Robust multi-market newsvendor models with interval demand data

• Published in: European journal of operational research 2011-07, Vol.212 (2), p.361-373
• Main Authors: Lin, Jun, Ng, Tsan Sheng
• Format: Article
• Language: English
• Subjects: Applied sciences; Approximation; Decision theory. Utility theory; Demand; Exact sciences and technology; Integer programming; Intervals; Inventory control, production control. Distribution; Marketing; Mathematical analysis; Mathematical models; Mathematical programming; Minimax regret; Minimax technique; Newsvendor problem; Operational research and scientific management; Operational research. Management science; Optimization algorithms; Order quantity; Risk analysis; Risk analysis Newsvendor problem Minimax regret Uncertainty modeling; Risk theory. Actuarial science; Studies; Uncertainty modeling
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2011.01.053

We present a robust model for determining the optimal order quantity and market selection for short-life-cycle products in a single period, newsvendor setting. Due to limited information about demand distribution in particular for short-life-cycle products, stochastic modeling approaches may not be suitable. We propose the minimax regret multi-market newsvendor model, where the demands are only known to be bounded within some given interval. In the basic version of the problem, a linear time solution method is developed. For the capacitated case, we establish some structural results to reduce the problem size, and then propose an approximation solution algorithm based on integer programming. Finally, we compare the performance of the proposed minimax regret model against the typical average-case and worst-case models. Our test results demonstrate that the proposed minimax regret model outperformed the average-case and worst-case models in terms of risk-related criteria and mean profit, respectively.