一种基于深度强化学习的酒店收益管理模型与方法-豆柴文库

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一种基于深度强化学习的酒店收益管理模型与方法
Title:ADeepReinforcementLearning-BasedModelandApproachforHotelRevenueManagement
Abstract:
Effectiverevenuemanagementplaysacrucialroleinthesuccessofthehotelindustry.Traditionally,revenuemanagementstrategieshavereliedonpredefinedrulesetsandheuristics.Withtheadvancementsinmachinelearningandspecificallydeepreinforcementlearning,newopportunitiesarisefordevelopingmoreintelligentanddynamicrevenuemanagementmodels.Inthispaper,weproposeanoveldeepreinforcementlearning-basedmodelandapproachforhotelrevenuemanagement.Themodelaimstomaximizethelong-termrevenuebydynamicallyadjustingpricesinresponsetochangingmarketconditions.Wealsopresentacasestudytodemonstratetheeffectivenessofourproposedmodelinoptimizinghotelrevenue.
1.Introduction:
Revenuemanagementhasbecomeanintegralpartofthehotelindustryashoteliersstrivetooptimizetheirprofits.Traditionalrevenuemanagementstrategiesfacelimitationsintheirabilitytoadapttodynamicmarketconditionsanduncertaincustomerbehavior.Deepreinforcementlearningoffersapromisingsolutionbyenablingthemodeltolearnandadaptitspricingstrategiesthroughtrialanderrorinteractionswiththeenvironment.Theobjectiveofthisresearchistodevelopadeepreinforcementlearning-basedmodelforhotelrevenuemanagementthatmaximizeslong-termrevenuebydynamicallyadjustingprices.
2.RelatedWork:
Thissectionprovidesanoverviewoftheexistingliteratureonhotelrevenuemanagementmodelsandtheapplicationofdeepreinforcementlearningtechniquesinvariousdomains.Wediscussthelimitationsoftraditionalrevenuemanagementmodelsandhighlighttheadvantagesandpotentialbenefitsofincorporatingdeepreinforcementlearningforrevenuemanagement.
3.Methodology:
Weproposeadeepreinforcementlearning-basedmodelforhotelrevenuemanagement.Themodelconsistsofanagent,anenvironment,andarewardfunction.Theagentutilizesadeepneuralnetworktolearntheoptimalpricingpolicybyinteractingwiththeenvironment.Theenvironmentsimulatesthemarketconditions,customerdemand,competitorprices,andotherinfluencingfactors.Therewardfunctionprovidesfeedbackto