Technical note – operational statistics: Properties and the risk‐averse case |
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| Authors: | Mengshi Lu J George Shanthikumar Zuo‐Jun Max Shen |
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| Affiliation: | 1. Krannert School of Management, Purdue University, West Lafayette, Indiana;2. Department of Industrial Engineering and Operations Research, Department of Civil and Environmental Engineering, University of California, Berkeley, California |
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| Abstract: | Consider a repeated newsvendor problem for managing the inventory of perishable products. When the parameter of the demand distribution is unknown, it has been shown that the traditional separated estimation and optimization (SEO) approach could lead to suboptimality. To address this issue, an integrated approach called operational statistics (OS) was developed by Chu et al., Oper Res Lett 36 (2008) 110–116. In this note, we first study the properties of this approach and compare its performance with that of the traditional SEO approach. It is shown that OS is consistent and superior to SEO. The benefit of using OS is larger when the demand variability is higher. We then generalize OS to the risk‐averse case under the conditional value‐at‐risk (CVaR) criterion. To model risk from both demand sampling and future demand uncertainty, we introduce a new criterion, called the total CVaR, and find the optimal OS under this new criterion. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 206–214, 2015 |
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| Keywords: | parameter uncertainty operational statistics risk‐aversion |
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