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A new modified randomly iterated search and statistical competency (RISC) approach for solving a nonlinear controlling model in three-level supply chain

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Abstract

This paper proposes a nonlinear controlling model in three-level supply chain. The proposed model consists of a manufacturer, a warehouse and two retailers. As nonlinear multi level programming problems are much more difficult to solve, the proposed model was converted to two nonlinear bilevel programming problems in order to make the model easier both to solve and to describe. The first model consists of warehouse's objective function at its first level and the manufacturer at the second level. In the second model, retailers are the leader and warehouse is the follower. Bilevel programming has been investigated to be NP-hard problem. Numerous algorithms have been developed so far for solving bilevel programming problem; however, algorithm proposed in this paper is easier than other algorithms for solving this type of problems. In this paper, we proposed a modified randomly iterated search and statistical competency approach in genetic algorithm to provide precise and reliable optimal solutions for manufacturer, warehouse and retailers' value in situations with no empirical observations and we presented confidence interval as solution.

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References

  1. Erenguc S, Simpson N (1999) Integrated production/distribution planning in supply chains: an invited review. Eur J Oper Res 115(2):219–236

    Article  Google Scholar 

  2. Sarimveisa H, Patrinosa P, Tarantilisb CD, Kiranoudisa CT (2008) Dynamic modeling and control of supply chain systems: a review. Comp Oper Res 35(11):3530–3561

    Article  Google Scholar 

  3. Li H, Wang Y (2007) A hybrid genetic algorithm for solving nonlinear programming problems with applied simplex method. ICNC '07 Proceedings of the Third International Conference on Natural Computation—IEEE Computer Society Washington, DC, USA, Volume 04, Pages 91–95

  4. Deb K, Sinha A (2008) Solving bilevel multi-objective optimization problems using evolutionary algorithms. Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization. Springer, Heidelberg, pp 110–124

    Google Scholar 

  5. Calvete HI, Galé C, Mateo PM (2009) A genetic algorithm for solving linear fractional bilevel problems. Ann Oper Res Springer Sci 166(1):39–56

    Article  MATH  Google Scholar 

  6. Hejazi SR, Memariani A, Jahanshahloo G, Sepehri MM (2002) Linear bilevel programming solution by genetic algorithm. Comput Oper Res 29(31):1913–1925

    Article  MATH  MathSciNet  Google Scholar 

  7. Roghanian E, Sadjadi SJ, Aryanezhad MB (2007) A probabilistic bi-level linear multi-objective programming problem to supply chain planning. Appl Math Comput 188:786–800

    Article  MATH  MathSciNet  Google Scholar 

  8. Shirzaei M, Walter TR (2009) Randomly iterated search and statistical competency as powerful inversion tools for deformation source modeling: application to volcano interferometric synthetic aperture radar data. J Geophys Res. doi:10.1029/2008JB006071

    Google Scholar 

  9. Lan H, Liu Z, Li R, Wang R (2010) Bi-level programming model for inventory control of deteriorating items based on VMI. Log Sys Intel Manage. doi:10.1109/ICLSIM.2010.5461062

    Google Scholar 

  10. Lu Z, Sun Y-c (2009) Point estimation optimization model of life distribution parameters based on genetic algorithm. Second International Conference on Information and Computing Science

  11. Long-ying HU, Yong-qi C, Zhang-sheng J (2007) Research on the coordination mechanism model of the tree-level supply chain. Manag Sci Eng. doi:10.1109/ICMSE.2007.4421933

    MATH  Google Scholar 

  12. Lee ES, Shih H-S (2001) Fuzzy and multi-level decision making. Springer, New York. ISBN 978-1-4471-1177-1

    Book  Google Scholar 

  13. Park S, Lee T-E, Sung CS (2010) A three-level supply chain network design model with risk-pooling and lead times. Elsevier Transp Res E 46:563–581

    Article  Google Scholar 

  14. Monthatipkul C, Yenradee P (2008) Inventory/distribution control system in a one-warehouse/multi-retailer supply chain. Elsevier Int J Prod Econ 114:119–133

    Article  Google Scholar 

  15. Huijun S, Ziyou G, Jianjun W (2008) A bi-level programming model and solution algorithm for the location of logistics distribution centers. Elsevier Appl Math Model 32:610–616

    Article  MATH  Google Scholar 

  16. Gümüs ZH, Floudas CA (2001) Global optimization of nonlinear bi-level programming problems. J Glob Optim 20:1–31

    Article  MATH  Google Scholar 

  17. Michalewicz Z (1995) Genetic algorithms, numerical optimization, and constraints. Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 151–158

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Correspondence to Samira Shirzaee.

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Shirzaee, S., Shahanaghi, K. & Shirzaee, M.R. A new modified randomly iterated search and statistical competency (RISC) approach for solving a nonlinear controlling model in three-level supply chain. Int J Adv Manuf Technol 70, 1023–1031 (2014). https://doi.org/10.1007/s00170-013-5316-z

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  • DOI: https://doi.org/10.1007/s00170-013-5316-z

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