The Robust Optimization in Centralized Supply Chain
Science Journal of Business and Management
Volume 4, Issue 2, April 2016, Pages: 61-66
Received: May 4, 2016; Published: May 5, 2016
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Li Chenlu, School of Management, Shanghai University, Shanghai, China
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Considering the inaccurate demand forecasting in supply chain, we introduce robust optimization to reduce uncertainty. The method is mainly to modify the probability distribution of the demand, in order to obtain a more accurate demand. A classical model and a corresponding robust model are established in the context of a fixed number of products offered by the supplier. As to calculation, we also propose the fast Fourier transform approach which greatly reduces the amount of computation. Finally, the process of robust optimization and improved algorithm are interpreted by numerical examples. The results show that the expected revenue of the robust model is lower. Because the method is conservative and robust.
Supply Chain, Robust Optimization, Demand Forecasting Uncertainty, Fast Fourier Transform Approach
To cite this article
Li Chenlu, The Robust Optimization in Centralized Supply Chain, Science Journal of Business and Management. Vol. 4, No. 2, 2016, pp. 61-66. doi: 10.11648/j.sjbm.20160402.16
Hallikas J, Virolainen V M, Tuominen M. Risk analysis and assessment in network environments: A dyadic case study. International Journal of Production Economics, vol. 78, pp. 45-55, 2002.
Huang Xiaoyuan, Yan Nina. Research Progress on Supply Chain Robustness. Chinese Journal of Management, vol. 4, pp. 521-528, 2007.
Willemain T R, Smart C N, Schwarz H F. A new approach to forecasting intermittent demand for service parts inventories. International Journal of forecasting, vol.20, pp. 375-387, 2004.
Aburto L, Weber R. Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, vol.7, pp. 136-144, 2007.
Fang F, Wong T N. Applying hybrid case-based reasoning in agent-based negotiations for supply chain management. Expert Systems with Applications, vol. 37, pp. 8322-8332, 2010.
Kou Yukun, Huang Mengxing, Chen Hongyu. Predicting Model of Supply Chain Demand Based on Petri Net and Agent System. Computer Engineering and Design, vol. 39, 2015.
Chen C L, Lee W C. Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, vol. 28, pp. 1131-1144, 2004.
Bertsimas D, Thiele A. A robust optimization approach to inventory theory. Operations Research, vol.54, pp. 150-168, 2006.
Perakis G, Sood A. Competitive multi-period pricing for perishable products: A robust optimization approach. Mathematical Programming, vol.107, pp. 295-335, 2006.
Yan Nina, Huang Xiaoyuan, Ma Longlong. Research on Robust Stochastic Optimization of Multi-Retailers Competition under Demand Uncertainty. Chinese Journal of Management Science, vol. 16, pp. 50-54, 2008.
Xu Jiawang, Huang Xiaoyuan, Guo Haifeng. Robust Optimization Model for Operating of Closed-loop Supply Chain with Uncertain Demands. Systems Engineering and Electronics, vol. 30, pp. 283-287, 2008.
Pan F, Nagi R. Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, vol. 37, pp. 668-683, 2010.
Li C, Liu S. A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment. Applied Mathematical Modelling, vol. 37(3), pp. 707-718, 2013.
Aouam T, Brahimi N. Integrated production planning and order acceptance under uncertainty: A robust optimization approach. European Journal of Operational Research, vol. 228(3), pp. 504-515, 2013.
Ait-Alla A, Teucke M, Lütjen M, et al. Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk. Mathematical Problems in Engineering, vol. 2014, 2014.
Melamed M, Ben-Tal A, Golany B. On the average performance of the adjustable RO and its use as an offline tool for multi-period production planning under uncertainty. Computational Management Science, pp. 1-23, 2016.
Carrizosa E, Olivares-Nadal A V, Ramírez-Cobo P. Robust newsvendor problem with autoregressive demand. Computers & Operations Research, vol. 68, pp. 123-133, 2016.
Golub G H, Van Loan C F. Matrix computations. Johns Hopkins University, Press, Baltimore, MD, USA, pp. 374-426, 1996.
Bertsekas D P. Nonlinear programming. 1999.
Birbil S I, Frenk J B G, Gromicho J A S, et al. The role of robust optimization in single-leg airline revenue management. Management Science, vol.55, pp. 148-163, 2009.
Ben-Tal A, Nemirovski A. Robust convex optimization. Mathematics of Operations Research, vol. 23, pp. 769-805, 1998.
Bertsimas D, Sim M. Robust discrete optimization and network flows. Mathematical programming, vol. 98, pp. 49-71, 2003.
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