15th EUROPT Workshop on Advances in Continuous Optimization

Montréal, Canada, 12 — 14 juillet 2017

15th EUROPT Workshop on Advances in Continuous Optimization

Montréal, Canada, 12 — 14 juillet 2017

Horaire Auteurs Mon horaire
Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402

Pricing and revenue management

14 juil. 2017 08h45 – 10h00

Salle: St-Hubert

Présidée par Thibault Barbier

3 présentations

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    08h45 - 09h10

    Choice-Based Revenue Optimization and Intelligence Applied to the Railway Industry

    • Morad Hosseinalifam, prés., Expretio Technologies

    We present a new-generation of Revenue Optimization tools to meet the demands of railway operators. We integrate powerful customer behavior models into a large-scale optimization framework to face this dynamic and competitive environment. The framework developed by Expretio allows operators to optimally manage their seat inventory and set fares, as well as other product attributes, across channels and customer profiles while taking into account passenger purchase habits and competitors’ actions.

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    09h10 - 09h35

    Railway Demand Forecasting Using Machine Learning Approaches

    • Neda Etebarialamdari, prés.,
    • Gilles Savard, Polytechnique Montréal
    • Miguel F. Anjos, GERAD, Polytechnique Montréal

    In railway industries, demand forecasting is the estimation of the number of passengers aiming to travel by train with a specific itinerary. Railway uses this information to satisfy their demands and maximize the revenues. In this study, we present detailed analyses of applications of various machine learning algorithms and preprocessing techniques to predict the future bookings in railway industries in two different aggregation levels. As a result, stacked generalization method combined with proper preprocessing techniques outperformed other approaches at both levels. We successfully achieved 11% Mean Absolute Percentage Error for level-1 aggregation and 18% Weighted Absolute Percentage Error for level-2.

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    09h35 - 10h00

    Products closing times optimization for choice-based revenue management

    • Thibault Barbier, prés., Polytechnique Montréal
    • Gilles Savard, Polytechnique Montréal
    • Miguel F. Anjos, GERAD, Polytechnique Montréal
    • Fabien Cirinei, Expretio

    Choice behavioral aspect is now paramount to network revenue management. Tackling both "buy-up" and "buy-down" phenomena, choice-based models provide more accurate and robust solutions, thus generating better revenue. Most choice-based models optimize revenue by adjusting how much time each set of products must be offered over the booking process. This approach was introduced by the choice deterministic linear program (CDLP) and has been extensively developed since then to reduce resolution time and to tackle different choice behavior. We offer a new approach for choice-based network revenue management that finds when to stop selling each product during the booking process. Numerical experiments on literature instances demonstrate promising performance. Our model quickly returns a directly implementable control that often gives better revenue when simulated than traditional products sets approaches.

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