A minmax regret price control model for managing perishable products with uncertain parameters

Jiamin Wang and Baichun Xiao

European Journal of Operational Research, 2017, vol. 258, issue 2, 652-663

Abstract: Conventional revenue management assumes that demand parameters used in the models are known. The control policy derived based on such a premise, however, guarantees no optimality as the real parameters can significantly deviate from their estimates. This article proposes a robust price-control model for managing perishable products. It attempts to find a policy that minimizes the worst-case regret due to imperfect information. Distribution of each uncertain parameter is assumed arbitrary, with only the lower and upper bound available. We formulate the dynamic price control problem as a continuous-time model. Under fairly mild conditions, we derive the optimality condition for the control policy and develop a recursive procedure for the optimal solution. Our analysis shows that the proposed minmax regret price-control model is equivalent to conventional RM models when demand parameters are deterministic. We examine structural properties of the solution and managerial insights they imply. Numerical results show that the proposed robust model outperforms the conventional RM model when parameters are unknown. In particular, it significantly reduces the variation in revenues without sacrificing the average revenue.

Keywords: Revenue management; Robust control; Dynamic pricing; Minmax regret (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221716307603
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:258:y:2017:i:2:p:652-663

DOI: 10.1016/j.ejor.2016.09.024

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().