A multi-objective fuzzy genetic algorithm for job-shop scheduling problems

• Autorzy: Xing Y. J. , Wang Z. Q. , Sun J. , Meng J. J.
• Języki publikacji: EN

Abstrakty

EN

Purpose: Many uncertain factors in job shop scheduling problems are critical for the scheduling procedures. There are not genetic algorithms to solve this problem drastically. A new genetic algorithm is proposed for fuzzy job shop scheduling problems. Design/methodology/approach: The imprecise processing times are modeled as triangular fuzzy numbers (TFNs) and the due dates are modeled as trapezium fuzzy numbers in this paper. A multi-objective genetic algorithm is proposed to solve fuzzy job shop scheduling problems, in which the objective functions are conflicting. Agreement index (AI) is used to show the satisfaction of client which is defined as value of the area of processing time membership function intersection divided by the area of the due date membership function. The multi-objective function is composed of maximize both the minimum agreement and maximize the average agreement index. Findings: Two benchmark problems were used to show the effectiveness of the proposed approach. Experimental results demonstrate that the multi-objective genetic algorithm does not get stuck at a local optimum easily, and it can solve job-shop scheduling problems with fuzzy processing time and fuzzy due date effectively. Research limitations/implications: In this paper only two objective functions of genetic algorithm are taken into consideration. Many other objective functions are not applied to this genetic algorithm. Originality/value: A new multi-objective fuzzy genetic algorithm is proposed for fuzzy genetic algorithm. The genetic operations can search the optimization circularly.

Słowa kluczowe

EN

artificial intelligence methods; scheduling; genetic algorithm; multiple objectives

PL

metody sztucznej inteligencji; szeregowanie zadań; algorytm genetyczny

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2006
• Tom: Vol. 17, nr 1-2
• Strony: 297--300
• Opis fizyczny: Bibliogr. 8 poz., rys., tab., wykr.

Twórcy

autor

Xing Y. J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024, P. R. China

autor

Wang Z. Q.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024, P. R. China

autor

Sun J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024, P. R. China

autor

Meng J. J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024, P. R. China

Bibliografia

• [1] R. Knosala and T. Wal, A production scheduling problem using genetic algorithm, Journal of Materials Processing Technology, Volume 109, Issues 1-2, 1 February 2001, Pages 90-95.
• [2] M. Gen, R. Cheng, Genetic Algorithms and Engineering Design, Wiley, New York, 1997.
• [3] S. Chanas, A. Kasperski, Minimizing maximum lateness in a single machine scheduling problem with fuzzy processing times and fuzzy due dates, Eng. Appl. Artif. Intell. 14 (2001) 377–386.
• [4] Nakano R. Conventional genetic algorithm for job shop problems. In: Proceedings of the Fourth International Conference on Genetic Algorithms, 1991. p. 474-9.
• [5] Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989.
• [6] Edy Bertolissi, Heuristic algorithm for scheduling in the no-wait flow-shop, Journal of Materials Processing Technology, Volume 107, Issues 1-3, 22 November 2000, Pages 459-465.
• [7] Wang Ling. Intelligent optimization algorithms with application [M].Beijing: Tsinghua University Press, 2001.
• [8] S. Tiwari and N. Chakraborti, Multi-objective optimization of a two-dimensional cutting problem using genetic algorithms, Journal of Materials Processing Technology, Volume 173, Issue 3, 20 April 2006, Pages 384-393.