Revenue Management Problem via Stochastic Programming in the Aviation Industry

2021 ◽  
pp. 145-157
Author(s):  
Mio Imai ◽  
Tetsuya Sato ◽  
Takayuki Shiina
Keyword(s):  
Stochastic Programming ◽  
Revenue Management ◽  
Aviation Industry ◽  
Management Problem

scholarly journals An Integrated Problem of p-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yan-Ting Hou ◽  
Jia-Zhen Huo ◽  
Feng Chu
Keyword(s):  
Stochastic Programming ◽  
Revenue Management ◽  
Programming Model ◽  
Profit Maximization ◽  
Transportation Cost ◽  
Sample Average Approximation ◽  
Management Problem ◽  
Hub Location ◽  
Two Stage ◽  
Airline Tickets

This paper considers an integrated hub location and revenue management problem in which a set of capacities is available from which one can be chosen for each hub and the disruption is considered in a star-star shaped airline network. We propose a two-stage stochastic programming model to maximize the profit of the network in which the cost of installing the hubs at different levels of capacities, the transportation cost, and the revenue obtained by selling airline tickets are considered. To provide flexible solutions, a hybrid two-stage stochastic programming-robust optimization model is developed by putting relative emphasis on a weighted sum of profit maximization. Furthermore, a sample average approximation approach is used for solving the stochastic programming formulation and a genetic algorithm approach is applied for both formulations. Numerical experiments are conducted to verify the mathematical formulations and compare the performance of the used approaches.

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

scholarly journals A Discrete Stochastic Programming Model to Estimate Optimal Burning Schedules on Rangeland

1987 ◽  
Vol 19 (2) ◽  
pp. 53-60 ◽  
Author(s):  
L. Garoian ◽  
J. R. Conner ◽  
C. J. Scifres
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Economic Feasibility ◽  
Range Management ◽  
Management Problem ◽  
Treatment Sequence ◽  
Net Returns ◽  
Discrete Stochastic Programming ◽  
Stochastic Programming Model

AbstractMacartney rose is a range management problem on 500,000 acres of rangeland in Texas. Roller chopping followed by burning is an effective method of improving infested rangeland. However, uncertainty associated with implementing effective burns adversely affects economic feasibility of the treatment sequence. Discrete stochastic programming is used to determine optimal burning schedules under uncertainty. Optimal schedules and expected net returns vary with changes in the probability of a successful burn.

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.

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