基于选择的网络收益管理的列生成算法

Juan José Miranda Bront, I. Méndez-Díaz, Gustavo J. Vulcano
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引用次数: 309

摘要

在过去几年中,有一种趋势是通过考虑客户选择行为来丰富建立在独立需求范式基础上的传统收益管理模型。这个扩展涉及建模和计算挑战。描述选择行为的一种方法是假设每个客户都属于一个细分市场,这个细分市场的特征是一个考虑集,也就是说,公司提供的产品的一个子集,客户将其视为选项。顾客根据多项logit准则选择特定的产品,这是一种在市场营销文献中广泛使用的模型。本文考虑了Gallego等人(2004)的基于选择的确定性线性规划模型(CDLP) [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004]。管理网络中灵活的产品。范瑞琴,刘强,刘庆。2008.基于改进的动态规划分解启发式算法[J] .北京:北京交通大学学报(自然科学版)。基于选择的网络收益管理线性规划模型研究。制造服务部门Management10(2) 288 - 310]。我们专注于这些模型的更一般的版本,其中客户属于重叠的细分市场。为了解决实际规模网络的CDLP问题,我们需要开发一种列生成算法。我们证明了相关的列生成子问题确实是np困难的,并提出了一个简单的、贪婪的启发式算法来克服精确算法的复杂性。我们的计算结果表明,启发式是相当有效的,并且总体方法导致高质量,实用的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Column Generation Algorithm for Choice-Based Network Revenue Management
During the past few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature. In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. (2004) [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. Technical Report CORC TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York], and the follow-up dynamic programming decomposition heuristic of van Ryzin and Liu (2008) [van Ryzin, G. J., Q. Liu. 2008. On the choice-based linear programming model for network revenue management. Manufacturing Service Oper. Management10(2) 288--310]. We focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-hard and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective and that the overall approach leads to high-quality, practical solutions.
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