An Intelligent Optimization Model for Multi-objective Order Allocation Planning
This paper presents a multi-objective order allocation
planning problem with the consideration of various real-world
production features. A novel hybrid intelligent optimization model,
integrating a multi-objective memetic optimization process, a Monte
Carlo simulation technique and a heuristic pruning technique, is
proposed to handle this problem. Experiments based on industrial data
are conducted to validate the proposed model. Results show that (1)
the proposed model can effectively solve the investigated problem by
providing effective production decision-making solutions, which
outperformsan NSGA-II-based optimization process and an industrial
method.
[1] J. Ashby and R. Uzsoy, "Scheduling and order release in a single-stage
production system," J. Manuf. Syst., vol. 14, pp. 290-306, 1995.
[2] Z. X. Guo, W. K. Wong, S. Y. S. Leung, J. T. Fan, and S. F. Chan,
"Genetic optimization of order scheduling with multiple uncertainties,"
Expert Syst. Appl., vol. 35, pp. 1788-1801, 2008.
[3] Z. Chen and G. Pundoor, "Order assignment and scheduling in a supply
chain," Oper. Res., vol. 54, pp. 555-572, 2006.
[4] B. Cesaret, C. Oguz, and F. Salman, "A tabu search algorithm for order
acceptance and scheduling," Comput. Oper. Res., vol. 39, pp. 1197-1205,
2012.
[5] T. Loukil, J. Teghem, and P. Fortemps, "A multi-objective production
scheduling case study solved by simulated annealing," Eur. J. Oper. Res.,
vol. 179, pp. 709-722, 2007.
[6] Z. X. Guo, W. K. Wong, S. Y. S. Leung, and J. T. Fan, "Intelligent
production control decision support system for flexible assembly lines,"
Expert Syst. Appl., vol. 36, pp. 4268-4277, 2009.
[7] Y. Li, K. Man, K. Tang, S. Kwong, and W. Ip, "Genetic algorithm to
production planning and scheduling problems for manufacturing
systems," Prod. Plan. Control, vol. 11, pp. 443-458, 2000.
[8] A. Berrichi, F. Yalaoui, L. Amodeo, and M. Mezghiche, "Bi-Objective
Ant Colony Optimization approach to optimize production and
maintenance scheduling," Comput. Oper. Res., vol. 37, pp. 1584-1596,
2010.
[9] G. Luh and C. Chueh, "A multi-modal immune algorithm for the job-shop
scheduling problem," Inform. Sciences, vol. 179, pp. 1516-1532, 2009.
[10] Y. Ong, M. Lim, N. Zhu, and K. Wong, "Classification of adaptive
memetic algorithms: a comparative study," IEEE T. Syst. Man Cy. B., vol.
36, pp. 141-152, 2006.
[11] M. Frutos, A. Olivera, and F. Tohme, "A memetic algorithm based on a
NSGAII scheme for the flexible job-shop scheduling problem," Ann.
Oper. Res., vol. 181, pp. 745-765, 2010.
[12] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," IEEE T. Evolut. Comput.,
vol. 6, pp. 182-197, 2002.
[13] M. F. Aburdene, Computer Simulation of Dynamical Systems. Dubuque,
IOWA: Wm. C. Brown Publishers, 1988.
[14] H. Taboada and D. Coit, "Multi-objective scheduling problems:
Determination of pruned Pareto sets," IIE Trans., vol. 40, pp. 552-564,
2008.
[15] F. Glover and M. Laguna, Tabu Search. Dordrecht: Kluwer Academic
Publishers, 1997.
[16] A. E. Eiben, E. Marchiori, and V. A. Valko, "Evolutionary algorithms
with on-the-fly population size adjustment," Lect. Notes Comput. Sc., vol.
3242, pp. 41-50, 2004.
[17] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning. Boston, MA, USA: Addison-Wesley, 1989.
[18] A. E. Eiben, P. E. Raue, and Z. Ruttkay, "Genetic algorithms with
multi-parent recombination," Proc. of PPSN III, vol. 866, pp. 78-87,
1994.
[1] J. Ashby and R. Uzsoy, "Scheduling and order release in a single-stage
production system," J. Manuf. Syst., vol. 14, pp. 290-306, 1995.
[2] Z. X. Guo, W. K. Wong, S. Y. S. Leung, J. T. Fan, and S. F. Chan,
"Genetic optimization of order scheduling with multiple uncertainties,"
Expert Syst. Appl., vol. 35, pp. 1788-1801, 2008.
[3] Z. Chen and G. Pundoor, "Order assignment and scheduling in a supply
chain," Oper. Res., vol. 54, pp. 555-572, 2006.
[4] B. Cesaret, C. Oguz, and F. Salman, "A tabu search algorithm for order
acceptance and scheduling," Comput. Oper. Res., vol. 39, pp. 1197-1205,
2012.
[5] T. Loukil, J. Teghem, and P. Fortemps, "A multi-objective production
scheduling case study solved by simulated annealing," Eur. J. Oper. Res.,
vol. 179, pp. 709-722, 2007.
[6] Z. X. Guo, W. K. Wong, S. Y. S. Leung, and J. T. Fan, "Intelligent
production control decision support system for flexible assembly lines,"
Expert Syst. Appl., vol. 36, pp. 4268-4277, 2009.
[7] Y. Li, K. Man, K. Tang, S. Kwong, and W. Ip, "Genetic algorithm to
production planning and scheduling problems for manufacturing
systems," Prod. Plan. Control, vol. 11, pp. 443-458, 2000.
[8] A. Berrichi, F. Yalaoui, L. Amodeo, and M. Mezghiche, "Bi-Objective
Ant Colony Optimization approach to optimize production and
maintenance scheduling," Comput. Oper. Res., vol. 37, pp. 1584-1596,
2010.
[9] G. Luh and C. Chueh, "A multi-modal immune algorithm for the job-shop
scheduling problem," Inform. Sciences, vol. 179, pp. 1516-1532, 2009.
[10] Y. Ong, M. Lim, N. Zhu, and K. Wong, "Classification of adaptive
memetic algorithms: a comparative study," IEEE T. Syst. Man Cy. B., vol.
36, pp. 141-152, 2006.
[11] M. Frutos, A. Olivera, and F. Tohme, "A memetic algorithm based on a
NSGAII scheme for the flexible job-shop scheduling problem," Ann.
Oper. Res., vol. 181, pp. 745-765, 2010.
[12] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," IEEE T. Evolut. Comput.,
vol. 6, pp. 182-197, 2002.
[13] M. F. Aburdene, Computer Simulation of Dynamical Systems. Dubuque,
IOWA: Wm. C. Brown Publishers, 1988.
[14] H. Taboada and D. Coit, "Multi-objective scheduling problems:
Determination of pruned Pareto sets," IIE Trans., vol. 40, pp. 552-564,
2008.
[15] F. Glover and M. Laguna, Tabu Search. Dordrecht: Kluwer Academic
Publishers, 1997.
[16] A. E. Eiben, E. Marchiori, and V. A. Valko, "Evolutionary algorithms
with on-the-fly population size adjustment," Lect. Notes Comput. Sc., vol.
3242, pp. 41-50, 2004.
[17] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning. Boston, MA, USA: Addison-Wesley, 1989.
[18] A. E. Eiben, P. E. Raue, and Z. Ruttkay, "Genetic algorithms with
multi-parent recombination," Proc. of PPSN III, vol. 866, pp. 78-87,
1994.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:56972", author = "W. K. Wong and Z. X. Guo and P.Y. Mok", title = "An Intelligent Optimization Model for Multi-objective Order Allocation Planning", abstract = "This paper presents a multi-objective order allocation
planning problem with the consideration of various real-world
production features. A novel hybrid intelligent optimization model,
integrating a multi-objective memetic optimization process, a Monte
Carlo simulation technique and a heuristic pruning technique, is
proposed to handle this problem. Experiments based on industrial data
are conducted to validate the proposed model. Results show that (1)
the proposed model can effectively solve the investigated problem by
providing effective production decision-making solutions, which
outperformsan NSGA-II-based optimization process and an industrial
method.", keywords = "Multi-objective order allocation planning, Pareto
optimization, Memetic algorithm, Mento Carlo simulation", volume = "6", number = "4", pages = "803-6", }