Itinerary based Revenue Management Problem with Quantity Discount in Airlines

2021 ◽  
Vol 38 (3) ◽  
pp. 67-81
Author(s):  
Moon Gil Yoon ◽  
Hwi Young Lee
Keyword(s):  
Revenue Management ◽  
Management Problem ◽  
Quantity Discount

A revenue management problem with a choice model of consumer behaviour in a random environment

OR Spectrum ◽  
2014 ◽  
Vol 37 (2) ◽  
pp. 457-473 ◽  
Author(s):  
Can Özkan ◽  
Fikri Karaesmen ◽  
Süleyman Özekici
Keyword(s):  
Revenue Management ◽  
Random Environment ◽  
Consumer Behaviour ◽  
Choice Model ◽  
Management Problem

Revenue Management with Repeated Customer Interactions

2020 ◽  
Author(s):  
Andre P. Calmon ◽  
Florin D. Ciocan ◽  
Gonzalo Romero
Keyword(s):  
Revenue Management ◽  
Online Advertising ◽  
Management Problem ◽  
Natural Conditions ◽  
Short Term ◽  
Performance Guarantees ◽  
Look Ahead ◽  
Myopic Policies

Motivated by online advertising, we model and analyze a revenue management problem where a platform interacts with a set of customers over a number of periods. Unlike traditional network revenue management, which treats the interaction between platform and customers as one-shot, we consider stateful customers who can dynamically change their goodwill toward the platform depending on the quality of their past interactions. Customer goodwill further determines the amount of budget that they allocate to the platform in the future. These dynamics create a trade-off between the platform myopically maximizing short-term revenues, versus maximizing the long-term goodwill of its customers to collect higher future revenues. We identify a set of natural conditions under which myopic policies that ignore the budget dynamics are either optimal or admit parametric guarantees; such simple policies are particularly desirable since they do not require the platform to learn the parameters of each customer dynamic and only rely on data that is readily available to the platform. We also show that, if these conditions do not hold, myopic and finite look-ahead policies can perform arbitrarily poorly in this repeated setting. From an optimization perspective, this is one of a few instances where myopic policies are optimal or have parametric performance guarantees for a dynamic program with nonconvex dynamics. We extend our model to the cases where supply varies over time and where customers may not interact with the platform in every period. This paper was accepted by Chung Piaw Teo, optimization.

A Simheuristic approach to the vehicle ferry revenue management problem

2016 ◽  
Author(s):  
Christopher Bayliss ◽  
Julia M. Bennell ◽  
Christine S.M. Currie ◽  
Antonio Martinez-Sykora ◽  
Mee-Chi So
Keyword(s):  
Revenue Management ◽  
Management Problem

Revenue management with two market segments and reserved capacity for priority customers

2000 ◽  
Vol 32 (03) ◽  
pp. 800-823 ◽  
Author(s):  
Y Feng ◽  
B. Xiao
Keyword(s):  
Exact Solution ◽  
Revenue Management ◽  
Optimal Stopping ◽  
Stopping Time ◽  
Service Level ◽  
Optimal Decision ◽  
High Price ◽  
Management Problem ◽  
Market Segments ◽  
Made In

This paper studies a revenue management problem in which a finite number of substitutable commodities are sold to two different market segments at respective prices. It is required that a certain number of commodities are reserved for the high-price segment to ensure a minimum service level. The two segments are served concurrently at the beginning of the season. To improve revenues, management may choose to close the low-price segment at a time when the chance of selling all items at the high price is promising. The difficulty is determining when such a decision should be made. We derive the exact solution in closed form using the theory of optimal stopping time. We show that the optimal decision is made in reference to a sequence of thresholds in time. These time thresholds take both remaining sales season and inventory into account and exhibit a useful monotone property.

Choice-Based Dynamic Pricing for Vacation Rentals

2021 ◽  
Author(s):  
Yaping Wang ◽  
Kelly McGuire ◽  
Jeremy Terbush ◽  
Michael Towns ◽  
Chris K. Anderson
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Conditional Logistic Regression ◽  
Model Performance ◽  
Price Sensitivity ◽  
Management Problem ◽  
Rolling Window ◽  
Time Availability ◽  
Optimization Routine ◽  
The Impact

In this paper, we propose a new dynamic pricing approach for the vacation rental revenue management problem. The proposed approach is based on a conditional logistic regression that predicts the purchasing probability for rental units as a function of various factors, such as lead time, availability, property features, and market selling prices. In order to estimate the price sensitivity throughout the booking horizon, a rolling window technique is provided to smooth the impact over time and build a consistent estimation. We apply a nonlinear optimization algorithm to determine optimal prices to maximize the revenue, considering current demand, availability from both the rental company and its competitors, and the price sensitivity of the rental guest. A booking curve heuristic is used to align the booking pace with business targets and feed the adjustments back into the optimization routine. We illustrate the proposed approach by successfully applying it to the revenue management problem of Wyndham Destinations vacation rentals. Model performance is evaluated by pricing two regions within the Wyndham network for part of the 2018 vacation season, indicating revenue per unit growth of 3.5% and 5.2% (for the two regions) through model use.

scholarly journals Online Network Revenue Management Using Thompson Sampling

2018 ◽  
Vol 66 (6) ◽  
pp. 1586-1602 ◽  
Author(s):  
Kris Johnson Ferreira ◽  
David Simchi-Levi ◽  
He Wang
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Domain Knowledge ◽  
Poor Performance ◽  
Management Problem ◽  
Network Revenue Management ◽  
Thompson Sampling ◽  
Main Challenge ◽  
Inventory Constraints ◽  
Network Revenue

Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide attention, as Internet companies have successfully implemented it for online ad display. In “Online network revenue management using Thompson sampling,” K. Ferreira, D. Simchi-Levi, and H. Wang propose using Thompson sampling for a revenue management problem where the demand function is unknown. A main challenge to adopt Thompson sampling for revenue management is that the original method does not incorporate inventory constraints. However, the authors show that Thompson sampling can be naturally combined with a linear program formulation to include inventory constraints. The result is a dynamic pricing algorithm that incorporates domain knowledge and has strong theoretical performance guarantees as well as promising numerical performance results. Interestingly, the authors demonstrate that Thompson sampling achieves poor performance when it does not take into account domain knowledge. Finally, the proposed dynamic pricing algorithm is highly flexible and is applicable in a range of industries, from airlines and internet advertising all the way to online retailing.

scholarly journals A Robust Optimization of Capacity Allocation Policies in the Third-Party Warehouse

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xu Xian-hao ◽  
Dong Wei-hong ◽  
Peng Hongxia
Keyword(s):  
Robust Optimization ◽  
Revenue Management ◽  
Optimization Technique ◽  
Capacity Allocation ◽  
Third Party ◽  
Management Problem ◽  
Robust Methods ◽  
Numerical Examples ◽  
The Third ◽  
Management Perspective

We study the capacity allocation policies of a third-party warehouse center, which supplies several different level services on different prices with fixed capacity, on revenue management perspective. For the single period situation, we use three different robust methods, absolute robust, deviation robust, and relative robust method, to maximize the whole revenue. Then we give some numerical examples to verify the practical applicability. For the multiperiod situation, as the demand is uncertain, we propose a stochastic model for the multiperiod revenue management problem of the warehouse. A novel robust optimization technique is applied in this model to maximize the whole revenue. Then we give some numerical examples to verify the practical applicability of our method.

scholarly journals A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jia-Zhen Huo ◽  
Yan-Ting Hou ◽  
Feng Chu ◽  
Jun-Kai He
Keyword(s):  
Revenue Management ◽  
Profit Maximization ◽  
Transportation Cost ◽  
Case Analysis ◽  
Capacity Allocation ◽  
Worst Case Analysis ◽  
Management Problem ◽  
Hub Location ◽  
Worst Case ◽  
Average Case

This paper investigates joint decisions on airline network design and capacity allocation by integrating an uncapacitated single allocation p-hub median location problem into a revenue management problem. For the situation in which uncertain demand can be captured by a finite set of scenarios, we extend this integrated problem with average profit maximization to a combined average-case and worst-case analysis of this integration. We formulate this problem as a two-stage stochastic programming framework to maximize the profit, including the cost of installing the hubs and a weighted sum of average and worst case transportation cost and the revenue from tickets over all scenarios. This model can give flexible decisions by putting the emphasis on the importance of average and worst case profits. To solve this problem, a genetic algorithm is applied. Computational results demonstrate the outperformance of the proposed formulation.

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