Discounting Strategy in Two-Echelon Supply Chain with Random Demand and Random Yield | ||
| Journal of Quality Engineering and Production Optimization | ||
| مقاله 4، دوره 3، شماره 2، بهمن 2018، صفحه 51-62 اصل مقاله (885.27 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jqepo.2018.3133.1059 | ||
| نویسندگان | ||
| Marjan Zarea1؛ Maryam Esmaeili* 2؛ Mohsen Shaeyan3؛ Ramin Sadeghian4 | ||
| 1Department of Engineering Alzahra University Iran | ||
| 2Department of Engineering Alzahra University Iran | ||
| 3Graduate Student at Università di Bologna Italy | ||
| 4Department of Industrial Engineering, Tehran Payam Noor University, Tehran, Iran | ||
| چکیده | ||
| T This paper analyzes different pricing strategies in a two-echelon supply chain including one supplier and two retailers. The supplier and the retailers face random yield and random demand, respectively. Moreover, coordination or non-coordination of retailers in receiving the discount is investigated. Game theory is used to model and analyze the problems. The supplier as a leader of Stackelberg specifies quantity discount and an initial wholesale price. Then, retailers determine their optimal order quantity in which their profit is maximized. Finally, the supplier decides on the quantity of the input for production. Coordination of the retailers in receiving discount quantity enhances their profit and improves supply chain performance. However, the supplier gains more profit by escalating competition between customers/retailers. Numerical examples are shown to explain the results. | ||
| کلیدواژهها | ||
| Discount Strategy؛ Supply Chain؛ Coordination؛ Non-coordination | ||
| مراجع | ||
|
Baghalian, A., Rezapour, S., & Farahani, R. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. European Journal of Operational Research, 227(1), 199-215.
Blackhurst, J., Dunn, K., & Craighead, C. (2011). An empirically derived framework of global supply resiliency. Journal of Business Logistics, 32(4), 374-391.
Carvalho, H., Azevedo, S., & Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logistics Research, 4(1-2), 49-62.
Carvalho, H., Barroso, A., Machado, V., Azevedo, S., & Cruz-Machado, V. (2012). Supply chain redesign for resilience using simulation. Computers & Industrial Engineering, 62(1), 329-341.
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1-14.
Ehrenhuber, I., Treiblmaier, H., Nowitzki, C., & Gerschberger, M. (2015). Toward a framework for supply chain resilience. International Journal of Supply Chain and Operations Resilience, 1(4), 339.
Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2017). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets and Systems.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176-200.
Govindan, K., & Fattahi, M. (2017). Investigating risk and robustness measures for supply chain network design under demand uncertainty: a case study of glass supply chain. International Journal of Production Economics, 183, 680-699.
Hamidieh, A., & Fazli-Khalaf, M. (2017). A possibilistic reliable and responsive closed loop supply chain network design model under uncertainty. Journal of Advanced Manufacturing Systems, 16(04), 317-338.
Hamidieh, A., Naderi, B., Mohammadi, M., & Fazli-Khalaf, M. (2017). A robust possibilistic programming model for a responsive closed loop supply chain network design. Cogent Mathematics, 4(1).
Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: a parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52.
Hatefi, S., Jolai, F., Torabi, S., & Tavakkoli-Moghaddam, R. (2014). A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. International Journal of Computer Integrated Manufacturing, 28(6), 664-678.
Helm, P. (2015). Risk and resilience: strategies for security. Civil Engineering and Environmental Systems, 32(1-2), 100-118.
Iakovou, E., Vlachos, D., & Xanthopoulos, A. (2007). An analytical methodological framework for the optimal design of resilient supply chains. International Journal of Logistics Economics and Globalisation, 1(1), 1.
Ishfaq, R. (2012). Resilience through flexibility in transportation operations. International Journal of Logistics Research and Applications, 15(4), 215-229.
Jabbarzadeh, A., Fahimnia, B., & Rastegar, S. (2017). Green and resilient design of electricity supply chain networks: a multiobjective robust optimization approach. IEEE Transactions on Engineering Management, 1-21.
Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Management: An International Journal, 16(4), 246-259.
Klibi, W., & Martel, A. (2012). Modeling approaches for the design of resilient supply networks under disruptions. International Journal of Production Economics, 135(2), 882-898.
Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014). A model of resilient supply chain network design: a two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39-49.
Lee, C. (2014). Establishing a decision-making model of global supply chain risk management from the perspective of risk and vulnerability. International Journal of Supply Chain and Operations Resilience, 1(1), 28.
Liu, B., & Liu, Y. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions On Fuzzy Systems, 10(4), 445-450.
Manuj, I., & Mentzer, J. (2008). Global supply chain risk management. Journal of Business Logistics, 29(1), 133-155.
Mari, S., Lee, Y., & Memon, M. (2014). Sustainable and resilient supply chain network design under disruption risks. Sustainability, 6(10), 6666-6686.
Mohammaddust, F., Rezapour, S., Farahani, R., Mofidfar, M., & Hill, A. (2017). Developing lean and responsive supply chains: a robust model for alternative risk mitigation strategies in supply chain designs. International Journal of Production Economics, 183, 632-653.
Niknamfar, A., Niaki, S., & Pasandideh, S. (2014). Robust optimization approach for an aggregate production–distribution planning in a three-level supply chain. The International Journal of Advanced Manufacturing Technology, 76(1-4), 623-634.
Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, 37(4), 668-683.
Peidro, D., Mula, J., Poler, R., & Verdegay, J. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657.
Ponomarov, S., & Holcomb, M. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143.
Pishvaee, M., & Fazli Khalaf, M. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling, 40(1), 407-418.
Pishvaee, M., Razmi, J., & Torabi, S. (2012). Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets and Systems, 206, 1-20.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1-2), 328-344.
Salehi Sadghiani, N., Torabi, S., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95-114.
Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41(2), 259-269.
Sherafati, M., & Bashiri, M. (2016). Closed loop supply chain network design with fuzzy tactical decisions. Journal of Industrial Engineering International, 12(3), 255-269. http://dx.doi.org/10.1007/s40092-016-0140-3
Sodhi, M., Son, B., & Tang, C. (2011). Researchers’ perspectives on supply chain risk management. Production and Operations Management, 21(1), 1-13.
Tang, C. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics, 9(1), 33-45.
Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52(5), 639-657.
Yager, R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161.
Zhang, Y., & Huang, G. (2010). Fuzzy robust credibility-constrained programming for environmental management and planning. Journal of the Air & Waste Management Association, 60(6), 711-721.
Zhang, Y., Huang, G., Lin, Q., & Lu, H. (2012). Integer fuzzy credibility constrained programming for power system management. Energy, 38(1), 398-405.
Zhang, P., & Zhang, W. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets and Systems, 255, 74-91. | ||
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