A New Hybrid GA/SA Algorithm for the Job Shop Scheduling Problem
Among the modern heuristic methods, simulated annealing (SA) and genetic algorithms (GA) represent powerful combinatorial optimization methods with complementary strengths and weaknesses. Borrowing from the respective advantages of the two paradigms, an effective combination of GA and SA, called Genetic Simulated Algorithm (GASA), is developed to solve the job shop scheduling problem (JSP). This new algorithm incorporates metropolis acceptance criterion into crossover operator, which could maintain the good characteristics of the previous generation and reduce the disruptive effects of genetic operators. Furthermore, we present two novel features for this algorithm to solve JSP. Firstly, a new full active schedule (FAS) based on the operation-based representation is presented to construct schedule, which can further reduce the search space. Secondly, we propose a new crossover operator, named Precedence Operation Crossover (POX), for the operation-based representation. The approach is tested on a set of standard instances and compared with other approaches. The Simulation results validate the effectiveness of the proposed algorithm.
KeywordsGenetic Algorithm Simulated Annealing Crossover Local Search
Unable to display preview. Download preview PDF.
- 1.Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of the International Conference on Genetic Algorithms and their Applications, pp. 136–149. Lawrence Erl-baum, Hillsdale (1985)Google Scholar
- 10.DeJong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Dissertation Abstracts International 36(10), 5140B(University Microfilms No.76-9381), Ph.D. Thesis, University of Michigan, Ann Arbor (1975)Google Scholar
- 13.Brown, D.E., Huntley, C.L., Spillane, A.R.: A Parallel Genetic Heuristic for the Quadratic Assignment Problem. In: Proceedings of the Third International Conference on Genetic Algorithms, Fairfax, VA, pp. 406–415 (1989)Google Scholar
- 14.Lin, F.T., Kao, C.Y., Hsu, C.C.: Incorporating Genetic Algorithms into Simulated Annealing. In: Proceeding of the Fourth International Symposium on Artificial Intelligence, pp. 290–297 (1991)Google Scholar
- 16.Gen, M., Tsujimura, Y., Kubota, E.: Solving Job-Shop Scheduling Problems by Genetic Algorithm. In: Proceedings of the 1995 IEEE International Conference on Systems, Man, and Cybernetics. Institute of Electrical and Electronics Engineers, Vancouver, pp. 1577–1582 (1995)Google Scholar
- 17.Shi, G.Y., Iima, H., Sannomiya, N.: A new encoding scheme for Job Shop problems by Genetic Algorithm. In: Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, pp. 4395–4400 (1996)Google Scholar
- 18.Cheng, R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithms – I. Representation. Computers and Industrial Engineering 30(9), 83–97 (1996)Google Scholar
- 20.Gonçalves, J.F.: A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem. AT&T Labs Research Technical Report TD-5EAL6J (September 2002)Google Scholar
- 21.Kobayashi, S., Ono, I., Yamamura, M.: An Efficient Genetic Algorithm for Job Shop Scheduling Problems. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 506–511 (1995)Google Scholar