Supplier Selection in Three Echelon Supply Chain & Vendor Managed Inventory Model Under Price Dependent Demand Condition

Document Type: Research Paper

Authors

1 Department of industrial engineering, PNU University, Tehran, Iran

2 Department of Industrial Engineering Bu Ali Sina University, Hamadan, Iran

3 Department of Industrial Engineering PNU University, Isfahan, Iran

Abstract

This paper, considers the supplier selection in three echelon supply chain with Vendor Managed Inventory (VMI) strategy under price dependent demand condition. As there is a lack of study on the supplier selection in VMI literature, this paper presents a VMI model in supply chain including multi supplier, one distributer and multi retailer that distributer selects suppliers. Two class models (traditional vs. VMI) are presented and we compare them to study the impact of VMI on supply chain and supplier selection. As the proposed model is a NP-hard problem, a meta-heuristics namely Harmony Search is employed to optimize the proposed models. We show that how the VMI system can effect on supplier selection and can change the set of selected suppliers. Finally the conclusion and further studies are presented

Keywords

Main Subjects


Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, Vol. 51(1), pp. 196-215. 

Amin, S. H., Razmi, J., & Zhang, G. (2011). Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. Expert Systems with Applications, Vol. 38(1), pp. 334-342.

Bandyopadhyay, S., & Bhattacharya, R. (2014). Solving a tri-objective supply chain problem with modified NSGA-II algorithm. Journal of Manufacturing Systems, Vol. 33(1), pp. 41-50. 

Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., & Tap, M. B. M. (2014). Supplier Selection: A Fuzzy-ANP Approach. Procedia Computer Science, Vol. 31, pp. 691-700. 

David Simchi-levi, P. K., Edith Simchi-levi , Shankar. R.,. (2007). Designing and managing supply chain: concepts, strategies and case studies. New York: McGraw-Hill.

Deshmukh, A., & Chaudhari, A. (2011). A Review for Supplier Selection Criteria and Methods. In K. Shah, V. R. Lakshmi Gorty & A. Phirke (Eds.), Technology Systems and Management, Vol. 145, pp. 283-291.

Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, Vol. 344(2–3), pp. 243-278. 

Esmaeili Aliabadi, D., Kaazemi, A., & Pourghannad, B. (2013). A two-level GA to solve an integrated multi-item supplier selection model. Applied Mathematics and Computation, Vol. 219(14), pp. 7600-7615.

Geem, Z. W., Kim, n. A. J. H., Loganathan, n. A., & G.V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION.

Guan, R., & Zhao, X. (2010). On contracts for VMI program with continuous review (r, Q) policy. European Journal of Operational Research, Vol. 207(2), pp. 656-667. 

Holmström, J. (1998). Business process innovation in the supply chain – a case study of implementing vendor managed inventory. European Journal of Purchasing & Supply Management, Vol. 4(2–3), pp. 127-131. 

Jadidi, O., Cavalieri, S., & Zolfaghari, S. (2015). An improved multi-choice goal programming approach for supplier selection problems. Applied Mathematical Modelling, Vol. 39(14), pp. 4213-4222. 

Kuo, R. J., Pai, C. M., Lin, R. H., & Chu, H. C. (2015). The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation. Applied Mathematics and Computation, Vol. 250, pp. 958-972. 

Lim, A., & Zhu, W. (2006). A Fast and Effective Insertion Algorithm for Multi-depot Vehicle Routing Problem with Fixed Distribution of Vehicles and a New Simulated Annealing Approach. In M. Ali & R. Dapoigny (Eds.), Advances in Applied Artificial Intelligence: 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, June 27-30, 2006. Proceedings (pp. 282-291). Berlin, Heidelberg: Springer Berlin Heidelberg.

Mafakheri, F., Breton, M., & Ghoniem, A. (2011). Supplier selection-order allocation: A two-stage multiple criteria dynamic programming approach. International Journal of Production Economics, Vol. 132(1), pp. 52-57. 

Mani, V., Agrawal, R., & Sharma, V. (2014). Supplier selection using social sustainability: AHP based approach in India. International Strategic Management Review, Vol. 2(2), pp. 98-112. 

Nachiappan, S. P., & Jawahar, N. (2007). A genetic algorithm for optimal operating parameters of VMI system in a two-echelon supply chain. European Journal of Operational Research,Vol. 182(3), pp. 1433-1452. 

Naso, D., Surico, M., Turchiano, B., & Kaymak, U. (2007). Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete. European Journal of Operational Research, Vol. 177(3), pp. 2069-2099. 

Sadeghi, J., Taghizadeh, M., Sadeghi, A., Jahangard, R., & Tavakkoli-Moghaddam, R. (2014). Optimizing a vendor managed inventory (VMI) model considering delivering cost in a three-echelon supply chain using two tuned-parameter meta-heuristics. International Journal of System Assurance Engineering and Management, pp. 1-11. 

Silva, C. A., Sousa, J. M. C., Runkler, T. A., & Sá da Costa, J. M. G. (2009). Distributed supply chain management using ant colony optimization. European Journal of Operational Research, Vol. 199(2), pp. 349-358. 

Taleizadeh, A. A., Niaki, S. T. A., Shafii, N., Meibodi, R. G., & Jabbarzadeh, A. (2010). A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain. The International Journal of Advanced Manufacturing Technology, Vol. 51(9), pp. 1209-1223.

Veni, K. K., Rajesh, R., & Pugazhendhi, S. (2012). Development of Decision Making Model Using Integrated AHP and DEA for Vendor Selection. Procedia Engineering, Vol. 38, pp. 3700-3708. 

Wang, X. (2011, 8-11 Jan. 2011). Inventory decision for stock-level-dependent demand items with and without VMI. Paper presented at the Management Science and Industrial Engineering (MSIE), 2011 International Conference on.

Ware, N. R., Singh, S. P., & Banwet, D. K. (2014). A mixed-integer non-linear program to model dynamic supplier selection problem. Expert Systems with Applications, Vol. 41(2), pp. 671-678. 

Yang, P. C., Wee, H. M., Pai, S., & Tseng, Y. F. (2011). Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm. Expert Systems with Applications, Vol. 38(12), pp. 14773-14777. 

Yang, X.-S. (2009). Harmony Search as a Metaheuristic Algorithm. In Z. W. Geem (Ed.), Music-Inspired Harmony Search Algorithm: Theory and Applications (pp. 1-14). Berlin, Heidelberg: Springer Berlin Heidelberg.

Yao, Y., Evers, P. T., & Dresner, M. E. (2007). Supply chain integration in vendor-managed inventory. Decision Support Systems, Vol. 43(2), pp. 663-674.

Yu, Y., Chu, F., & Chen, H. (2009). A Stackelberg game and its improvement in a VMI system with a manufacturing vendor. European Journal of Operational Research, Vol. 192(3), pp. 929-948.  

Yu, Y., Hong, Z., Zhang, L. L., Liang, L., & Chu, C. (2013). Optimal selection of retailers for a manufacturing vendor in a vendor managed inventory system. European Journal of Operational Research, Vol. 225(2), pp. 273-284. 

Yu, Y., & Huang, G. Q. (2010). Nash game model for optimizing market strategies, configuration of platform products in a Vendor Managed Inventory (VMI) supply chain for a product family. European Journal of Operational Research, Vol. 206(2), pp. 361-373. 

Yu, Y., Huang, G. Q., & Liang, L. (2009). Stackelberg game-theoretic model for optimizing advertising, pricing and inventory policies in vendor managed inventory (VMI) production supply chains. Computers & Industrial Engineering, Vol. 57(1), pp. 368-382. 

Yu, Y., Wang, Z., & Liang, L. (2012). A vendor managed inventory supply chain with deteriorating raw materials and products. International Journal of Production Economics, Vol. 136(2), pp. 266-274.