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New heuristics for revenue managemen...

Columbia University.

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New heuristics for revenue management problem with customer choice models.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	New heuristics for revenue management problem with customer choice models./
Author:	Li, Lin.
Description:	176 p.
Notes:	Adviser: Guillermo Gallego.
Contained By:	Dissertation Abstracts International70-01B.
Subject:	Operations Research. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3343519
ISBN:	9780549984580

New heuristics for revenue management problem with customer choice models.
Li, Lin.

New heuristics for revenue management problem with customer choice models. - 176 p.

Adviser: Guillermo Gallego.

Thesis (Ph.D.)--Columbia University, 2009.

The conclusions are summarized in the last chapter.

ISBN: 9780549984580Subjects--Topical Terms:

626629
Operations Research.

New heuristics for revenue management problem with customer choice models.
LDR:03487nam 2200313 a 45 001 854528
005 20100706
008 100706s2009 ||||||||||||||||| ||eng d
020 $a 9780549984580
035 $a (UMI)AAI3343519
035 $a AAI3343519
040 $a UMI $c UMI
100 1 $a Li, Lin. $3 1020924
245 1 0 $a New heuristics for revenue management problem with customer choice models.
300 $a 176 p.
500 $a Adviser: Guillermo Gallego.
500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0675.
502 $a Thesis (Ph.D.)--Columbia University, 2009.
520 $a The conclusions are summarized in the last chapter.
520 $a This dissertation focuses on designing efficient heuristics for revenue management problem with customer choice behavior. This problem has gained growing interest in recent years as a result of fierce competition, fare transparency and restriction free pricing. Existing solutions to RM with restriction free pricing are either based on ad-hoc adjustments to traditional capacity allocation or computationally intensive dynamic programming. In our framework, demand for different fare classes is estimated by a customer choice model. We show how a multinomial logit demand model can be used to provide upsell estimates for handling dependent demands and also account for competitive effects.
520 $a In chapter 2, we present new EMSR-based formulations for the single-leg, nested, multiple fare RM problem that can be used to solve both dependent and independent demand problem. We develop efficient and nearly optimal static heuristics for RM optimization that are more general and provide better performance than the widely used EMSR-b algorithm for independent demands. Variations of the algorithm for both low-to-high and mixed arrival order cases are included.
520 $a Chapter 3 deals with the dynamic formulation for the single-leg problem (Talluri and van Ryzin (2004a)) where the control policy depends on remaining time and remaining capacity. We develop near optimal and computationally efficient heuristics which generate control policy without having to solve the computationally intensive dynamic programming. The heuristics are designed for the case where the efficient sets are nested, as in the Multinomial Logit Model or the independent demand model. The nesting, however, does not necessarily need to be by fare values. The extensions to RM problem with time heterogenous choice models as well as non-homogenous customer arrivals are also studied. The numerical experiments indicate that the heuristics perform well.
520 $a Parallel flights problem with two fare classes is studied in Chapter 4. We assume that low fare demands come first in stage 1 followed by high fare demands in stage 2. We consider simultaneous seat inventory control of low fare class on these n parallel flights where the spilled demand can either upsell to the high fare on the same flight or goes to alternative same fare class on other flights. We provide both the upper bound and the lower bound on the value function and propose a heuristic which is based on capacity pooling technique together with the static Choice-Based EMSR heuristic.
590 $a School code: 0054.
650 4 $a Operations Research. $3 626629
690 $a 0796
710 2 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 70-01B.
790 $a 0054
790 1 0 $a Gallego, Guillermo, $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3343519