Research on double-objective optimal scheduling algorithm for dual resource constrained job shop

  • Authors:

  • Li Jingyao;Sun Shudong;Huang Yuan;Niu Ganggang

  • Affiliations:

  • Key Lab of Contemporary Design and Integrated Manufacturing Technology, MOE, Xi'an, China;Key Lab of Contemporary Design and Integrated Manufacturing Technology, MOE, Xi'an, China;School of Mechatronics, Northwestern Polytechnical University, Xi'an, China;Key Lab of Contemporary Design and Integrated Manufacturing Technology, MOE, Xi'an, China

  • Venue:
  • AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
  • Year:
  • 2010

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Abstract

To solve the double-objective optimization of dual resource constrained job shop scheduling, an inherited genetic algorithm is proposed. In the algorithm, evolutionary experience of parent population is inherited by the means of branch population supplement based on pheromones to accelerate the convergence rate. Meanwhile, the activable decoding algorithm based on comparison among time windows, the resource crossover operator and resource mutation operator, which are all established based on four-dimensional coding method are utilized with reference to the character of dual resource constrained to improve the overall searching ability. Furthermore, the championship selection strategy based on Pareto index weakens the impact of the Pareto level of chromosomes obviously. The elitist preservation strategy guarantees reliable convergence of the algorithm. Simulation results show that the performance of the proposed inherited GA is effective and efficient.