Simulation based optimization of a train maintenance facility
Tóm tắt
In this paper, a simulation based optimization method is developed for optimization of scheduling policies. This method uses the technique of coupling industrial simulation software with a multi-objective optimizer based on genetic algorithms. It is used to optimize the performances of a railway maintenance facility by choosing the best scheduling policy. Numerical results show that a significant improvement is achieved with respect to the simulation results of the existing system. The method adapted by our problem can be extended to deal with the selection of scheduling rules in using other types of simulation models.
Tài liệu tham khảo
Baesler, F., et al. (2002). Productivity improvement in the wood industry using simulation and artificial intelligence. Winter Simulation Conference; San Diego, pp. 1095–1098.
Deb, K. (2002). Multi-Objective Optimization using Evolutionary Algorithms. John Wiley and Sons Ltd., pp. 245–253.
Drake, G., & Smith, J. (1996). Simulation system for real-time planning, scheduling, and control. Winter Simulation Conference; Coronado, pp. 1083–1090.
Elmaraghy H. et al (1998). On-line simulation and control in manufacturing systems. Annals of the CIRP 47: 401–404
Glover, F., et al. (1996). New advances and applications of combining simulation and optimization. Winter Simulation Conference; Coronado, pp. 144–152.
Goldberg, D. E. (1989). Genetic algorithms in search optimization and machine learning. Addison Wesley.
Hani, Y., Chehade, H., Amodeo, L., & Yalaoui, F. (2006). Simulation based optimization of a train maintenance facility model using genetic algorithms. IEEE International conference Service System and Service Management SSSM’06, Troyes France, p. 6.
Hani, Y., Amodeo, L., Yalaoui, F., & Chen, H. (2007). Ant colony optimization for solving an industrial layout problem. European Journal of Operational Research, (In Press), Available online 12 January.
Harmonosky, C. (1995). Simulation based real-time scheduling: review of recent developments. Winter Simulation Conference; Arlington, pp. 220–225.
Iannoni, A., & Morabito, R. A. (2005). Discrete simulation analysis of a logistics supply system. Transportation Research Part E (in press).
Kelton, W., Sadowski, R., & Sadowski, D. (1998). Simulation with Arena. Etats Unis: McGraw-Hill, 547, p. ISBN 0-07-027509-2.
Lacomme, P., Prins, C., & Sevaux, M. (2005). A genetic algorithm for a bi-objective capacitated arc routing problem. Computers and Operations Research, pp. 1–25.
Liu M. and Wu C. (2004). Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line. Robotics and Computer Integrated Manufacturing 20: 225–236
Mansouri S. (2005). A multi-objective genetic algorithm for mixed model sequencing on JIT assembly lines. European Journal of Operational Research 167: 696–716
Marasini, R., & Dawood, N. (2002). Simulation modeling and optimization of stockyard layouts for precast concrete products. Winter Simulation Conference; San Diego, pp. 1731–1736.
Meunier H. (2002). Algorithme évolutionnaires parallèles pour l’optimisation multiobjectif de réseaux de télécommunications. Université des sciences et technologie de Lille.
Muhl E., Charpentier P. and Chaxel F. (2003). Optimization of physical flows in an automotive manufacturing plant: some experiments and issues. Engineering Applications of Artificial Intelligence 16: 293–305
Pierreval H. (1997). Using Evolutionary Algorithms and simulation for the optimization of manufacturing systems. IIE Transactions 29(3): 181–190
Sadowski, D., & Bapat, V. (1999). The Arena product family: enterprise modeling solutions. Winter Simulation Conference; Phoenix, pp. 159–166.
Yalcin A. and Namballa R. (2005). An object oriented simulation framework for real-time control of automated flexible manufacturing systems. Computers and Industrial Engineering 48: 111–127
Yang T. and Chou P. (2005). Solving a multi response simulation-optimization problem with discrete variables using a multiple-attribute decision-making method. Mathematics and Computers in Simulation 68: 9–21