Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty
Automation, Control and Intelligent Systems
Volume 3, Issue 6, December 2015, Pages: 112-117
Received: Dec. 20, 2015;
Published: Dec. 21, 2015
Views 3591 Downloads 83
De Gu, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China
Jishuai Wang, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
This paper presents a methodology to include financial risk management for the design of multiproduct, multi-echelon supply chain networks under uncertainty. The method is in the framework of two-stage stochastic programming. Definitions of financial risk and downside risk are adapted. Using these definitions, financial risk management constraints are introduced and a new two-stage stochastic programming model is established. Case studies illustrate the applicability of such financial risk management. Trade-offs between expected cost and risk are also analyzed.
Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty, Automation, Control and Intelligent Systems.
Vol. 3, No. 6,
2015, pp. 112-117.
D. J. Garcia, F. You, Supply chain design and optimization: Challenges and opportunities. Computers & Chemical Engineering, 2015, 81: 153-70.
P. Tsiakis, N. Shah, C. C. Pantelides, Design of multi-echelon supply chain networks under demand uncertainty. Industrial & Engineering Chemistry Research, 2001, 40 (16): 3585-3604.
T. Santoso, S. Ahmed, M. Goetschalckx, A. Shapiro, A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 2005, 167 (1): 96-115.
A. M. Geoffrion, G. W. Graves, Multi-commodity distribution system design by benders decomposition. Management Science, 1974, 20: 822-844.
C. H. Aikens, facility location models for distribution planning. European Journal of Operational Research, 1985, 22 (3): 263-279.
C. J. Vidal, M. Goetschalckx, Strategic production-distribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research, 1997, 98 (1): 1-18.
T. Assavapokee, W. Wongthatsanekorn, Reverse production system infrastructure design for electronic products in the state of Texas. Computers & Industrial Engineering, 2012, 62(1): 129-140.
D. C. Cafaro, I. E. Grossmann, Strategic planning, design, and development of the shale gas supply chain network. AIChE Journal, 2014, 60(6): 2122-2142.
M. A. Kalaitzidou, P. Longinidis, P. Tsiakis, M. C. Georgiadis, Optimal Design of Multiechelon Supply Chain Networks with Generalized Production and Warehousing Nodes. Industrial & Engineering Chemistry Research, 2014, 53(33): 13125-13138.
I. Heckmann, T. Comes, S. Nickel, A critical review on supply chain risk–Definition, measure and modeling. Omega, 2015, 52: 119-132.
M. Talaei, B. F. Moghaddam, M. S. Pishvaee, A. Bozorgi-Amiri, S. Gholamnejad, A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 2015.
G. Cairns, P. Goodwin, G. Wright, A decision-analysis-based framework for analysing stakeholder behaviour in scenario planning. European Journal of Operational Research, 2016, 249(3): 1050-1062.
A. Barbaro, M. J. Bagajewicz, Managing financial risk in planning under uncertainty. AIChE Journal, 2004, 50 (5): 963-989.
F. You, J. M. Wassick, I. E. Grossmann, Risk management for a global supply chain planing uncder uncertainty: modes and algorithms. AIChE Journal, 2009, 55: 931-946.
S. R. Cardoso, A. P. Barbosa-Póvoa, S. Relvas. Integrating Financial Risk Measures into the Design and Planning of Closed-loop Supply Chains. Computers & Chemical Engineering, 2016, 85: 105-123.