酒店收益管理外文翻译中英文.docx
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酒店收益管理外文翻译中英文.docx
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酒店收益管理外文翻译中英文
酒店收益管理中动态客房分配的解决方法中英文2019
原文
Decompositionmethodsfordynamicroomallocationinhotelrevenuemanagement
N.Aydin,S.I.Birbil
Abstract
Long-termstaysarequitecommoninthehotelbusiness.Consequently,itiscrucialforthehotelmanagementstoconsidertheallocationofavailableroomstoastreamofcustomersrequestingtostaymultipledays.Thisrequirementleadstothesolvingofdynamicnetworkrevenuemanagementproblemsthatarecomputationallychallenging.Aremedyistoapplydecompositionapproachessothatanapproximatesolutioncanbeobtainedbysolvingmanysimplerproblems.Inthisstudy,weinvestigateseveralroomallocationpoliciesinhotelrevenuemanagement.Weworkonvariousdecompositionmethodstofindreservationpoliciesforadvancebookingsandstay-overcustomers.Wealsodevisesolutionalgorithmstosolvetheresultingproblemsefficiently.
Keywords:
Revenuemanagement,Hotel,Capacitycontrol,Decompositionmethods
Introduction
Historically,theairlineindustryplayedthesteeringroleinrevenuemanagement(RM).Today,however,thereisawiderangeofapplicationsindifferentindustrieswithvolatiledemand,requestingfixedandperishablecapacity(Kimes,1989).Althoughthehotelindustryisoneofthetypicalapplicationareasofrevenuemanagement,theresearchinthisparticulararealagsbehindtheworkproducedforotherserviceindustries.Intheirrecentwork, IvanovandZhechev(2012) and Ivanov(2014) presentareviewofthemethodsproposedinthehotelRMliteratureandpointoutthegaps.
Ingeneral,well-knownairlineRMtechniques,suchasbookingcontrolandpricing,canbeappliedtohotelRMproblems.However,itisimportanttoconsiderseveralconstraintsthatareuniquetohotelreservationsystems.First,multi-daystaysinhotelsarequitecommon.Whileaflightitineraryincludes,onaveragefewerthanthreelegs,thenumberofnightsatypicalcustomerspendsinahotelcanbeaweekorevenmore(Zhang&Weatherford,2017).Second,thedemandprocessisdifferent.Hotelcustomersmaydecidetostaylongerandextendtheirreservationwhiletheyarestayinginthehotel(Kimes,1989).Third,airlinecustomersgenerallymakeadvancebookingsbutanumberofhotelcustomersconsistofwalk-ins.Moreover,theearlyreservationsinthebookingintervalareevenallowedtocanceltheirbookingsatnoextracost.
Inthispaper,wefocusontheroomallocationdecisionsforahotel.The optimalpolicy toacceptorrejectanarrivingcustomercanbeobtainedbyanalyzingthestochasticnatureofthecustomerarrivalprocess.Inhotelreservationsystems,thecustomersareclassifiedasthe advancebookings,the stay-overs andthe walk-ins.Whiletheadvancebookingsmakeroomreservationsbeforetheyarriveatthehotel,thewalk-insshowupwithoutanyreservation.Thestay-oversarethecustomerswhoaskforanextensionfortheirreservationsduringtheirstayinthehotel.Recently,hotelreservationsystemshavestartedofferingextendedstayasanoptionduetohighcustomerdemand(Tepper,2015).Forinstance, Priceline(2017) and Hotwire(2017)present“add-a-night”and“addtoyourstay”optionstotheirexistingcustomers.Thearrivalprocessoftheadvancebookingsandwalk-insaresimilar.Theonlydifferenceisthatthewalk-incustomersarriveafterthereservationperiodends.However,thestay-overrequestsdependontheacceptedadvancebookings.Tosimplifyournotation,weignorethewalk-incustomersandformulateourproblembyconsideringtheadvancebookingsandthestay-overs.Then,weexplainhowonecaneasilyincorporatethewalk-incustomerstoourproposedmodels.Tothebestofourknowledge,thedynamicmodelofstay-overcustomersinanetworksettinghasnotbeenpreviouslystudiedintheliterature.
Theresearchcontributionsinthispapercomefromtheapplicationandtheanalysisoftwodecompositionapproaches.Thesearetheday-basedandtheperiod-baseddecompositions.Ourday-baseddecompositionissimilartotheoneproposedby KunnumkalandTopaloglu(2010).Wesimplifytheirdecompositionmethodandshowthatourproposedmodelprovidesa lowerbound totheirmodel.Wesetforthadynamicmodelfortheadvancebookingsandformulatea linearprogram fortheproblem.Theresultingmodelisthensolvedwiththeconstraintgenerationmethod.Wealsoproposealternateapproximatemodels,whichprovideupperandlowerboundsontheoptimalexpectedrevenueoftheoriginalmodel.Tomanagethestay-overrequests,oneneedstokeeptrackofthenumberofreservationsineachbookingtype.Aday-basedmethod,however,decomposesthenetworkproblemintoindependentdays,andthisdecompositionapproachcauseslossofinformationonthenumberofcustomersineachbookingtype.Oursolutiontothishindranceisaperiod-baseddecompositionmethod,whichisanextensionofanotherapproachrecentlyproposedby Birbil,Frenk,Gromicho,andZhang(2014).First,wefocusonthesingle-daystay-overproblem,astherequestforanadditionalnightisthemostfrequentlyrealizedstay-overcaseinreal-life(Talya,2016).Thoughourmodelisdifferentthantheonesetforthby Birbilet al.(2014),wesuccessfullybuildontheirdecompositionidea.Second,weconsiderthemulti-daystay-overproblemandpresentatwo-periodapproximation,whichcombinesthepair-baseddecompositionwiththedeterministiclinearprogrammingapproach.Inperiodone,weobservethereservationactivityoftheadvancebookingcustomers.Inperiodtwo,wetakeintoaccountthestay-overrequestsofthecustomerswhosebookingshavebeenaccepted.Totesttheperformancesoftheproposeddecompositionapproaches,weconductsimulationexperimentsandcompareourresultswiththoseobtainedbyseveralwell-knownmodelsfromtheliterature.OurcomputationalstudyindicatesthattheproposeddecompositionapproachesareapttoeffectiveroomallocationinhotelRM.
Reviewofrelatedliterature
WebeginbyreviewingtherelatedworkonhotelRM.Then,wesummarizethedecompositionapproachesfrequentlyappliedtothenetworkRMproblems.
Ladany(1976) worksonasingle-daystaymodelforahotelwithtwotypesofresources.Theaimofthemodelistofindanallocationpolicytomaximizethedailyexpectedrevenue.Hedevelopsadynamicprogrammingformulationandobtainsthedecisionpolicyforeachresource. Williams(1977) worksonthesingle-daystaymodelduringthepeakdemandperiod.Inthismodel,heassumesthatdemandarrivesfromthreedifferentsources:
thestay-overs,thereservationsandthewalk-ins.Hecomputesthereservationpolicyforeachcustomertypebycomparingthecostsofunderbookingandoverbooking. BitranandLeong(1989) focusonthemulti-dayproblembyconsideringthewalk-inandstay-overrequests.Theymodelthemulti-dayreservationsasaseriesofindependent,single-dayreservations. BitranandMondschein(1995) developadynamicprogrammingmodelforasingle-daystayproblemwithmultipleproducts.Sincetheresultingmodeliscomputationallyintractablefortherealsizeproblems,theyutilizeseveralheuristicswhensearchingfortheoptimalallocationpolicy. Weatherford(1995) focusesontheeffectofthelengthofstay.Heproposesa heuristicmethod basedonastaticmodelandcomparesthismethodwiththeotherbookingpoliciesdevelopedforthesingle-daystayproblems. BitranandGilbert(1996) workonasingle-daystayandsingle-roomproblem.Theyassumethatduringtheserviceday,threetypesofcustomersshow-up:
thecustomerswithguaranteedreservations,thecustomerswithreservationsandthewalk-ins.Theydevelopadynamicmodelandproposeaheuristicmethodtoobtaintheroomallocationpolicy. BakerandCollier(1999) extendthestudyof Weatherford(1995) aswellastheworkof BitranandMondschein(1995) byallowingcancellations,overbookingandstay-overs.Theydeveloptwoheuristicsthatintegrateoverbookingwiththecapacityallocationdecisions.Theycomparetheperformancesoftheseheuristicsagainsttheotherbookingcontrolpoliciesintheliterature.Throughthiscomparison, BakerandCollier(1999) discusstheadvantagesofeachpolicyunderdifferentoperatingenvironments.
Laterstudiesfocusonmulti-productandmulti-daystayproblems. Chen(1998)presentsageneralformulationforadeterministicproblemanddiscussesthatitcanbetransferredtoanetworkflowproblem.Moreover,heshowsthattheoptimalsolutionofthe linearprogram isalwaysintegral. Goldman,Freling,Pak,andPiersma(2002) proposedeterministicandstochasticlinearprogrammingmodelstofindthenestedbookinglimitsandthebidpricesforthemulti-daystayproblem.Theyfollowtheworkof Weatherford(1995) todevelopthe deterministicmodel.Forthe stochasticmodel,theyextendtheworkof De Boer,Freling,andPiersma(2002)ontheairlinerevenuemanagementproblem.However,unlikethemodelsproposedby Weatherford(1995) and De Boeret al.(2002),theyusethebookingcontrolpoliciesoverarollinghorizonofdecisionperiods. LaiandNg(2005) workonastochasticprogrammingformulationforamulti-daystayproblem.Theyapplyrobustoptimizationtechniquestosolvetheproblemonascenariobasis.Theyalsoconsiderthe riskaversion ofthedecisionmakerandusethemeanabsolutevaluetomeasuretherevenuedeviationrisk. KoideandIshii(2005) workontheoptimalroomallocationpoliciesforasingle-daystaybyconsideringtheearlydiscounts,thecancellationsandtheoverbookings.Theyexaminethepropertiesoftheexpectedrevenuefunctionandshowthatitisunimodalonthenumberofallocatedroomsforearlydiscountandoverbooking.Aswith LaiandNg(2005), Liu,Lai,andWang(2008) presentrevenueoptimizationmodelsforamulti-daystayproblem
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