A binary differential evolution algorithm for airline revenue management: a case study

In the current highly competitive airline market, many companies have failed due to their low revenue rates. For this reason, many of them have to develop strategies to increase their revenue. In this study, we develop revenue management (RM) strategy for the Iranian airline industry. More specifically, we present a mathematical model that considers some conditions not studied in previous research in order to provide a more realistic RM modeling of airlines that fits well for the special characteristics of Iranian Airways. A binary differential evolution algorithm is employed to solve the model due to the stochastic nature of data and the NP-hardness of the considered problem. To generate maximum revenue among the six types of airplanes that fly the four capital cities of Iran, the airline under investigation is advised to operate only 21 flights to those cities and cancel the rest of the flights.

[1]  Jochen Gönsch,et al.  A Survey on Risk-Averse and Robust Revenue Management , 2017, Eur. J. Oper. Res..

[2]  B. Kinghorn,et al.  Differential evolution - an easy and efficient evolutionary algorithm for model optimisation , 2005 .

[3]  X S Zhao,et al.  An Improved Binary Differential Evolution Algorithm for Feature Selection in Molecular Signatures , 2018, Molecular informatics.

[4]  Xin-She Yang Harmony Search as a Metaheuristic Algorithm , 2009 .

[5]  Pablo Cortés,et al.  An overview of revenue management in service industries: an application to car parks , 2011 .

[6]  Gary Parker Optimising airline revenue management , 2003 .

[7]  Lin Tian,et al.  A stochastic multi-channel revenue management model with time-dependent demand , 2018, Comput. Ind. Eng..

[8]  Weijie Zheng,et al.  Working principles of binary differential evolution , 2018, GECCO.

[9]  Hadi Mokhtari,et al.  A bi-objective airline revenue management problem with possible cancellation , 2016 .

[10]  J. B. G. Frenk,et al.  A Network Airline Revenue Management Framework Based on Decomposition by Origins and Destinations , 2014, Transp. Sci..

[11]  R. Brennan,et al.  A Framework for Key Account Management and Revenue Management Integration , 2014 .

[12]  Hwi Young Lee,et al.  Dynamic pricing & capacity assignment problem with cancellation and mark-up policies in airlines , 2017 .

[13]  Christos Koulamas,et al.  Optimal pricing and seat allocation for a two-cabin airline revenue management problem , 2018, International Journal of Production Economics.

[14]  Giampaolo Viglia,et al.  Strategic and tactical price decisions in hotel revenue management , 2016 .

[15]  Ching-Cheng Chao,et al.  Effects of cargo types and load efficiency on airline cargo revenues , 2017 .

[16]  Minrui Fei,et al.  A novel modified binary differential evolution algorithm and its applications , 2012, Neurocomputing.

[17]  Alf Kimms,et al.  Transfer price optimization for option-based airline alliance revenue management , 2013 .

[18]  Chen Xu,et al.  Transformation of optimization problems in revenue management, queueing system, and supply chain management , 2013 .

[19]  J. B. G. Frenk,et al.  Single-Leg Airline Revenue Management with Overbooking , 2013, Transp. Sci..

[20]  Octavian Oancea Analytical framework for airline revenue management and network planning , 2016 .

[21]  Wenjian Cai,et al.  A global optimized operation strategy for energy savings in liquid desiccant air conditioning using self-adaptive differential evolutionary algorithm , 2017 .

[22]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[23]  Jinxing Xie,et al.  Duopoly game of callable products in airline revenue management , 2016, Eur. J. Oper. Res..

[24]  Yanmin Liu,et al.  An improved binary differential evolution algorithm for optimizing PWM control laws of power inverters , 2018 .

[25]  Piero Baraldi,et al.  Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics , 2018 .

[26]  Abhijit Gosavi,et al.  Simulation optimization for revenue management of airlines with cancellations and overbooking , 2006, OR Spectr..

[27]  Muhammad El-Taha,et al.  Dynamic two-leg airline seat inventory control with overbooking, cancellations and no-shows , 2004 .

[28]  Gang Yu,et al.  Optimizing Pilot Planning and Training for Continental Airlines , 2004, Interfaces.

[29]  Andries Petrus Engelbrecht,et al.  Binary Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[30]  Robert D. van der Mei,et al.  Revenue management under customer choice behaviour with cancellations and overbooking , 2015, Eur. J. Oper. Res..

[31]  Michael D. Wittman,et al.  Personalization in airline revenue management – Heuristics for real-time adjustment of availability and fares , 2017 .

[32]  Nurhan Karaboga,et al.  Performance Comparison of Genetic and Differential Evolution Algorithms for Digital FIR Filter Design , 2004, ADVIS.

[33]  K. S. Swarup,et al.  Differential evolutionary algorithm for optimal reactive power dispatch , 2008 .

[34]  Xing Hu,et al.  Revenue Sharing in Airline Alliances , 2013, Manag. Sci..

[35]  Bo Liao,et al.  An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees , 2017, BioMed research international.

[36]  Tim Baker,et al.  An evaluation of the bid price and nested network revenue management allocation methods , 2018, Comput. Ind. Eng..

[37]  Subhashish Samaddar,et al.  Improving Revenue Management Decision Making for Airlines by Evaluating Analyst-Adjusted Passenger Demand Forecasts , 2007, Decis. Sci..

[38]  Robert A. Shumsky,et al.  Dynamic Revenue Management in Airline Alliances , 2010, Transp. Sci..

[39]  Alf Kimms,et al.  Airline revenue management games with simultaneous price and quantity competition , 2016, Comput. Oper. Res..

[40]  Zhe Chen,et al.  Comprehensive Cost Minimization in Distribution Networks Using Segmented-Time Feeder Reconfiguration and Reactive Power Control of Distributed Generators , 2016, IEEE Transactions on Power Systems.

[41]  Basak Denizci Guillet,et al.  Revenue management research in hospitality and tourism: A critical review of current literature and suggestions for future research , 2015 .

[42]  Dan Zhang,et al.  Pricing substitutable flights in airline revenue management , 2009, Eur. J. Oper. Res..

[43]  Huseyin Topaloglu,et al.  Delayed Purchase Options in Single-Leg Revenue Management , 2017, Transp. Sci..

[44]  Sebastian Koch,et al.  A review of revenue management: Recent generalizations and advances in industry applications , 2020, Eur. J. Oper. Res..

[45]  Dan Zhang,et al.  Revenue Management for Parallel Flights with Customer-Choice Behavior , 2005, Oper. Res..

[46]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[47]  Tapas K. Das,et al.  A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking , 2002 .

[48]  S. Ilker Birbil,et al.  Decomposition methods for dynamic room allocation in hotel revenue management , 2018, Eur. J. Oper. Res..

[49]  Mohammad Modarres,et al.  On the fairness of airlines’ ticket pricing as a result of revenue management techniques , 2014 .

[50]  Dimitris Bertsimas,et al.  Simulation-Based Booking Limits for Airline Revenue Management , 2005, Oper. Res..

[51]  Alessandro V. M. Oliveira Simulating revenue management in an airline market with demand segmentation and strategic interaction , 2003 .

[52]  Danping Lin,et al.  Air cargo revenue management under buy-back policy , 2017 .

[53]  Cindy Yoonjoung Heo,et al.  New performance indicators for restaurant revenue management: ProPASH and ProPASM , 2017 .

[54]  S R Parija,et al.  Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem , 2017 .

[55]  Richard Klophaus,et al.  Airline overbooking with dynamic spoilage costs , 2007 .

[56]  Anming Zhang,et al.  REVENUE SHARING WITH MULTIPLE AIRLINES AND AIRPORTS , 2010 .

[57]  Alf Kimms,et al.  Revenue management under horizontal and vertical competition within airline alliances , 2016 .

[58]  Lixin Tang,et al.  An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-Continuous Casting Production , 2014, IEEE Transactions on Evolutionary Computation.

[59]  Raha Akhavan-Tabatabaei,et al.  A stochastic dynamic pricing model for the multiclass problems in the airline industry , 2015, Eur. J. Oper. Res..

[60]  Hedieh Sajedi,et al.  Satellite Broadcast Scheduling Based on a Boosted Binary Differential Evolution , 2017, New Generation Computing.

[61]  Dong Li,et al.  Dynamic booking control for car rental revenue management: A decomposition approach , 2017, Eur. J. Oper. Res..

[62]  Aditya S Kothari,et al.  Simulating the flavors of revenue management for airlines , 2015 .