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Joint Inventory and Pricing Coordination with Incomplete Demand Information
Author(s) -
Lu Ye,
Song Miao,
Yang Yi
Publication year - 2016
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12504
Subject(s) - convexity , revenue , generalization , complete information , computer science , microeconomics , joint (building) , upper and lower bounds , stock (firearms) , inventory control , economics , operations research , mathematical optimization , operations management , mathematics , finance , architectural engineering , mechanical engineering , mathematical analysis , engineering
In retailing operations, retailers face the challenge of incomplete demand information. We develop a new concept named K ‐approximate convexity, which is shown to be a generalization of K ‐convexity, to address this challenge. This idea is applied to obtain a base‐stock list‐price policy for the joint inventory and pricing control problem with incomplete demand information and even non‐concave revenue function. A worst‐case performance bound of the policy is established. In a numerical study where demand is driven from real sales data, we find that the average gap between the profits of our proposed policy and the optimal policy is 0.27%, and the maximum gap is 4.6%.