A practical approach to revenue management in passenger train services: A case study of the Indonesian railways Argo Parahyangan

Pratikto, Fransiscus Rian

doi:10.1016/j.jrtpm.2019.100161

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wen and Zhang (2019) exploit passengers’ demand pattern and differential pricing at HSR and examine the efficacy of seat allocation. Pratikto (2020) utilize price variation and capacity management for revenue maximization where customers’ willingness-to-pay method is used for demand estimation. Hu et al (2020b) present an integrated model where pricing policy and seat allocation are studied jointly.…”

Section: Related Workmentioning

confidence: 99%

An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion

Haque

Hamid²

2022

Transportation Research Part A: Policy and Practice

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion

Haque

Hamid²

2022

Transportation Research Part A: Policy and Practice

View full text Add to dashboard Cite

“…Also, pricing influences customer demand, playing a significant role in attracting customers, and increasing sales [50]. Since the demand changes dynamically (especially during seasons of holidays and festivals), rail transportation corporations are facing complicated and different strategies and techniques to assign ticket prices [29]. Also, demand has a high degree of uncertainty [28], which influences fare prices and thereby affects railway planning; therefore, it is important to use an appropriate method to deal with the uncertainty of demand.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning-optimization approach to pricing and train formation problem under demand uncertainty

Yousefi

Pishvaee

2022

RAIRO-Oper. Res.

View full text Add to dashboard Cite

Due to the complexity of pricing in service industry, it is important to provide an efficient pricing framework for real-life and large-sized applications. To this end, we combined an optimization approach with a regression-based machine learning method to provide a reliable and efficient framework for integrated pricing and train formation problem under hybrid uncertainty. To do so, firstly, a regression-based machine learning model is applied to forecast the ticket price of the passenger railway and then, the obtained price in is used as the input of a train formation optimization model. Further, in order to deal with the hybrid uncertainty of demand parameter, a robust fuzzy stochastic programming model is proposed. Finally, a real transportation network from the Iran railway is applied to demonstrate the efficiency of the proposed model. The analysis of numerical results indicated that the proposed framework is able to state the optimal price with less complexity in comparison to traditional models.

show abstract

Dynamic revenue management in a passenger rail network under price and fleet management decisions

2023

View full text Add to dashboard Cite

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company's historical sales data. A mixed-integer non-linear programming model is presented to maximize the company's profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company.

show abstract

A practical approach to revenue management in passenger train services: A case study of the Indonesian railways Argo Parahyangan

Cited by 4 publications

References 11 publications

An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion

An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion

A hybrid machine learning-optimization approach to pricing and train formation problem under demand uncertainty

Dynamic revenue management in a passenger rail network under price and fleet management decisions

Contact Info

Product

Resources

About