Pricing and Inventory for a Supply Chain with Perishable and Substitutable Products

Document Type : Research Paper

Authors

Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Decentralization allows channel members to make their decisions, independently. In this decentralized channels, members may have different power to make its decisions. Hence, decisions of each member may influence on the other members’ decisions. To model such situations, bi-level programming models are useful. In this study, a supply chain including one manufacturer and multiple competitive retailers are considered. The manufacturer produces several perishable and substitutable products. The problem is formulated as a multi-follower bi-level programming model. Since bi-level models are often NP-Hard, simulated annealing algorithm is applied to solve the proposed model. The numerical results of sensitivity analysis indicate that in the larger markets, even increasing in the prices leads to increase the profit of members. Also, when the consumers are price-sensitive, the manufacturer and the retailers must decrease their prices to attract the consumers and increase their profits, consequently.

Keywords

Main Subjects

References

  1. Huang, Y., Huang, G.Q. and Newman, S.T. (2011). “Coordinating pricing and inventory decisions in a multi-level supply chain: a game-theoretic approach”. Transportation Research part E, Vol. 47, PP. 115–129.
  2. Xie, J. and Wei, J. (2009). “Coordinating advertising and pricing in a manufacturer–retailer channel”. European Journal of Operational Research, Vol. 197, PP. 785–791.
  3. Shy, O. (2008). How to price: a guide to pricing techniques and yield management. Cambridge University Press, New York.
  4. Jia, J., and Hu, Q. (2011). Dynamic ordering and pricing for a perishable goods supply chain. Computers & Industrial Engineering, Vol. 60, PP. 302–309.
  5. EI-Ansary, A.L. and Stern, L.W. (1972). “Power measurement in the distribution channel”. Journal of Marketing Research, Vol. 9, No. 1, PP. 47–52.
  6. Gallego, G., and van Ryzin, G. (1994). “Optimal dynamic pricing of inventories with stochastic demand over finite horizons”. Management Science, Vol. 40, No. 2, PP. 999–1020.
  7. Chew, E.P., Lee, C. and Liu, R. (2009). “Joint inventory allocation and pricing decisions for perishable products”. International Journal of Production Economics, Vol. 120, PP. 139–150.
  8. Chew, E.P., Lee, C., Liu, R., Hong, K.S. and Zhang, A. (2014). “Optimal dynamic pricing and ordering decisions for perishable products”. International Journal of Production Economics, (Article in Press)
  9. Chan, L.M.A., Shen, Z.J.M., Simchi-Levi, D. and Swann, J. (2004). “Coordination of pricing and inventory decisions: a survey and classification”. In: Simchi-Levi, D., Wu, S.D. and Shen, Z.J.M. (eds.) Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era, Kluwer Academic Publishers, PP. 335-392.
  10. Chen, X. and Simchi-Levi, D. (2012). “Pricing and inventory management”. In: Ӧzer, Ӧ. and Philips, R. (eds.) The Oxford Handbook of Pricing Management, 1st edn. Oxford University press, PP. 784-824.
  11. Hsieh, C.C. and Wu, C.H. (2008). “Capacity allocation, ordering, and pricing decisions in a supply chain with demand and supply uncertainties”. European Journal of Operational Research, Vol. 184, PP. 667–684.
  12. Wu, C.H., Chen, C.W. and Hsieh, C.C. (2012). “Competitive pricing decisions in a two-echelon supply chain with horizontal and vertical competition”. International Journal of Production Economics, Vol. 135, PP. 265–274.
  13. Zhao, J., Tang, W. and Wei, J. (2012). “Pricing decision for substitutable products with retail competition in a fuzzy environment”. International Journal of Production Economics, Vol. 135, PP. 144–153.
  14. Gao, Y., Zhang, G., Lu, J. and We, H.M. (2011). “Particle swarm optimization for bi-level pricing problems in supply chains. Journal of Global Optimization, Vol. 51, PP. 245-254.
  15. Mokhlesian, M. and Zegordi, S.H. (2014). “Application of multidivisional bi-level programming to coordinate pricing and inventory decisions in a multi-product competitive supply chain”.The International Journal of Advanced Manufacturing Technology, Vol. 71, PP. 1975-1989.
  16. Giraud-Héraud, E., Hammoudi, H. and Mokrane, M. (2003). “Multiproduct firm behaviour in a differentiated market”. Can. J. Econ., Vol. 36, No. 1, PP. 41–61.
  17. Xie, J. and Wei, J.C. (2009). “Coordinating advertising and pricing in a manufacturer–retailer channel”. European Journal of Operational Research, Vol. 197, PP. 785–791.
  18. Esmaeili. M., Aryanezhad, M.B. and Zeephongeskul,.P. (2009). “A game theory approach in seller–buyer supply chain”. EuropeanJournalofOperationalResearch, Vol. 195, PP. 442-448.
  19. Transchel, S. and Minner, S. (2009). “The impact of dynamic pricing on the economic order decision”. European Journal of Operational Research, Vol. 198, PP. 773–789.
  20. Ho, C.H., Ouyang, L.Y. and Su, C.H. (2008). “Optimal pricing, shipment and payment policy for an integrated supplier–buyer inventory model with two-part trade credit”. European Journal of Operational Research, Vol. 187, PP. 496–510.
  21. Ha, A.Y., Li, L. and Ng, S.M. (2003). “Price and delivery logistics competition in a supply chain”. Management Science, Vol. 49, No. 9, PP. 1139-1153.
  22. Guan, R. and Zhao, X. (2011). “Pricing and inventory management in a system with multiple competing retailers under (r,Q) policies”. Computers & Operations Research, Vol. 38, No. 9, PP. 1294-1304.
  23. Shi, J., Zhang, G. and Sha, J. (2011). “Jointly pricing and ordering for a multi-product multi-constraint newsvendor problem with supplier quantity discounts”. Applied Mathematical Modelling, Vol. 35, PP. 3001–3011.
  24. Gurnani, H., Erkoc, M. and Luo, Y. (2007). “Impact of product pricing and timing of investment decisions on supply chain co-opetition”. European Journal of Operational Research, Vol. 180, PP. 228–248.
  25. Chun, Y.H. (2003). “Optimal pricing and ordering policies for perishable commodities”. European Journal of Operational Research, Vol. 144, PP. 68–82.
  26. Kuiper, W.E. and Meulenberg, M.T.G. (2004). “Price leadership within a marketing channel: A cointegration study”. Intern. J. of Research in Marketing, Vol. 21, PP. 137–158.
  27. Xia, Y. (2011). “Competitive strategies and market segmentation for suppliers with substitutable products”. European Journal of Operational Research, Vol. 210, No. 2, PP. 194-203.
  28. Edirisinghe, N.C.P., Bichescu, B. and Shi, X. (2011). “Equilibrium analysis of supply chain structures under power imbalance”. European Journal of Operational Research, Vol. 214, No. 3, PP. 568-578.
  29. Serel, D.A. (2008). “Inventory and pricing decisions in a single-period problem involving risky supply”. International Journal of Production Economics, Vol. 116, No. 1, PP. 115-128.
  30. Sajadieh, M. and Akbari Jokar, M. (2009). “Optimizing shipment, ordering and pricing policies in a two-stage supply chain with price-sensitive demand”. Transportation Research Part E, Vol. 45, PP. 564–571.
  31. Ray, S., Gerchak, Y. and Jewkes, E.M. (2005). “Joint pricing and inventory policies for make-to-stock products with deterministic price-sensitive demand”. Int. J. Production Economics, Vol. 97, PP. 143–158.
  32. Lakhal, S.Y. (2006). “An operational profit sharing and transfer pricing model for network-manufacturing companies”. European Journal of Operational Research, Vol. 175, PP. 543–565.
  33. Boyacı, T. and Gallego, G. (2002). “Coordinating pricing and inventory replenishment policies for one wholesaler and one or more geographically dispersed retailers”. Int. J. Production Economics, Vol. 77, PP. 95–111.
  34. Colson, B., Marcotte, P. and Savard, G. (2005). “Bilevel programming: A survey”. 4OR, Vol. 3, PP. 87–107.
  35. Rajesh, J., Gupta, K., Kusumakar, H.S., Jayaraman, V.K. and Kulkarni, B.D. (2003). “A tabu search based approach for solving a class of bilevel programming problems in chemical engineering”. Journal of Heuristics, Vol. 9, PP. 307–319.
  36. Wang, G., Wang, X., Wan, Z. and Jia, S. (2007). “An adaptive genetic algorithm for solving bilevel linear programming problem”. Applied Mathematics and Mechanics (English Edition), Vol. 28, No. 12, PP. 1605–1612.
  37. Wang, G., Wan, Z., Wang, X. and Fang, D. (2007). “Genetic algorithm for solving quadratic bilevel programming problem”. Wuhan University Journal of Natural Sciences, Vol. 12, No. 3, PP. 421-425.
  38. Kuo, R.J. and Huang, C.C. (2009). “Application of particle swarm optimization algorithm for solving bi-level linear programming problem”. Computers and Mathematics with Applications, Vol. 58, PP. 678-685.
  39. Talbi, E.G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons, Inc., Hoboken, New Jersey.
  40. Koulamas, C., Antony, S.R. and Jaen, R. (1994). “A Survey of Simulated Annealing Applications to Operations Research Problems”. Omega, Vol. 22, No. 1, PP. 41-56.
  41. Sahni, S. (1974). “Computationally related problems”. SIAM Journal on Computing, Vol. 3, PP. 262–279.

 

Advances in Industrial Engineering
Volume 49, Issue 2 - Serial Number 2
پاییز و زمستان
October 2015
Pages 185-197

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