15] Li X., Wang Q. (2006). Heuristics for permutation flow shops to minimize total flowtime," 2006 10th International Conference on Computer Supported Cooperative Work in Design, Nanjing, China, 2006, pp. 1-5, doi: 10. 1109/ CSCWD. 2006.253167.
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