Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/137181


Title: 供需相依不確定下的雙重採購: 隨機規劃分析
Dual Sourcing under Interdependent Demand and Supply Uncertainties: A Two-Stage Stochastic Programming Model
Authors: 李瑞文
Lee, Jui-Wen
Contributors: 莊皓鈞
彭朱如

李瑞文
Lee, Jui-Wen
Keywords: 相依不確定性
雙重採購
隨機規劃
情境樹
供給鏈
Interdependent uncertainties
Dual sourcing
Stochastic programming
Scenario tree
Supply chain
Date: 2021
Issue Date: 2021-09-02 18:20:45 (UTC+8)
Abstract: 原料供應商與終端顧客需求的不確定性一直是製造商在採購中的重要風險來源,許多的研究致力於透過數學規劃模型輔助不確定性下的決策,但多數的模型忽略供應鏈中的不確定性彼此間的互相影響。本研究以雙重採購為基礎,建構兩階段的隨機規劃模型,將相依不確定性可能發生的所有情境納入模型計算。在第一階段,根據估計的市場需求、供給到貨率進行事前採購,廠商可將訂單分配給較可靠的昂貴供應商和一般的便宜供應商;第二階段,再根據實現的需求與供貨資訊,決定追加訂單與否。經過分析,我們發現採購價格和缺貨風險這兩樣變數左右了兩家供應商的採購量分配;此外,市場波動、異常供貨風險越高則相依性模型的效益更為顯著,但可靠供應商的採購成本若過高,則會造成避險的效益遞減。根據研究結果,此基於情境樹的隨機規劃模型能協助管理者趨避缺貨風險,相較於獨立假設,我們發現考量相依不確定性的規劃模型於雙重採購下的成本更低,且面對預測誤差的波動也更為穩健。
In procurement, uncertainties in end product demand and components availability have always been unneglectable source of risk. So far, a number of researchers have dedicated to handle procurement decisions under uncertainty with mathematical programming models; nevertheless, the impact of interdependent demand and supply is seldom explored. In this research, based on dual sourcing framework, a two-stage stochastic programming model is proposed in which pre-orders are placed to both unreliable and reliable suppliers during the first stage, and in the second stage, additional orders could be placed after stochastic elements are realized. The numerical experiments indicate that procurement cost and the risk of shortage are critical variables in order allocation decisions. Moreover, the value of interdependency is more significant as risk of shortage surges, but the results also reveal that ascending cost of reliable supplier might offset such benefit. According to the analysis, our tree-based stochastic model outperforms the model which assumes independent demand and supply uncertainty in terms of managing risk and minimizing cost; furthermore, the robustness under forecast error volatility is validated.
Reference: Higle, J. L. (2005). Stochastic Programming: Optimization When Uncertainty Matters. Emerging Theory, Methods, and Applications, 30–53.

Kaki, A., Salo, A., & Talluri, S. (2014). Scenario-Based Modeling of Interdependent Demand and Supply Uncertainties. IEEE Transactions on Engineering Management, 61(1), 101–113.

Kazemi Zanjani, M., Nourelfath, M., & Ait-Kadi, D. (2009). A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand. International Journal of Production Research, 48(16), 4701–4723.

Kempf, K. G., Erhun, F., Hertzler, E. F., Rosenberg, T. R., & Peng, C. (2013). Optimizing Capital Investment Decisions at Intel Corporation. Interfaces, 43(1), 62–78.

Khakdaman, M., Wong, K. Y., Zohoori, B., Tiwari, M. K., & Merkert, R. (2014). Tactical production planning in a hybrid Make-to-Stock–Make-to-Order environment under supply, process and demand uncertainties: a robust optimisation model. International Journal of Production Research, 53(5), 1358–1386.

Kouvelis, P., Chambers, C., & Wang, H. (2009). Supply Chain Management Research and Production and Operations Management: Review, Trends, and Opportunities. Production and Operations Management, 15(3), 449–469.

Li, Z., Ryan, J. K., Shao, L., & Sun, D. (2014). Supply Contract Design for Competing Heterogeneous Suppliers under Asymmetric Information. Production and Operations Management, 24(5), 791–807.

Li, T., Sethi, S. P., & Zhang, J. (2012). Supply Diversification with Responsive Pricing. Production and Operations Management, 22(2), 447–458.

Mardan, E., Amalnik, M. S., & Rabbani, M. (2015). An integrated emergency ordering and production planning optimization model with demand and yield uncertainty. International Journal of Production Research, 53(20), 6023–6039.

Simangunsong, E., Hendry, L. C., & Stevenson, M. (2012). Supply-chain uncertainty: a review and theoretical foundation for future research. International Journal of Production Research, 50(16), 4493–4523.

Sodhi, M. S., Son, B.-G., & Tang, C. S. (2011). Researchers’ Perspectives on Supply Chain Risk Management. Production and Operations Management, 21(1), 1–13.

Tang, S. Y., & Kouvelis, P. (2011). Supplier Diversification Strategies in the Presence of Yield Uncertainty and Buyer Competition. Manufacturing & Service Operations Management, 13(4), 439–451.

Tomlin, B. (2006). On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks. Management Science, 52(5), 639–657.

Wilding, R. (1998). The supply chain complexity triangle: Uncertainty generation in the supply chain. International Journal of Physical Distribution & Logistics Management, 28(8), 599–616.

Xu, B., Zhong, P.-A., Zambon, R. C., Zhao, Y., & Yeh, W. W.-G. (2015). Scenario tree reduction in stochastic programming with recourse for hydropower operations. Water Resources Research, 51(8), 6359–6380.

Yu, H., Zeng, A. Z., & Zhao, L. (2009). Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega, 37(4), 788–800.
Description: 碩士
國立政治大學
企業管理研究所(MBA學位學程)
108363074
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108363074
Data Type: thesis
Appears in Collections:[企業管理研究所(MBA學位學程)] 學位論文

Files in This Item:

File Description SizeFormat
307401.pdf2658KbAdobe PDF0View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing