NDLI: Single-resource capacity control with random-fuzzy demand

Content Provider	IEEE Xplore Digital Library
Author	Lei Jun-li Li Jin-lin Ran Lun
Copyright Year	2011
Description	Author affiliation: School of Management and Economics, Beijing Institute of Technology, P.R. China, 100081 (Lei Jun-li; Li Jin-lin; Ran Lun)
Abstract	This paper studies the capacity control problem in revenue management with single resource, where the decision maker can only estimate the type of probability distribution of uncertain demand, but they do not have the necessary historic data to estimate the parameters of these distributions. We model these uncertainty parameters with fuzzy variables. Based on the theory of fuzzy satisfaction degree for objectives, the fuzzy nonlinear programming model in this paper is transformed into a convex programming problem. By using the lagrangian multiplier and the Kuhn-Tucker conditions we obtain the optimal solution of the model. Owing to the information for demand in random-fuzzy environment is less than only in the random environment, the paper reveals that the protection level for the highest class with random-fuzzy demand are more than only in random environment and it depends on the membership of the fuzzy environment, what's more, if and only if the membership equals 1, the protection level in these two situations are the same to each other. The model in this paper can be viewed as a generalization of single-resource capacity control with random demand.
Starting Page	105
Ending Page	110
File Size	187436
Page Count	6
File Format	PDF
ISBN	9781457718854
ISSN	21551855
e-ISBN	9781457718885
DOI	10.1109/ICMSE.2011.6069950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-13
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	capacity control protection level Uncertainty Operations research fuzzy satisfaction degree random-fuzzy demand Stochastic processes Programming Probability distribution Numerical models Approximation methods
Content Type	Text
Resource Type	Article

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