A new hybrid GA/SA algorithm for the job shop scheduling problem

  • Authors:

  • Chaoyong Zhang;Peigen Li;Yunqing Rao;Shuxia Li

  • Affiliations:

  • School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, P.R. China;School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, P.R. China;School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, P.R. China;School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, P.R. China

  • Venue:
  • EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.