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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.07.049 Byline: Tsan-Ming (Jason) Choi (a), Duan Li (b), Houmin Yan (b) Keywords: Quick response policy; Inventory; Bayesian information updates; Supply chain management Abstract: In this paper we investigate the quick response (QR) policy with different Bayesian models. Under QR policy, a retailer can collect market information from the sales of a pre-seasonal product whose demand is closely related to a seasonal product's demand. This information is then used to update the distribution for the seasonal product's demand by a Bayesian approach. We study two information update models: one with the revision of an unknown mean, and the other with the revision of both an unknown mean and an unknown variance. The impacts of the information updates under both models are compared and discussed. We also identify the features of the pre-seasonal product which can bring more significant profit improvement. We conclude that an effective QR policy depends on a precise information update model as well as a selection of an appropriate pre-seasonal product as the observation target. Author Affiliation: (a) Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (b) Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Article History: Received 8 February 2002; Accepted 13 July 2004

An approach is developed to solve probabilistic linear programming problems with exponential random variables. The first step involves obtaining the probability density function (p.d.f.) of the linear combination of n independent exponential random variables. Probabilistic constraints are then transformed to the deterministic constraints using the p.d.f. The resulting non-linear deterministic model is then solved using a non-linear programming solution method.