Online Travel Agency Channel Pricing Policy based on Dynamic Pricing Model to Maximize Sales Profit Using Nonlinear Integer Programming Approach

Abstract

A dynamic pricing strategy on the cooperation between a hotel and an online travel agency (OTA) is commonly applied to build a pricing policy. The purpose of this study is to propose a pricing policy according to the dynamic pricing model on a single online travel agency channel. The paper provides a dynamic pricing model adjusted to hotel problems with multiple room types. The study consists of two stages. First, we apply a revenue management tool that is dynamic pricing to model the effect of price on demand. The price dynamically changes based on the parameter of demand model. Second, we use a nonlinear integer programming approach to maximize the profit by substituting the demand model which has the lowest root mean square error. The parameter of the demand model is estimated by using the historical sales-price data from one of the hotels in Bandung, West Java, Indonesia. Our results propose a pricing policy of each room types that able to increase 18.54% from the historical sales profit. The proposed pricing policy completes the gap of the method in the existing pricing policy. Moreover, the findings provide an optimal room rate to the front office manager along the planning horizon.

Designing simulation model for minimizing coal train unloading time: A case study of Kereta Api Logistik Company

“…Research has been done on the management of information flows such as the development of a sustainable procurement monitoring system to support risk management of the food supply chain [1] and the development of reverse logistics to monitoring system in the leather tanning industry [2]. In financial flow, there are several studies to maximize the profit, such as the development of dynamic pricing in the hotel business sector [3] and online travel agency [4]. There are also several studies that have been done to improve the material flow performance such as the development of the e-Kanban application to reduce zero picks for pharmaceutical warehouses [5] and the development of warehouse picking models with genetic algorithm methods to minimize delays [6].…”