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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.16 No.2
- 발행연도
- 2017.6
- 수록면
- 253 - 264 (12page)
- DOI
- 10.7232/iems.2017.16.2.253
이용수
초록· 키워드
오류제보하기
In recent decades, flexible manufacturing systems have emerged as a response to market demands of high product diversity. Scheduling is one important phase in production planning in all manufacturing systems. Although scheduling in classical manufacturing systems, such as flow and job shops, are well studied. Rarely, any paper studies scheduling of the more recent flexible manufacturing system. Since the problem class is NP-hard, different scheduling algorithms such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) can be designed to solve this problem. This paper investigates a multi-objective evolutionary algorithm for scheduling flexible manufacturing systems to minimizing makespan, earliness and tardiness and startup costs. The distinctive feature of the proposed multi-objective evolutionary algorithm is its ability to search the solution space by an intelligent method, which is unlike other meta-heuristic algorithms avoid the coincidental method. Also, answer with the best quality and highest dispersion to obtain the dominant answer is used. Finally, we carry out computational experiments to demonstrate the effectiveness of our algorithm. The results show that the proposed algorithm has the ability to achieve the good solutions in reasonable computational time.
목차
ABSTRACT
1. INTRODUCTION AND LITERATURE REVIEW
2. PROBLEM FORMULATION
3. SCATTER SEARCH METHOD
4. THE GENERAL STRUCTURE OF SCATTER SEARCH METHOD
5. THE STRUCTURE OF PROPOSED SCATTER SEARCH METHOD
6. NSGA-II ALGORITHM
7. DETERMINING THE PARAMETERS OF ALGORITHM
8. CONCLUSION
REFERENCES
참고문헌 (26)
참고문헌 신청
Abazari, A. M., Solimanpur, M., and Sattari, H. (2012), Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm, Computers & Industrial Engineering, 62(2), 469-478.
Balaji, A. N. and Porselvi, S. (2014), Artificial immune system algorithm and simulated annealing algorithm for scheduling batches of parts based on job availability model in a multi-cell flexible manufacturing system, Procedia Engineering, 97, 1524-1533.
Baruw, O. T. and Piera, M. A. (2015), Identifying FMS repetitive patterns for efficient search-based scheduling algorithm: A colored Petri net approach, Journal of Manufacturing Systems, 35, 120-135.
Candan, G. and Yazgan, H. R. (2015), Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem, International Journal of Production Research, 53(3), 897-915.
Cotta, C. (2006), Scatter search with path relinking for phylogenetic inference, European Journal of Operational Research, 169(1), 520-539.
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