Model for inventory optimization using genetic algorithms
Keywords:
Genetic algorithms, Bullwhip effect, Supply chain optimization, BMN coefficients
Abstract
This paper presents the design of a genetic algorithm (GA) that optimizes inventory management in supply chains. They were considered warehousing, distribution, and manufacturing product costs, plus the cost of individual items to be ordered. The string used in the simulation contains 5 levels, being: customer, retail, warehouse, distributor and factory. The amounts of each pair were considered to be evaluated by the GA in the best chromosome. Additionally the BMN coefficients model was used to generate the evaluation function of chromosomes selected by the GA and satisfies the constraints considered in the model.
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References
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[9] H. Happ, «Optimal Power Dispatch», IEEE Transactions on Power Apparatus and Systems, vol. 93, no. 3, pp. 820-830, May. 1974.
[10] LanshunNie, XiaofeiXu, y Dechen Zhan, «Collaborative Planning in Supply Chains by Lagrangian Relaxation and Genetic Algorithms», en 2006 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006, pp. 7258-7262.
[11] P. Radhakrishnan, V. M. Prasad, y M. R. Gopalan, «Genetic Algorithm Based Inventory Optimization Analysis in Supply Chain Management», en 2009 IEEE International Advance Computing Conference, Patiala, India, 2009, pp. 418-422.
[12] J. Lu, P. Humphreys, R. McIvor, y L. Maguire, «Employing genetic algorithms to minimise the bullwhip effect in a supply chain», en 2007 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 2007, pp. 1527-1531.
[13] R. Perumalsamy y J. Natarajan, «Predictive analytics using Genetic Algorithm for efficient supply chain inventory optimization», in 2010 Second International conference on Computing, Communication and Networking Technologies, Karur, India, 2010, pp. 1-8.
[14] W. Jianhua y H. Xianfeng, «A hybrid genetic algorithm for agile supply chain scheduling optimization», en 2010 2nd International Conference on Future Computer and Communication, Wuhan, China, 2010, pp. V1-396-V1-400.
[15] N. P. Joseph y G. Radhamani, «Determining Robust Solutions in Supply Chain Using Genetic Algorithm», en 2010 International Conference on Data Storage and Data Engineering, Bangalore, India, 2010, pp. 275-277.
[2] J. Forrester, «Industrial Dynamics», Harvard Business Review. Boston, n. 36, Julio-Agosto 1958.
[3] L. H. Lee, V. Padmanabhan y S. Whang, «Information Distortion in a Supply Chain: The Bullwhip Effect», Management Science, Vol. 50, No. 12, Diciembre 2004.
[4] S. Chopra y P. Meindl, «Gerenciamento da Cadeia de Suprimentos – Estratégica, Planejamento e Operação», São Paulo: Prentice Hall, 2003.
[5] J. P. P. Dias, 2003, «Gestão dos estoques numa cadeia de distribuição com sistema de reposição automática e ambiente colaborativo», Disertación de Maestría – Programa de Post-graduación enIngenieríade Producción Escuela Politécnica de la Universidad de San Pablo.
[6] J. Lu, P. Humphreys, R. McIvor, y L. Maguire, «Employing Genetic Algorithms to minimise the Bullwhip Effect in an online efficient-responsive supply chain», en 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics, Chicago, IL, USA, 2009, pp. 117-122.
[7] E. Hill y W. Stevenson, «A New Method of Determining Loss Coefficients», IEEE Transactions on Power Apparatus and Systems, vol. 87, no. 7, pp. 1548-1553, Jul. 1968.
[8] G. Kumaran y V. S. R. K. Mouly, «Using evolutionary computation to solve the economic load dispatch problem», en Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, South Korea, pp. 296-301.
[9] H. Happ, «Optimal Power Dispatch», IEEE Transactions on Power Apparatus and Systems, vol. 93, no. 3, pp. 820-830, May. 1974.
[10] LanshunNie, XiaofeiXu, y Dechen Zhan, «Collaborative Planning in Supply Chains by Lagrangian Relaxation and Genetic Algorithms», en 2006 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006, pp. 7258-7262.
[11] P. Radhakrishnan, V. M. Prasad, y M. R. Gopalan, «Genetic Algorithm Based Inventory Optimization Analysis in Supply Chain Management», en 2009 IEEE International Advance Computing Conference, Patiala, India, 2009, pp. 418-422.
[12] J. Lu, P. Humphreys, R. McIvor, y L. Maguire, «Employing genetic algorithms to minimise the bullwhip effect in a supply chain», en 2007 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 2007, pp. 1527-1531.
[13] R. Perumalsamy y J. Natarajan, «Predictive analytics using Genetic Algorithm for efficient supply chain inventory optimization», in 2010 Second International conference on Computing, Communication and Networking Technologies, Karur, India, 2010, pp. 1-8.
[14] W. Jianhua y H. Xianfeng, «A hybrid genetic algorithm for agile supply chain scheduling optimization», en 2010 2nd International Conference on Future Computer and Communication, Wuhan, China, 2010, pp. V1-396-V1-400.
[15] N. P. Joseph y G. Radhamani, «Determining Robust Solutions in Supply Chain Using Genetic Algorithm», en 2010 International Conference on Data Storage and Data Engineering, Bangalore, India, 2010, pp. 275-277.
Published
2011-12-12
How to Cite
Valencia Niño, C., & Cáceres Quijano, S. (2011). Model for inventory optimization using genetic algorithms. ITECKNE, 8(2), 156-162. https://doi.org/https://doi.org/10.15332/iteckne.v8i2.2731
Issue
Section
Research and Innovation Articles
