Multi-matrix Real-coded Genetic Algorithm for Minimising Total Costs in Logistics Chain Network
The importance of supply chain and logistics
management has been widely recognised. Effective management of
the supply chain can reduce costs and lead times and improve
responsiveness to changing customer demands. This paper proposes a
multi-matrix real-coded Generic Algorithm (MRGA) based
optimisation tool that minimises total costs associated within supply
chain logistics. According to finite capacity constraints of all parties
within the chain, Genetic Algorithm (GA) often produces infeasible
chromosomes during initialisation and evolution processes. In the
proposed algorithm, chromosome initialisation procedure, crossover
and mutation operations that always guarantee feasible solutions
were embedded. The proposed algorithm was tested using three sizes
of benchmarking dataset of logistic chain network, which are typical
of those faced by most global manufacturing companies. A half
fractional factorial design was carried out to investigate the influence
of alternative crossover and mutation operators by varying GA
parameters. The analysis of experimental results suggested that the
quality of solutions obtained is sensitive to the ways in which the
genetic parameters and operators are set.
[1] H. Gunnarsson, M. Ronnqvist, and J. T. Lundgren, "Supply chain
modelling of forest fuel," Eur. J. Oper. Res., vol. 158, no. 1, pp. 103-
123, 2004.
[2] R.Z. Farahani, and M. Elahipanah, "A genetic algorithm to optimize the
total cost and service level for just-in-time distribution in a supply
chain," Int. J. Prod. Econ., to be published.
[3] A. Syarif, Y. Yun, and M. Gen, "Study on multi-stage logistic chain
network: a spanning tree-based genetic algorithm approach," Comp. Ind.
Eng., vol. 43, no. 1-2, pp. 299-314, 2002.
[4] D. E. Goldberg, Genetic Algorithms in Search, Optimisation and
Machine Learning. Massachusetts, Addison-Wesley, 1989.
[5] M. Gen, and R. Cheng, Genetic Algorithms and Engineering Design.
New York, John Wiley and Sons, 1997.
[6] P. Pongcharoen, C. Hicks, and P. M. Braiden, "The development of
genetic algorithms for the finite capacity scheduling of complex
products, with multiple levels of product structure," Eur. J. Oper. Res.,
vol. 152, no. 1, pp. 215-225, 2004.
[7] C. Hicks, "A Genetic Algorithm tool for optimising cellular or
functional layouts in the capital goods industry," Int. J. Prod. Econ., vol.
104, no. 2, pp. 598-614, 2006.
[8] P. Pongcharoen, W. Promtet, P. Yenradee, and C. Hicks, "Stochastic
optimisation timetabling tool for university course scheduling," Int. J.
Prod. Econ., to be published.
[9] H. Aytug, M. Knouja, and F. E. Vergara, "Use of genetic algorithms to
solve production and operations management problems: a review," Int. J.
Prod. Res., vol. 41, no. 17, pp. 3955-4009, 2003.
[10] S. S. Chaudhry, and W. Luo, "Application of genetic algorithms in
production and operation management: a review," Int. J. Prod. Res., vol.
43, no. 19, pp. 4083-4101, 2005.
[11] L. Sun, Y. Zhang, and C. Jiang, "A matrix real-coded genetic algorithm
to the unit commitment problem," Elec. Pow. Syst. Res., vol. 76, no. 9-
10, pp. 716-728, 2006.
[12] P. Pongcharoen, D. J.Stewardson, C. Hicks, and P. M. Braiden,
"Applying designed experiments to optimize the performance of genetic
algorithms used for scheduling complex products in the capital goods
industry," J. Appl. Stat., vol. 28, no. 3-4, pp. 441-455, 2001.
[13] D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Designing and
managing the supply chain: concepts, strategies and case studies.
McGraw-Hill, 2003.
[14] S. Chopra, and P. Meindl, Supply chain management: strategy, planning
and operations. Prentice Hall, 2004.
[15] C. Hicks, T. McGovern, and C. F. Earl, "A typology of UK engineer-toorder
companies," Int. J. Logis. Res. and Appl., vol. 4, no. 1, pp. 43-56,
2001.
[16] C. Blum, and A. Roli, "Metaheuristics in combinatorial optimization:
Overview and conceptual comparison," ACM Comp. Surv., vol. 35, no.
3, pp. 268-308, 2003.
[17] D. C. Montgomery, Design and analysis of experiments. NY: John
Wiley and Sons, 2001.
[18] P. Pongcharoen, C. Hicks, P. M. Braiden, and D.J. Stewardson,
"Determining optimum genetic algorithm parameters for scheduling the
manufacturing and assembly of complex products," Int. J. Prod. Econ.,
vol. 78, no. 3, pp. 311-322, 2002.
[19] P. Pongcharoen, W. Chainate, and P. Thapatsuwan, "Exploration of
genetic parameters and operators through travelling salesman problem,"
ScienceAsia, vol. 33, no. 2, pp. 215-222, 2007.
[1] H. Gunnarsson, M. Ronnqvist, and J. T. Lundgren, "Supply chain
modelling of forest fuel," Eur. J. Oper. Res., vol. 158, no. 1, pp. 103-
123, 2004.
[2] R.Z. Farahani, and M. Elahipanah, "A genetic algorithm to optimize the
total cost and service level for just-in-time distribution in a supply
chain," Int. J. Prod. Econ., to be published.
[3] A. Syarif, Y. Yun, and M. Gen, "Study on multi-stage logistic chain
network: a spanning tree-based genetic algorithm approach," Comp. Ind.
Eng., vol. 43, no. 1-2, pp. 299-314, 2002.
[4] D. E. Goldberg, Genetic Algorithms in Search, Optimisation and
Machine Learning. Massachusetts, Addison-Wesley, 1989.
[5] M. Gen, and R. Cheng, Genetic Algorithms and Engineering Design.
New York, John Wiley and Sons, 1997.
[6] P. Pongcharoen, C. Hicks, and P. M. Braiden, "The development of
genetic algorithms for the finite capacity scheduling of complex
products, with multiple levels of product structure," Eur. J. Oper. Res.,
vol. 152, no. 1, pp. 215-225, 2004.
[7] C. Hicks, "A Genetic Algorithm tool for optimising cellular or
functional layouts in the capital goods industry," Int. J. Prod. Econ., vol.
104, no. 2, pp. 598-614, 2006.
[8] P. Pongcharoen, W. Promtet, P. Yenradee, and C. Hicks, "Stochastic
optimisation timetabling tool for university course scheduling," Int. J.
Prod. Econ., to be published.
[9] H. Aytug, M. Knouja, and F. E. Vergara, "Use of genetic algorithms to
solve production and operations management problems: a review," Int. J.
Prod. Res., vol. 41, no. 17, pp. 3955-4009, 2003.
[10] S. S. Chaudhry, and W. Luo, "Application of genetic algorithms in
production and operation management: a review," Int. J. Prod. Res., vol.
43, no. 19, pp. 4083-4101, 2005.
[11] L. Sun, Y. Zhang, and C. Jiang, "A matrix real-coded genetic algorithm
to the unit commitment problem," Elec. Pow. Syst. Res., vol. 76, no. 9-
10, pp. 716-728, 2006.
[12] P. Pongcharoen, D. J.Stewardson, C. Hicks, and P. M. Braiden,
"Applying designed experiments to optimize the performance of genetic
algorithms used for scheduling complex products in the capital goods
industry," J. Appl. Stat., vol. 28, no. 3-4, pp. 441-455, 2001.
[13] D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Designing and
managing the supply chain: concepts, strategies and case studies.
McGraw-Hill, 2003.
[14] S. Chopra, and P. Meindl, Supply chain management: strategy, planning
and operations. Prentice Hall, 2004.
[15] C. Hicks, T. McGovern, and C. F. Earl, "A typology of UK engineer-toorder
companies," Int. J. Logis. Res. and Appl., vol. 4, no. 1, pp. 43-56,
2001.
[16] C. Blum, and A. Roli, "Metaheuristics in combinatorial optimization:
Overview and conceptual comparison," ACM Comp. Surv., vol. 35, no.
3, pp. 268-308, 2003.
[17] D. C. Montgomery, Design and analysis of experiments. NY: John
Wiley and Sons, 2001.
[18] P. Pongcharoen, C. Hicks, P. M. Braiden, and D.J. Stewardson,
"Determining optimum genetic algorithm parameters for scheduling the
manufacturing and assembly of complex products," Int. J. Prod. Econ.,
vol. 78, no. 3, pp. 311-322, 2002.
[19] P. Pongcharoen, W. Chainate, and P. Thapatsuwan, "Exploration of
genetic parameters and operators through travelling salesman problem,"
ScienceAsia, vol. 33, no. 2, pp. 215-222, 2007.
@article{"International Journal of Business, Human and Social Sciences:64277", author = "Pupong Pongcharoen and Aphirak Khadwilard and Anothai Klakankhai", title = "Multi-matrix Real-coded Genetic Algorithm for Minimising Total Costs in Logistics Chain Network", abstract = "The importance of supply chain and logistics
management has been widely recognised. Effective management of
the supply chain can reduce costs and lead times and improve
responsiveness to changing customer demands. This paper proposes a
multi-matrix real-coded Generic Algorithm (MRGA) based
optimisation tool that minimises total costs associated within supply
chain logistics. According to finite capacity constraints of all parties
within the chain, Genetic Algorithm (GA) often produces infeasible
chromosomes during initialisation and evolution processes. In the
proposed algorithm, chromosome initialisation procedure, crossover
and mutation operations that always guarantee feasible solutions
were embedded. The proposed algorithm was tested using three sizes
of benchmarking dataset of logistic chain network, which are typical
of those faced by most global manufacturing companies. A half
fractional factorial design was carried out to investigate the influence
of alternative crossover and mutation operators by varying GA
parameters. The analysis of experimental results suggested that the
quality of solutions obtained is sensitive to the ways in which the
genetic parameters and operators are set.", keywords = "Genetic Algorithm, Logistics, Optimisation, Supply
Chain.", volume = "1", number = "11", pages = "768-6", }
