Revenue Management Games
|
|
- Timothy Norman
- 7 years ago
- Views:
Transcription
1 Revenue Management Games Sergue Netessne and Robert A. Shumsky 2 Unversty of Rohester W. E. Smon Graduate Shool of Busness Admnstraton Rohester, NY 4627 Otober, 2000 netessnse@smon.rohester.edu 2 shumsky@smon.rohester.edu
2 Revenue Management Games Abstrat: A well-studed problem n the lterature on arlne revenue or yeld) management s the optmal alloaton of seat nventory among fare lasses gven a demand dstrbuton for eah lass. In the lterature thus far, passenger demand s an exogenous parameter. owever, the seat alloaton desons of one arlne affet the passenger demands for seats on other arlnes. In ths paper we examne the seat nventory ontrol problem wth two fare lasses and two arlnes under ompetton. Eah arlne hooses an optmal bookng lmt for the lower-fare lass whle takng nto aount the overflow of passengers from ts ompettor. We show that under ertan ondtons ths 'revenue management game' has a pure-strategy Nash equlbrum, and for speal ases we show that the equlbrum s unque. We also ompare the total number of seats avalable n eah fare lass wth, and wthout, ompetton. Analytal results for one speal ase as well as numeral examples demonstrate that, all else equal, under ompetton more seats are proteted for hgher-fare passengers than when a sngle arlne ats as a monopoly or when arlnes form an allane to maxmze overall profts.
3 . Introduton Consder the arlne flght shedules dsplayed n able. We see that WA and Delta shedule flghts between the same orgn and destnaton, at exatly the same tmes, usng the same equpment, and hargng nearly the same pre for advane-purhase tkets. hs paper examnes how suh dret ompetton for ustomers affets a fundamental revenue management deson, the alloaton of seat nventory among fare lasses. Arlne Flght Arraft Departure Pre advane purhase) WA 3832 Jetstream 4 8:35am $23.50 Delta 622 Jetstream 4 8:35am $ WA 3834 Jetstream 4 2:24pm $23.50 Delta 6204 Jetstream 4 2:24pm $ WA 3836 Jetstream 4 5:50pm $23.00 Delta 6206 Jetstream 4 5:50pm $ able. Flght shedule for WA and Delta ROC-JFK, Aprl 23, here s a substantal lterature analyzng arlne eonoms under ompetton as well as a reent stream of operatons researh lterature on the problem of optmal seat alloaton. owever, there are no publshed papers that plae the seat alloaton problem n a ompettve framework. As the example above llustrates, arlnes fae ntense ompetton, and the mpat of ompetton on seat nventory desons and arlne revenues s of nterest to arlne plannng and marketng managers, as well as government regulators. In ths paper we model ompetton between two arlnes offerng two flghts that serve as substtutes for ustomers. Eah arlne s faed wth an ntal demand from passengers who wsh to purhase tkets, but eah arlne may also sell tkets to passengers that were dened a reservaton on the ompetng arlne. ene, the optmal apaty lmts for eah lass the bookng lmts) on eah arlne are nterdependent. We ompare the optmal revenue management poles of two ompetng arlnes wth the poly of a monopolst who operates both flghts or, equvalently, the poles of two arlnes who form an allane to maxmze total profts. We show that under ertan ondtons a pure-strategy Nash equlbrum exsts for the ompettve ase, and we dentfy speal ases under whh the equlbrum s unque and stable. Gven the assumpton that low and hgh-fare tket pres reman onstant, we fnd that under
4 ompetton more seats are alloated for hgher-fare ustomers, and fewer seats are alloated for the lower-fare ustomers, than under entralzed ontrol. Readers famlar wth the arlne ndustry may fnd ths uxtaposton of ompettve analyss and yeld management unusual. In general, ompettve desons and seat alloaton desons are made by dfferent funtonal unts wthn arlnes, at dfferent tmes n the plannng proess, and over extremely dfferent tme horzons. he deson to enter markets, the assgnment of arraft to partular markets, and the reaton of a shedule takes plae over a tme horzon of years and months. he alloaton of seat nventory among ustomer lasses s an operatonal deson wth a tme horzon of weeks, days, or even mnutes see Jaobs, Ratlff and Smth [2000] for a general desrpton of ths plannng proess). owever, our smple model wll show that ompetton on a partular route at a partular tme an have a profound effet on yeld management desons. In general, arlne planners have reently shown an nreased nterest n the ntegraton of arlne funtons. For example, numeral experments n Jaobs, Ratlff and Smth [2000] demonstrate the value of smultaneous optmzaton of yeld management and shedulng desons. Yuen and Irrgang [998] emphasze the benefts of ntegratng sales, yeld management, prng and shedulng desons. Publatons that onsder the nteratons among eonom fores, strateg arlne market entry desons, and arlne shedules nlude the network desgn models of ederer and Nambmamdom [999], Dobson and ederer [994], and the empral work by Borensten and Rose [994]. Another body of researh fouses on the arlnes' shedulng desons under ompetton usng varants of the spatal model developed by otellng [929]. See, for example, the reent empral papers by Borensten and Netz [999] and Rhard [999]. hese papers fous on broad ompettve problems and gnore the spefs of seat nventory alloaton. In ths paper we wll not be onerned wth the reasons arlnes shedule ther flghts at the same tme or wth the prng deson for eah flght. Rather, we wll onentrate on the mplatons of ompettve shedulng on seat nventory ontrol. here are numerous papers n the area of revenue management that fous on arlne seat nventory ontrol, although to our knowledge only one addresses the ssues desrbed here. For fundamental results on the general subet of seat nventory ontrol see Belobaba [989], 2
5 Brumelle et.al. [990], and a useful lterature revew by MGll and van Ryzn [999]. Our paper s related to the work by and Oum [998], whh frst ntrodued the seat alloaton problem for two arlnes n ompetton. he model developed by and Oum has a relatvely restrtve assumpton about how demand s alloated among arlnes: total demand s splt aordng to the proporton of seats avalable on eah arraft n eah fare lass, and the overflow proess s not expltly modeled. In addton, the paper by and Oum dentfes one, symmetr equlbrum but does not determne whether the equlbrum s unque. Our approah s more general and the results more advaned; we wll plae no restrtons on how ntal demand s dstrbuted and wll show that for speal ases of the problem the equlbrum s unque. he lterature on nventory management has seen a stream of losely related papers devoted to ompetton among frms n whh the frms determne nventory levels and ustomers may swth among frms untl a sutable produt s found ths has been desrbed as a 'newsboy game'). Parlar [988] examnes the ompetton between two retalers fang ndependent demands. e establshes that a unque Nash equlbrum exsts. Karalanen [992] formulates the problem for an arbtrary number of produts wth ndependent demands. ppman and MCardle [997] examne both the two-frm game and a game wth an arbtrary number of players. In ther models, ntal ndustry demand s alloated among the players aordng to a pre-spefed 'splttng rule.' hs ntal alloaton may be ether determnst e.g., 40% of demand to player ) or stohast the rule tself depends on the outome of a random experment). For the two-frm game they establsh the exstene of a pure-strategy Nash equlbrum and show that the equlbrum s unque when the ntal alloaton s determnst and strtly nreasng n the total ndustry demand for eah player. Reent extensons of these models nlude Mahaan and van Ryzn [999] who model demand as a stohast sequene of utlty-maxmzng ustomers. For an arbtrary number of frms, they demonstrate that an equlbrum exsts and show that t s unque for a symmetr game. Rud and Netessne [2000] analyze a problem smlar to Parlar [988] but for an arbtrary number of produts. Gven mld parametr assumptons they establsh the exstene of, and haraterze, a unque, globally stable Nash equlbrum. Many of these papers ompare total nventory levels under frm ompetton wth nventory levels when frms ooperate. ppman and MCardle [997] show that ompetton 3
6 an lead to hgher nventores, and Mahaan and van Ryzn [999] derve smlar results gven ther dynam model of ustomer purhasng. On the other hand, wth the substtuton struture of the model of Rud and Netessne [2000], under ompetton some frms may stok less than under entralzaton. In ths paper we fnd that ompetton leads to an nrease n hgh-fare seat 'nventory,' a result smlar to earler fndngs. owever, our model dffers n many respets from the newsboy ompetton desrbed by ppman and MCardle. As n Mahaan and van Ryzn, the method of alloatng arrvng ustomers to frms s more natural than the stylzed splttng rules proposed by ppman and MCardle. In our model, demand for eah fare lass on eah arlne an follow an arbtrary dstrbuton, and we allow an arbtrary orrelaton struture among demands. Numeral experments wll demonstrate that the degree of orrelaton an have a sgnfant mpat on seat alloaton desons, and an even determne whether a pure-strategy equlbrum exsts. here s also a fundamental dfferene between the problem onsdered here and the problem onsdered n the nventory lterature. ere we onsder the alloaton of a fxed nventory pool between two produts, whle the nventory lterature assumes that the nventory of eah produt s a deson varable. In our problem, the effet of a hange n one arlne's bookng lmt s qute omplex. As the bookng lmt rses, demand by low-fare passengers to a ompettor delnes whle hgh-fare demand to the ompettor rses. In addton, we wll see that the bookng lmt of an arlne an affet the volume of ts own hgh-fare demand. In the next Seton we desrbe the revenue management game and provde examples of senaros n whh Nash equlbra do, and do not, exst. We examne one varaton of the game for whh we establsh the exstene of a pure-strategy Nash equlbrum. Seton 3 fouses on ompetton wth partal overflow, models n whh only low-fare or only hgh-fare passengers spll to a ompetng arlne. In Seton 4 we ompare analytally the behavor of a monopolst or allane between arlnes) wth the behavor of two arlnes under ompetton. Seton 5 desrbes numeral examples and ompares the serve levels perentage of ustomers who are able to purhase tkets) under the ompettve and ooperatve ases. In Seton 6 we dsuss the mplatons of our results on the prate of yeld management and desrbe areas for future researh. 4
7 2. he Full Revenue Management Game Suppose two arlnes offer dret flghts between the same orgn and destnaton, wth departures and arrvals at smlar tmes. We assume that other flghts on ths route are sheduled suffently far away n tme so that they an be gnored. For smplty, we assume that both flghts have the same seat apaty and that there are only two fare lasses avalable for passengers: a 'low-fare' and a 'hgh-fare.' A tket purhased at ether fare gves aess to the same produt: a oah-lass seat on one flght leg. As s tradtonal n the lterature on arlne seat nventory ontrol, we assume that demand for low-fare tkets ours before demand for hgh-fare tkets, as s the ase when advane-purhase requrements are used to dstngush between ustomers wth dfferent valuatons on pre and purhase onvenene. Customers who prefer a low fare and are wllng to aept the purhase restrtons wll be alled 'low-fare ustomers'. Customers who prefer to purhase later, at the hgher pre, are alled 'hgh-fare ustomers'. We also assume that there are no ustomer anellatons. o maxmze expeted profts, both arlnes establsh bookng lmts for low-fare seats. One ths bookng lmt s reahed, the low fare s losed. Sales of hgh-fare tkets are aepted untl ether the arplane s full or the flght departs. If ether type of ustomer s dened a tket at one arlne, the ustomer wll attempt to purhase a tket from the other we all these overflow passengers ). herefore, both arlnes are faed wth a random ntal demand for eah fare lass as well as demand from ustomers who are dened tkets by the other arlne. Passengers dened a reservaton by both arlnes are lost. Fgure shows both overflow proesses as well as the followng notaton:, = passenger lasses, for low-fare passengers and for hgh-fare passengers. C = apaty of the arraft. B = bookng lmt for low fare establshed by arlne =,2. D k = a random varable, demand for lass k tkets at arlne, k =, and =, 2. p k = revenue from lass k=, passengers less varable ost. 5
8 Arlne Arlne 2 D C gh fare lass hgh-fare overflow from to 2 hgh-fare overflow from 2 to gh fare lass C D2 B B2 D ow fare lass low-fare overflow from to 2 low-fare overflow from 2 to ow fare lass D2 Fgure. he Bas Compettve Model We assume that both arlne's pres are the same and that p < p. We also assume that the random varables D k have nonnegatve support. owever, to derve the followng results establshng the exstene of a pure-strategy equlbrum we do not assume that the umulatve dstrbutons are ontnuous we may have dsrete or ontnuous probablty dstrbutons), and there may be an arbtrary orrelaton struture among demands. In Seton 3, however, to establsh the exstene of a unque equlbrum we wll assume that fnte probablty denstes exst and that low and hgh-fare demands are ndependent. In ths paper we study ompetng arlnes engagng n a nonooperatve game wth omplete nformaton. Eah arlne attempts to maxmze ts profts by adustng ts bookng levels. In other words, the bookng level B [0,C] s the strategy spae of arlne for smplty, we assume that the bookng level may be any real number n ths range). Eah arlne knows the strategy spaes and demand dstrbutons of ts own flght as well as those of the ompetng arlne. An mportant assumpton of the model s that the ntal demands D k are exogenous; they are not affeted by the bookng lmts hosen by eah arlne. hs assumpton s onsstent wth the newsboy game models of Parlar, Karalanen, and ppman and MCardle. owever, one mght argue that the bookng lmts determne seat avalablty, and that n the long run ths aspet of serve qualty affets ntal demand. A more omplete model would norporate ths relatonshp between bookng lmts and demand, and the soluton would supply equlbrum demands as well as equlbrum bookng lmts. For our applaton, however, the relatonshp between bookng lmts and demand s weakened by marketng efforts suh as advertsng and frequent-flyer programs. In addton, the use of travel agents and on-lne reservaton tools 6
9 redues the margnal searh ost assoated wth makng eah bookng. Gven low searh osts, the deson as to whh arlne to query frst may depend on fators that domnate the lkelhood that the query wll result n a bookng. Our model smplfes other aspets of the atual envronment. For example, the model assumes that passengers dened a tket n one lass do not attempt to upgrade or downgrade to another lass. he model also assumes that a passenger, when frst dened a tket, wll not shft to a later or earler flght operated by the same arlne. owever, all results presented n ths paper also apply to a model n whh some fraton less than one) of passengers dened a tket on one arlne attempt to purhase a tket from the other arlne, whle some fraton greater than zero) are lost to both arlnes. o smplfy the model and mnmze the number of parameters, we assume that all passengers dened a tket from ther frst hoe overflow to ther seond-hoe arlne. he model ontans only two fare lasses, when n realty there may be many more see Belobaba [998] for an ntroduton to the omplextes of real-world yeld management systems). We also assume that the arlnes' bookng lmts are stat. hat s, the bookng lmt s set before demand s realzed and no adustments are made as low-fare demand s observed. As we wll see, even ths relatvely smple deson an be dffult to analyze n a ompettve game, and ths smple model allows us to fous on a few mportant questons. ow wll an optmal bookng lmt under ompetton dffer from a bookng lmt under a entralzed soluton, wth a sngle arlne or when two arlnes ooperate to maxmze total profts? ow does the exstene of 'spll' demand affet the alloaton of seat nventory? What s the effet of ompetton on profts, even when pres are held onstant? 2. ow-fare then gh-fare Spll hus far we have not desrbed the order of events n the game. We begn wth what may be the most natural order:. Arlnes establsh bookng lmts B and B ow-fare passengers arrve to ther frst-hoe arlnes and are aommodated up to the bookng lmts. 7
10 3. ow-fare passengers not aommodated on ther frst-hoe arlnes 'spll' to the alternate arlnes and are aommodated up to the bookng lmts. 4. gh-fare passengers arrve to ther frst-hoe arlnes and are aommodated wth any remanng seats, up to apaty C n eah arraft. 5. gh-fare passengers not aommodated on ther frst-hoe arlnes 'spll' to the alternate arlnes and are aommodated n any remanng seats, up to apaty C n eah arraft. o desrbe the problem n terms of ustomer demand and bookng lmts, defne: D = D D B ), total demand for low-fare tkets on arlne, =, =2 and =2, =. R = C mn D, B ), the number of seats avalable for hgh-fare passengers on arlne =,2. D = D D R ) he total revenue for arlne s, total demand for hgh-fare tkets, =, =2 and =2, =. [ p mn D, B ) p mn D, R )] π = E. ) Eah arlne wll maxmze ths expresson, gven the bookng lmt of ts ompettor. It wll be nstrutve to examne the frst dervatve of ths obetve funton. It s tedous to fnd the dervatve by the tradtonal methods e.g., applyng ebntz's rule). Instead, by applyng the tehnques desrbed n the Appendx of Rud and Netessne [2000], we fnd for =, =2 and =2, =, π B = p p Pr D Pr D > B ) p > B, D Pr D < B, D > C B, D > R, D > B ) < C B ). 2) Although ths s a omplex expresson, there s a straghtforward nterpretaton for eah term. An nremental nrease n the bookng lmt B by arlne has three effets on that arlne's total revenue. Frst, revenue from low-fare ustomers nreases wth probablty Pr D > B ). Seond, revenue from the hgh-fare ustomers dereases wth probablty Pr D > C B, D > B ). Whle these two effets are dret onsequenes of the hange n B, there s a thrd, ndret effet. Revenue from hgh-fare ustomers may derease beause ) an 8
11 nrease s B may redue the overflow of low-fare ustomers from to, ) a reduton n the number of low-fare ustomers at may nrease the number of seats avalable for hgh-fare ustomers at, ) ths may redue the overflow of hgh-fare ustomers from to and v) a delne n the overflow from may redue the number of hgh-fare ustomers aommodated at. he probablty of ths sequene of events s the thrd term on the rght-hand sde of equaton 2), whh mples that an nrease n the bookng lmt of arlne an result n a derease n hghfare demand to arlne. Beause the strategy spaes of the arlnes are ompat and the payoff funtons are ontnuous see Proposton, below), a Nash equlbrum n mxed strateges must exst. owever, a pure-strategy Nash equlbrum may, or may not, exst for arlnes playng ths game. Fgure 2 shows the best reply funtons, or reaton funtons r B ), of two arlnes, eah wth C =200 and multvarate normal demands the parameters for ths example wll be desrbed n detal n Seton 5). Fgure 3, showng a game wth multple equlbra, was also generated wth the multvarate normal dstrbuton agan, detals are gven n Seton 5). Fgure 4 dsplays two reaton funtons, eah wth two dsontnutes, produng a game wthout any pure-strategy equlbrum. An extremely unlkely demand pattern was used to produe ths outome. Fgure 4 was generated from: Bmodal demand dstrbutons for eah fare lass and arlne. he dstrbutons were reated by mxng two normal dstrbutons, one representng low-volume demand mean = 20 seats) and the other representng hgh-volume demand mean = 50 seats). Strong negatve orrelatons between low-fare and hgh-fare demands. When low-fare demand was hosen from the low-volume dstrbuton, hgh-fare demand was hosen from the hgh-volume dstrbuton, and ve-versa. As a result, ρ D, D ) = 0. 9 for =,2. 3 A large dfferene between hgh and low fares p / = 4 ). p 3 It s nterestng to note that n prate the strong negatve orrelaton would present an exellent opportunty for eah arlne to prate dynam yeld management, wth an adustable bookng lmt dependent on observed lowfare demand. Gven suh dynam deson-makng, there may well be a ompettve equlbrum. 9
12 B r2b) B r2b) rb2) 50 rb2) B Fgure 2: Unque Nash Equlbrum B Fgure 3: Multple Equlbra r 2B ) 50 r B 2) B Fgure 4: No Pure-Strategy Equlbrum B Whle we annot spefy analytally the general haratersts that would guarantee the exstene of an equlbrum, expresson 2) offers some nsght. For most reasonable probablty dstrbutons and for most values of B and B 2, the frst two terms domnate the thrd term, so that π B p Pr D > B ) p Pr D > C B, D > B ). 3) hs expresson s smlar to the frst-order ondtons for the standard two-fare seat alloaton problem of a stand-alone arlne, although here exogenous demands D k have been replaed by total demands D. Brumelle et.al [990] show that when the demands are monotonally k assoated, so that Pr D > C B D > B ) s nondereasng n B, then the obetve funton of arlne s quas-onave n B. Gven that the two players fae obetve funtons that are ontnuous and quas-onave n eah bookng lmt, there exsts a pure-strategy Nash equlbrum Mouln, 986). 0
13 hs reasonng does not provde us wth prese ondtons for the exstene of an equlbrum, but we have found ths analyss to be helpful when examnng the results of our numeral examples. When demands D and D and D are strongly negatvely orrelated then the total D are not monotonally assoated. In ths ase, the obetve funtons for eah arlne are not unmodal, produng the dsontnutes n the reaton funtons shown n Fgure 4. When orrelated, D and D are weakly negatvely orrelated, ndependent, or postvely D and D mantan the postve assoaton property and a pure-strategy equlbrum exsts. We wll see n Seton 5 that the latter ase apples for most reasonable problem parameters. Now we do dentfy two suffent ondtons for the exstene of a pure-strategy equlbrum. Frst, f low-fare demand s extremely hgh so that Pr D > C) = for =,2, then a pure-strategy equlbrum must exst and, under ertan ondtons, the equlbrum must be unque and stable. In ths ase, low-fare overflow s gnored by eah arlne beause there s already a surplus of low-fare ustomers, and arlnes only ompete for hgh-fare ustomers. Beause ths s a speal ase of the model presented n Seton 3., further dsusson and a proof wll be presented later see Proposton 3 and Corollary ). he seond ondton nvolves a revson of the tmng of the game. hs s presented n the next seton. 2.2 gh-fare then ow-fare Spll We wll now hange the order of events and assume that low-fare ustomers that overflow are aepted only after all other passengers have been aommodated. he order of events s as follows:. Arlnes establsh bookng lmts B and B ow-fare passengers arrve to ther frst-hoe arlnes and are aommodated up to the bookng lmts. 3. gh-fare passengers arrve to ther frst-hoe arlnes and are aommodated wth any remanng seats, up to apaty C n eah arraft. 4. gh-fare passengers not aommodated on ther frst-hoe arlnes 'spll' to the alternate arlnes and are aommodated n any remanng seats, up to apaty C n eah arraft.
14 5. ow-fare passengers not aommodated on ther frst-hoe arlnes 'spll' to the alternate arlnes and are aommodated up to the bookng lmts. o mantan the flavor of the tmng desrbed n Seton 2., n Step 5 we only book low-fare passengers up to the bookng lmt, even f addtonal seats are avalable. Note that ths game requres eah arlne to dstngush between low-fare passengers who hoose that arlne frst from those that ome to the arlne as a seond hoe. Whle ths may not always be possble, under ths re-orderng, t s possble to establsh the exstene of a pure-strategy Nash equlbrum beause an adustment n B does not affet the hgh-fare demand faed by arlne. Frst defne: mn D, B ), number of low-fare tkets sold n the frst round D = D D C mn B, D )), total demand for hgh-fare tkets at arlne D B ), overflow of low-fare passengers mn B, C D ) D ), number of seats avalable to the overflow low-fare passengers. he total revenue for arlne s: p mn D, B) π = E 4) p mn D B ), mn B, C D) D ) ) p mn C mn B, D ), D) Proposton. Gven the game orderng defned by steps -5 above, a pure-strategy Nash equlbrum n bookng lmts B, B 2 ) exsts. Proof: We wll show that the obetve funton for eah player s ontnuous and submodular n B, B 2 ). herefore, the obetve funton s ontnuous and supermodular n B, -B 2 ), whh are suffent ondtons for the exstene of a pure-strategy Nash equlbrum opks, 998). o see that the obetve funton s ontnuous, note that the strategy spae s fnte so that for any gven demand realzaton the obetve funton s bounded. In addton, the obetve funton s ontnuous n B, B 2 ) for any gven demand realzaton. herefore, by the bounded onvergene theorem, the expetaton 4) s ontnuous Bllngsley, 995). 2
15 o prove submodularty, note that the expetaton of a submodular funton s submodular, the sum of submodular funtons s a submodular funton, and a submodular funton multpled by a postve onstant s a submodular funton opks, 998, emma 2.6. and Corollary 2.6.2). herefore, we wll prove that for any gven demand realzaton, eah of the three terms n the sum 4) s submodular n B, B ). he frst term, mn D, B ), depends only on B, so t s submodular. For the last two terms we wll employ the followng two lemmas for the sake of readablty, n these lemmas and for the remander of the proof the term 'nreasng' mples nondereasng and the term 'dereasng' mples nonnreasng): emma Adopted from opks, 998, Example f).) If g B ) s nreasng and g B ) s dereasng then mn g B ), g B )) s a submodular funton n B, B ). emma 2 opks, 978, able ) Suppose g B, B ) s nreasng n both B and B and s a submodular supermodular) funton n B, B ). Also suppose that f z) s an nreasng onave onvex) funton. hen f g B, B )) s a submodular supermodular) funton n B, B ). mn We re-wrte the seond term of the obetve funton as D ) ) B ), mn B, C D ) D = D B ) mn 0, mn B, C D ) D ) D B ) ). 5) he term D B ) depends only on B and hene s submodular. o prove that the seond term n 5) s submodular, we wll employ emmas and 2. Sne f z) = mn 0, z) s a onave nreasng funton of z, t remans to show that mn B, C D ) D ) D B g B, B ) = ) 6) s an nreasng submodular funton. We frst show that t s an nreasng funton. It s obvous that ths funton s nreasng n B. Further, from the defnton of B, C D ) D ) D above, mn s ether lnearly dereasng n B for some demand realzatons) or does not hange as B hanges. In addton, ) D B s also ether lnearly nreasng or 3
16 nvarant n B. By examnng the two terms n 6), we see that when the seond term s lnearly nreasng n B then the frst term s ether lnearly dereasng or does not hange. When the frst term s lnearly dereasng n B then the seond term must be nreasng. herefore, the seond term domnates, and g B, B ) s nreasng n both B and B. We now show that g B, B ) s also submodular. Frst, mn B, C ) s nreasng D n B, dereasng n B, and by emma a submodular funton n B, B ). herefore, mn B, C D ) D s nreasng and supermodular n B, -B ). In addton, the funton f z) = z) = max 0, z) s onvex and nreasng n z, so that by emma 2 B, C D ) D ) mn s a supermodular funton n B, -B ) and therefore a submodular funton n B, B ). ene, g B, B ) s also a submodular funton. hs ompletes the proof for the seond term of 4). he thrd term of the obetve funton s C mn B, D ), D ) C mn B, D ) s dereasng n B, and C mn B, D ), D ) mn. Note that D s nreasng n B. By emma, mn s submodular. hs ompletes the proof. Whle we an be sure of a pure-strategy equlbrum n ths ase, we annot be sure that the equlbrum s unque. Condtons for unqueness wll be desrbed n the next seton. 3. Competton wth Partal Overflow In ths seton we onsder ompetng arlnes wth only hgh-fare passengers overflowng from one arlne to another Seton 3.) and wth only low-fare passengers overflowng Seton 3.2). For eah ase we wll fnd ondtons under whh a pure-strategy equlbrum exsts and s unque. It s, of ourse, reasonable to ask why we should be onerned wth these speal ases sne both hgh and low-fare ustomers are lkely to look for a seat on another arlne f one annot be found on the preferred arlne. In fat, these speal ases are good approxmatons of the general game desrbed n Seton 2., as long as the number of overflow ustomers from one of the two fare lasses s small. In addton, the model to be presented n Seton 3. 4
17 nludes the ase when hgh-fare passengers swth arlnes whle demand for low-fare tkets s suffently large to sell all avalable low-fare tkets. Moreover, analyss of these speal ases sheds some lght on the reasons why the full game presented n Seton 2 may fal to have a pure-strategy equlbrum. We wll see here that a game wth only hgh-fare overflow always has a pure-strategy equlbrum, whle a game wth only low-fare overflow may not. If only hgh-fare ustomers spll to a ompettor, then the arlnes are nvolved n a supermodular game smlar to the nventory game desrbed by Parlar and by ppman and MCardle. In terms of yeld management, an nrease n the bookng lmt by one arlne nreases demand by hgh-fare passengers to the ompettor, thus lowerng the ompettor's bookng lmt. Eah player's reaton funton s monoton n the other player's strategy, and an equlbrum must exst. owever, when both types of overflow our the response funtons need not be monoton, as n Fgure 4. Addtonal ondtons are needed to establsh the exstene of a pure-strategy equlbrum. 3. gh-fare Overflow Only We now assume that there s no overflow of the low-fare passengers and only hgh-fare passengers approah the other arlne when ther frst-hoe arlne s not avalable. Fgure 5 llustrates the flow of passengers. Note that the followng defntons dffer slghtly from the 'full-overflow' ase presented n Seton 2.. R = C mn D, B ), the number of seats avalable for hgh-fare passengers on arlne. D = D D R ), total demand for hgh-fare tkets on arlne, =, =2 and =2, =. 5
18 D Arlne Arlne 2 gh-fare lass D C D, ))) mn B D C D, ))) mn B gh-fare lass D2 B B2 mnb 2,D2) mnb,d) D ow-fare lass ow-fare lass D2 Fgure 5: gh-fare passengers overflow he total revenue for arlne s π = E [ p mn D, B ) p mn D, R )].. 7) he frst dervatve of the obetve funton wll be useful n the followng theorems. We fnd π B = p Pr D > B ) p Pr D > C B, D > B ). 8) he exstene of a pure-strategy Nash equlbrum, establshed n the followng proposton, follows from the supermodularty of the game. hs result holds for any demand dstrbuton, nludng dstrbutons wth orrelaton among arlnes and among fare lasses. As was the ase wth Proposton, the demand dstrbuton may be ontnuous or dsrete. Proposton 2. Gven overflow by hgh-fare ustomers only, a pure-strategy Nash equlbrum n bookng lmts B, B 2 ) exsts. Proof: By the reasonng presented n the proof of Proposton, the obetve funton 7) s ontnuous. We wll now show that both mn D, B ) and mn D, R ) are submodular, so that the obetve funton s submodular for any gven demand realzaton and therefore the expetaton 7) s submodular. hs s suffent to establsh the exstene of a pure-strategy Nash equlbrum opks, 999). 6
19 Observe that mn D, B ) depends only on B and hene s submodular. By the defntons above, R s dereasng n B and D s nreasng n B. By emma n the proof of Proposton, mn D, R ) s submodular. o show there s a sngle, unque equlbrum, we make the followng assumptons: Assumpton : here exsts, for eah random varable, a fnte probablty densty funton f τ ) = d Pr D < τ ) dτ. In addton, the densty funtons τ ) > 0 D k k / =,2. f for 0 τ C and Assumpton 2: Demands for low-fare and hgh-fare tkets are ndependent. More formally, let D = D, D 2) and D = D, D 2). We assume that D and D are mutually ndependent. Assumpton 3: PrD > C) > 0 for =,2. D Proposton 3. Gven overflow by hgh-fare ustomers only and Assumptons -3, there s a unque, globally stable Nash equlbrum n B, B 2 ). Proof: We wll haraterze the best reply funtons reaton funtons) of the players n the game and then wll show that the funtons are a ontraton on B, ). herefore, a sngle, unque equlbrum exsts and s stable. We wll frst show that eah funton π, wth B2 B held onstant, reahes ts maxmum at a unque pont B [ 0, C). Gven Assumpton 2, the frst dervatves of the obetve funtons may be wrtten as π B = Pr D > B ) p p Pr D > C B )) =,2. 9) From Assumpton 3, the frst dervatve s always less than zero at the upper boundary C: π B B= C = Pr D > C) p p Pr D > 0)) = Pr D > C) p p ) < 0. 0) Now onsder two ases. If Pr D > C) < p / p then 9) s postve when evaluated at the lower boundary: 7
20 π B B= 0 = Pr D > 0) p p Pr D > C)) = p p Pr D > C) > 0. ) By assumpton, Pr D > C B ) s strtly nreasng n B, and the obetve funton s strtly quas-onave n the nterval [0, C]. If there s an nteror soluton t s determned by the followng frst-order ondtons note that we have expanded the termd ): p Pr D D C mn D, B )) > C B ) =, =, = 2 and = 2, =. 2) p If Pr D > C) p / p, then the obetve funton s not nreasng at 0 and the slope does not hange sgn n the nterval [0, C]. herefore, the obetve s maxmzed at B = 0. Equaton 2) and the boundary ondton spefy reaton funtons r ) and r ) for B2 the two players. When the value of the reaton funton s n the nteror 0, C) then mplt dfferentaton of 2) fnds the magntudes of the slopes of the reaton funtons: 4 2 B r B ) f D = B D> C B C B f D ) Pr C B D ) > C B ) <. 3) If the value of the reaton funton s a boundary soluton, B = 0, then r B ) / B = 0 <. herefore, the reaton funtons B ), r )) r are a ontraton on B, ). 2 2 B B2 From Proposton 2, we know that at least one equlbrum pont exsts. From the proof of heorem 2.5 of Fredman [986], f there s at least one equlbrum pont and the reaton funton s a ontraton then the game has exatly one equlbrum pont. In addton, the expresson for the dervatve n 3) mples that r B B 2 2 ) r2 B) B < 4) so that the equlbrum s stable Mouln, 986). 4 In 3), the expresson τ ) f represents the densty funton of D gven event A. D A 8
21 hs result allows us to say somethng stronger about the full-overflow ase of Seton 2. when low-fare demand s suffent to fll both arraft. Corollary. Assume overflow by both low-fare and hgh-fare ustomers. Gven Assumptons and 2, and gven that low-fare demand s extremely large Pr D > C) = for =,2), there s a unque, globally stable Nash equlbrum n B, B 2 ). Proof: In ths ase, arlnes only ompete for hgh-fare ustomers and the overflow of low-fare ustomers an be gnored beause there are no extra seats to aommodate them. In the full model obetve funton ), we replae mn D, B ) wth B. hs s a speal ase of the model examned n Seton 3.. herefore, the unqueness and stablty results hold here. 3.2 ow-fare Overflow Only We wll now assume that hgh-fare passengers do not overflow and that only low-fare passengers swth arlnes f ther frst hoe s fully booked see Fgure 6). Arlne Arlne 2 D Full fare lass Full fare lass D2 B B2 MnB 2,D 2) MnB,D ) D Dsount fare lass D - B) D2 - B2) Dsount fare lass D2 Fgure 6. ow-fare passengers overflow Frst defne: D = D D B ), total demand for low-fare tkets on arlne, =, =2 and =2, =. R = C mn D, B ), the number of seats avalable for hgh-fare passengers on arlne. 9
22 he number of low-fare tkets sold s equal to mn D, B ) and the total revenue for arlne s [ p mn D, B ) p mn D, R )] π = E. 5) Surprsngly, a pure-strategy equlbrum need not exst for ths smple game. he obetve funton s not neessarly submodular or quas-onave. owever, under Assumptons -3 the equlbrum s unque and stable. Proposton 4. Gven overflow by low-fare ustomers only and Assumptons -3, there s a unque, globally stable Nash equlbrum n B, B 2 ). Proof: Gven ndependene between hgh and low-fare demands, the frst dervatve of the obetve funton 5) s π B = Pr D > B ) p p Pr D > C B )). 6) he obetve funton s quas-onave on [0,C] and t an be shown that the optmal soluton s always n the nteror, 0,C). he frst-order ondton p Pr D > C B ) = 7) p depends only on B and not on the ompettor's aton B. herefore, 7) defnes the unque optmal soluton for eah arlne and eah reaton funton has a slope of zero. he reaton funtons are a ontraton on B, ) and, followng the reasonng of the proof of Proposton B2 3, ths ontraton leads to a unque, globally stable equlbrum. hs soluton s dental to the soluton for a stand-alone arlne. When hgh-fare ustomers do not swth arlnes and hgh-fare and low-fare demands are ndependent, the optmal bookng lmts for both stand-alone arlnes and arlnes n ompetton are not nfluened by the demand dstrbutons of low-fare ustomers. 4. Comparng the Compettors and a Monopolst We wll now ompare the behavor of two arlnes n ompetton wth the behavor of a monopolst. Note that the term 'monopolst' does not neessarly mply that a sngle frm s the 20
23 only arrer on a partular route. he 'monopolst' may be two arlnes n an allane to oordnate yeld management desons. In addton, two arlnes may ompete on a partular route at ertan tmes of day, whle eah arlne may hold a vrtual monopoly at other tmes of day beause ts ompettor has not sheduled a ompetng flght at a pont lose n tme. For example, Unted Arlnes has the only dret flght from Rohester, NY to the Washngton DC area n the evenng, whle most of ts flghts durng the mornng and afternoon ompete dretly wth flghts by US Arways. In general, we wll fnd that the total bookng lmt for the monopolst s never less than the sum of the bookng lmts of two ompetng arlnes. In ths seton we provde a proof of ths result, gven a model wth hgh-fare overflow only the model presented n Seton 3.). In the followng seton we wll present numeral experments utlzng the full model of Seton 2.. o smplfy the omparson, we assume that the pre raton p /p and the dstrbutons of onsumer demands D k are equal under the ompettve and monopoly envronments. In Seton 6 we wll dsuss the mplatons of these assumptons. Our results are onsstent wth the fndngs of ppman and MCardle [997], who analyze ompetng newsvendors. hey fnd that ompetton never leads to a derease n total nventory. he 'nventory' of eah newsvendor s analogous to the stok of proteted hgh-fare seats, C B, and the demand for newspapers s analogous to demand by hgh-fare ustomers. owever, our problem norporates a sgnfant omplaton, the stohast demand by lowfare, as well as hgh-fare, ustomers. Frst we revew the ase wth no ompetton and only one arraft wth apaty C n the market for further detals, see Belobaba, 989, and Brumelle et.al., 990). Sne there s ust one arraft, we wll suppress the subsrpt =,2 whh denotes the arraft n the ompettve ase. After establshng a bookng lmt B, the arlne wll sell mn D, B) low-fare tkets and mn D, C mn D, B) ) he frst dervatve s hgh-fare tkets. herefore the total revenue s [ p mn D, B) p mn D, C mn D, B) )] π = E. 8) D F π B = p Pr D > B) p Pr D > C B, D > B). 9) 2
24 As mentoned n Seton 2, the frst-order ondtons are suffent for a soluton when Pr D > C B D B) s nondereasng n B [Brumelle et al. 990]. Note that ths ondton > s satsfed f D and D are ndependent. Gven ths property, a soluton B * wthn the nterval 0,C) satsfes 5 * * Pr D > C B D > B ) = p p. 20) Now onsder an arlne wth a monopoly or an allane between two arlnes) operatng two flghts. Passenger arrvals and overflows follow the order of events desrbed by Steps -5 at the begnnng of Seton 2.. Whle ths ase may seem to be more omplex than the sngleflght problem, t redues to the smpler problem desrbed above, sne the passenger overflow from one arraft s aptured by the same frm n the other arraft. We an wrte the obetve funton n ths two-arraft ase as [ p D D, B B ) p mn D D, 2C mn D D, B )] π = E mn B2 2) and the frst dervatve s smlar to 9) above, wth B = B B2 : π B = p Pr D D2 > B ) p Pr D D 2 > 2C B, D D2 > B ). 22) Now we onsder the stuaton ntrodued n Seton 3.. Assume that low-fare ustomers do not overflow to a seond-hoe flght whle hgh-fare passengers do overflow. he obetve funton for the monopoly arlne s p π = E mn D ) ), B) mn D2, B2 ) p mn D D 2,2C mn D, B) mn D2, B2 ) * * An nteror soluton B, ) satsfes the followng frst-order ondtons for =, =2 and =2, =: 6 B2. 23) 5 here s also a boundary ondton. If Pr D > C) p / p then B * = 0. 6 Agan, there are boundary ondtons. We present ondtons for 'extreme' solutons here. If * * Pr D D > 2C ) p / p then B, B ) = 0,0). If Pr D D > C mn D, C) p / p for =,2, then * * B, B ) = C, ). C 2 22
25 π B * B *, B ) = p Pr D > B * ) p Pr D D 2 > 2C B * mn D, B * ), D > B * ) = 0. 24) * * here may be multple values of B, ) that satsfy 24). B2 hs frst-order ondton and the frst-order ondtons 2) that unquely determne the ompettve equlbrum allow us to ompare, analytally, the entralzed and ompettve solutons. Proposton 5. Assume overflow by hgh-fare ustomers only and Assumptons -3. Also assume that the optmal soluton for the monopolst as well as the equlbrum under ompetton are n the nteror, e.g., B 0, C). hen the total number of proteted seats, B B 2, s lower under ompetton than under the entralzed soluton. Proof: Defne a B, =,2, as the optmal desons for the monopolst 'a' for allane) and defne B, =,2, as the equlbrum desons under ompetton. he allane soluton s determned by the frst-order ondtons, equatons 24). Gven Assumptons and 2, these frst-order ondtons may be re-wrtten as a a D > 2C B mn D, B )) Pr D 2 = p for =, =2 and =2, =. 25) p he ompettve optmalty ondtons 2) may be re-wrtten as: Pr D D > 2C B mn D, B )) 2 Pr D D < 2C B mn D, B ), D > C B ) 2 = p p 26) for =, =2 and =2, =. Note that 25) and 26) dffer by a sngle probablty term n the lefthand sde of 26). Sne ths extra term n nonnegatve and the rght-hand sdes are equal, the followng nequaltes hold smultaneously: a a D D > 2C B mn D, B )) Pr D D > 2C B mn D, B )) Pr a a D D > 2C B mn D, B )) Pr D D > 2C B mn D, B )) Pr Sne ths must be true for any value of C, these nequaltes defne stohast orders on two pars, 27). 28) 23
26 of sngle-valued funtons of random varables algebra manpulaton, 27) and 28) may be re-wrtten as where D and D. o make ths lear, after some a a a D D2 mn B D2, B B2 ) st D D 2 mn B D2, B B2 ), 29) a a a D D 2 mn B2 D, B B2 ) st D D2 mn B2 D, B B2 ), 30) X st Y ndates that X s smaller than Y n the usual stohast order. Beause of the ndependene between low-fare and hgh-fare demands Assumpton ) and the preservaton of stohast order under onvoluton Shaked and Shanthkumar, 994), a a a mn B D2, B B2 ) st mn B D2, B B2 ), 3) a a a mn B 2 D, B B2 ) st mn B2 D, B B2 ). 32) Fnally, by ontradton, assume that a a B B2 > B B2. hen for both nequaltes 3) and 32) to hold we would need smultaneously assumpton. ene, a a B B2 < B B2. a B B < and a B2 < B2, whh s nonsstent wth the Proposton 5 mples that, under ompetton, at least as many seats are held for hgh-fare ustomers as s optmal under ont proft maxmzaton. For the monopolst, every hgh-fare passenger who does not fnd a seat at arlne and turns to arlne s not 'lost' to the frm. Under ompetton, however, when arlne establshes a lower bookng lmt, arlne lowers ts bookng lmt as well as the two arlnes ompete for hgh-fare passengers. 5. Numeral Experments o determne whether the prevous seton's results apply to the full-fledged game desrbed n Seton 2., we alulate numerally both the ompettve equlbrum and the optmal monopoly soluton under a wde varety of parameter values. Our goal s to see whether the bookng lmt set by the monopoly, a a B B2, s onsstently greater than or equal to the total bookng lmt under ompetton, B B2. For eah senaro, demand s dstrbuted aordng to a multvarate normal dstrbuton and trunated at zero; any negatve demand s added to a mass pont at zero. Solutons are found by a 24
27 smple gradent algorthm and the gradents themselves, expressons 2) and 22), were evaluated by Monte Carlo ntegraton a smple searh proedure was also used f the obetve funton was not quas-onave). he senaros are reated by ombnng the followng parameters. - Rato of hgh fare to low fare: o over a range that nludes many atual pre ratos, we use the followng values: p / p = [.5, 2, 3, 4]. - Proporton of demand due to low-fare passengers: et µ µ ) be the average low-fare hgh-fare) demand for arlne, =,2. Beause n prate low-fare demand s often greater than hgh-fare demand, we assume that µ, and we use proportons µ µ ) µ / µ = [0.5, 0.75, 0.9]. Below we wll also dsuss experments n whh µ µ ) < 0.5. / µ - Proporton of demand due to arlne : et µ k µ k2) be the average demand for arlne 2), for demand lass k=,. Due to symmetry, we need only test senaros where µ k < µ k2. We use ratos µ k / µ k µ k 2) = [0., 0.25, 0.5]. - Varablty: o lmt the number of parameters, we assume that all four ustomer demand dstrbutons have the same oeffent of varaton, CV. We use values CV = [0.25, 0.5,,.5, 2]. Note that CV's hgher than rarely our n prate Jaobs, Ratlff and Smth [2000] desrbe 0.2 to 0.6 as a reasonable range for the CV). owever, we felt that there s some value n examnng envronments wth hghly varable demands. When we present the results below, we present both the aggregate results and the results for low CVs CV = 0.25 or 0.5). - Correlaton: Agan, to lmt the number of parameters, we assume that the orrelatons among all demands are equal. When four random varables are dstrbuted aordng to the multvarate normal dstrbuton, the lowest possble ommon orrelaton s /4 ) = 0.33; when the ommon orrelaton s lower than ths bound the ovarane matrx s not postve defnte ong, 980). For orrelaton, we use values ρ = [-0.3, 0.0, 0.5, 0.9]. When ombned, these parameters defne 4 *3*3*5*4 = 720senaros. 25
28 Before we examne aggregate statsts from the 720 senaros, let us fous on a sngle 'baselne' senaro. We hoose p / p = 2, CV = 0.5, and ρ = 0, set the mean low-fare demand to eah arlne at 50 passengers, and set the mean hgh-fare demand at 50 passengers so that µ / µ µ ) =0.75 and µ k / µ k µ k 2) = 0.5. Whle ertan parameter values nluded n the ranges above are unlkely to our n prate, ths senaro s relatvely plausble. Fgure 2 dsplays the reaton funtons of the arlnes, gven these parameter values. here s a unque equlbrum, resultng n B = B 44. herefore, the total bookng lmt s 2 = 288 and the arlnes reserve a total of 2 seats for hgh-fare ustomers. A monopolst, on the other hand, has an optmal total bookng lmt of B a B a 300 seats, wth 00 seats set asde 2 = for hgh-fare ustomers. If we defne the "serve level" as the probablty that a ustomer s able to purhase a seat on ether arraft, the dfferene n bookng lmts produes sgnfantly dfferent serve levels for eah ustomer lass. Under ompetton, 45% of low-fare ustomers found a seat on ether flght, whle under a monopoly the low-fare serve level rses to 50%. On the other hand, hgh-fare passengers beneft from ompetton. her serve level s 77% under ompetton, 70% under the monopoly. Whle ths partular example produed a unque equlbrum, n Seton 2 we saw that the full-overflow game may have multple equlbra or may not have any equlbra at all. Suh an outome would omplate the omparson between ompettve and monopoly bookng lmts. owever, by examnng the arlne response funtons for eah of the 720 senaros, we saw that n every ase an equlbrum exsts and was unque. All response funtons were ontnuous, and most produed a stable equlbrum, as n Fgure 2. As mentoned above, an extremely low negatve) orrelaton between hgh and low-fare demands an generate the outome shown n Fgure 4, n whh no pure-strategy equlbrum exsts. We have also found nstanes of multple equlbra when the rato µ µ ) s low e.g., 0.) and orrelaton / µ s negatve or zero. We wll dsuss these ases at the end of ths Seton. Frst we ompare the total bookng lmts n the ompettve and monopoly envronments for the orgnal 720 senaros. In every senaro, the bookng lmt for the monopoly s equal to, or greater than, the sum of the bookng lmts for the arlnes n ompetton. he mean a a dfferene B B ) B B ) aross all senaros s 5 seats, and the dfferene vares from
29 seats to 3 seats. When we examne only those senaros wth CV=0.25 or CV=0.5, the dfferenes are smaller. Under these senaros, the average dfferene s 9 seats wth a range from 0 to 03 seats. In general, the largest dfferenes our when orrelaton s low ρ = -0.3) and expeted demands are equally balaned among arlnes and lasses when µ µ ) =0.5 and / µ a a µ k / µ k µ k 2) =0.5). able 2 dsplays the dfferene B B2 ) B B2) for eah value of ρ. Eah olumn of able 2 represents an average over 80 senaros. As the orrelaton nreases, the dfferene between the monopoly and ompettve ases dereases. Avg. monopoly total bookng lmt Avg. ompettve total bookng lmt a a B B2 ρ = 0.3 ρ = 0.0 ρ = 0.5 ρ = B B a a Average B B ) B B ) ow-fare serve level monopoly-ompettve) 0.0% 4.%.3% 0.4% gh-fare serve level monopoly-ompettve) -0.0% -4.7% -.7% -0.4% able 2. Demand orrelaton and the effets of ompetton. he dfferenes n bookng lmts have a sgnfant effet on the serve levels offered to eah ustomer lass. Over all ases, the serve level offered to low-fare ustomers rose an average of 4% under the monopoly 39% to 43%), whle the serve level offered to hgh-fare ustomers delnes an average of 4% under the monopoly 75% to 7%). For senaros wth low CVs the average dfferenes were a bt smaller: 3.7% and 3.4%, respetvely. In addton, the range of results was extremely large. In fve senaros out of 720, monopoly low-fare serve levels were over 50% greater than the low-fare serve levels under ompetton. he dfferene n hgh-fare serve levels was as hgh as 3%. In general, the dfferene n total profts between the monopoly and ompettve ases was small. Averaged over all 720 senaros, profts to the monopoly are ust 0.3% hgher than the total profts under ompetton, wth a range from 0% to 5%. When restrted to senaros wth CV=0.25 or CV=0.5, the average dfferene n profts s 0.2% wth a range from 0% to 3.5%. he largest dfferenes n proft were seen when orrelaton s low, p / p s hgh, and expeted 27
30 demands are equally balaned among arlnes and lasses. hese small dfferenes n proft are not unexpeted sne n most ases the obetve funton s relatvely 'flat' near the optmum. It s more dffult to make these omparsons when the proporton of demand due to lowfare passengers s small µ µ ) < 0.5) beause senaros wth multple ompettve / µ equlbra begn to appear. For example, wth µ µ ) =0., we dentfed one senaro, / µ shown n Fgure 3, wth three equlbra: B = 6, B 36), B = 36, B 6), and 2 = 2 = 2 = B = 22, B 22). owever, under ths senaro the monopoly soluton s B a B a 93. As 2 = was true for the orgnal 720 senaros, at eah ompettve equlbrum the total bookng lmt s smaller than or equal to the bookng lmt hosen by a monopoly. hs was true for all examned senaros wth µ µ ) < 0.5. / µ 6. Observatons and Future Researh In ths paper we have examned how ompetton affets a fundamental deson n yeld management, the alloaton of seats among low and hgh-fare lasses. Besdes the tehnal results onernng the exstene and unqueness of ompettve equlbra and the analytal expressons for the frst-order ondtons, our prmary fndng s that the sum of the arlnes' bookng lmts under ompetton s no hgher than the total bookng lmt produed when total profts from both flghts are maxmzed as n a monopoly or when arlnes ooperate n settng bookng lmts). Under ompetton more hgh-fare tkets and fewer low-fare tkets may be sold than under a monopoly. hs s not an obvous result, for n many standard eonom models ompetton leads to a fall n pres e.g., a smple Bertrand model of pre ompetton). ere, we have held pres onstant, but ompetton leads to a realloaton of nventory among ustomer segments, produng a rse n the average pre pad for an arlne seat. Under the monopoly soluton, low-fare ustomers are more lkely to fnd a seat, and are more lkely to fnd a seat on a frst-hoe arlne, than under ompetton. Wth pres held onstant, a monopolst would mprove serve for the low-pre segment whle dmnshng serve for the hgh-pre segment. hs may be partularly nterestng n regulatory envronments n whh anttrust laws prohbt arlnes from olludng on pres but allow them to oordnate yeld management desons. 28
Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3
Royal Holloway Unversty of London Department of Physs Seres Solutons of ODEs the Frobenus method Introduton to the Methodology The smple seres expanson method works for dfferental equatons whose solutons
More informationFigure 1. Inventory Level vs. Time - EOQ Problem
IEOR 54 Sprng, 009 rof Leahman otes on Eonom Lot Shedulng and Eonom Rotaton Cyles he Eonom Order Quantty (EOQ) Consder an nventory tem n solaton wth demand rate, holdng ost h per unt per unt tme, and replenshment
More informationRecap. Duopoly models. ! Last class (January 13, 2004) ! Today (January 15, 2004) ! Two competing firms, selling a homogeneous good
Reap! Last lass (January 13, 24)! Domnant and domnated atons! Best response! ash eulbrum! Mxed strateges! Pareto domnane! Today (January 15, 24)! Examples of games wth ontnuous aton sets! Duopoly models:
More information24. Impact of Piracy on Innovation at Software Firms and Implications for Piracy Policy
4. mpat of Pray on nnovaton at Software Frms and mplatons for Pray Poly Jeevan Jasngh Department of nformaton & Systems Management, HKUST Clear Water Bay, Kowloon Hong Kong jeevan@ust.h Abstrat A Busness
More informationWhen can bundling help adoption of network technologies or services?
When an bundlng help adopton of network tehnologes or serves? Steven Weber Dept. of ECE, Drexel U. sweber@oe.drexel.edu Roh Guérn Dept. of CSE, WUSTL guern@wustl.edu Jaudele C. de Olvera Dept. of ECE,
More informationChapter 6. Demand Relationships Among Goods
Chapter 6 Demand Relatonshps Among Goods Up to ths pont, we have held the pre of other goods onstant. Now we onsder how hanges n p affet n a two-good world. I p I p I p I p p p ( ) ( ) then I p then (
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationEnergy-Efficient Design in Wireless OFDMA
Ths full text paper was peer revewed at the dreton of IEEE Communatons Soety subjet matter experts for publaton n the ICC 2008 proeedngs. Energy-Effent Desgn n Wreless OFDMA Guowang Mao, Nageen Hmayat,
More informationUse of Multi-attribute Utility Functions in Evaluating Security Systems
LLNL-TR-405048 Use of Mult-attrbute Utlty Funtons n Evaluatng Seurty Systems C. Meyers, A. Lamont, A. Sherman June 30, 2008 Ths doument was prepared as an aount of work sponsored by an ageny of the Unted
More informationPartner Choice and the Marital College Premium: Analyzing Marital Patterns Over Several Decades
Partner Choe and the Martal College Premum: Analyzng Martal Patterns Over Several Deades Perre-André Chappor Bernard Salané Yoram Wess January 31, 2015 Abstrat We onstrut a strutural model of household
More informationCONSIDER a connected network of n nodes that all wish
36 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 60, NO. 2, FEBRUARY 204 Coded Cooperatve Data Exhange n Multhop Networks Thomas A. Courtade, Member, IEEE, and Rhard D. Wesel, Senor Member, IEEE Abstrat
More informationOptimal Health Insurance for Multiple Goods and Time Periods
04 R.P. Ells, S. Jang, and W.G. Mannng Optmal Health Insurane for Multple Goods and Tme Perods Randall P. Ells a,, Sheny Jang b, Wllard G. Mannng a Department of Eonoms, Boston Unversty, 70 Bay State Road,
More informationMulti-settlement Systems for Electricity Markets: Zonal Aggregation under Network Uncertainty and Market Power 1
Proeedngs of the 35th Hawa Internatonal Conferene on System Senes - 2002 Mult-settlement Systems for Eletrty Markets: Zonal Aggregaton under Network Unertanty and Market Power 1 Ransh Kamat and Shmuel
More information1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationLognormal random eld approxmatons to LIBOR market models O. Kurbanmuradov K. Sabelfeld y J. Shoenmakers z Mathemats Subet Classaton: 60H10,65C05,90A09 Keywords: LIBOR nterest rate models, random eld smulaton,
More informationBehavior Coordination in E-commerce Supply Chains
Assoaton for Informaton ystems AI Eletron Lbrary AIeL) WHICEB 25 Proeedngs Wuhan Internatonal Conferene on e-busness ummer 6-9-25 Behavor Coordnaton n E-ommere upply Chans Yanhong Zhang Insttute of system
More informationData Analysis with Fuzzy Measure on Intuitionistic Fuzzy Sets
Proeedngs of the Internatonal MultConferene of Engneers and Computer Sentsts 2016 Vol II Marh 16-18 2016 Hong Kong Data nalyss wth Fuzzy Measure on Intutonst Fuzzy Sets Sanghyuk Lee * Ka Lok Man Eng Gee
More informationModern Problem Solving Techniques in Engineering with POLYMATH, Excel and MATLAB. Introduction
Modern Problem Solvng Tehnques n Engneerng wth POLYMATH, Exel and MATLAB. Introduton Engneers are fundamentally problem solvers, seekng to aheve some objetve or desgn among tehnal, soal eonom, regulatory
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationDECOMPOSITION ALGORITHM FOR OPTIMAL SECURITY-CONSTRAINED POWER SCHEDULING
DECOMPOSITION ALGORITHM FOR OPTIMAL SECURITY-CONSTRAINED POWER SCHEDULING Jorge Martínez-Crespo Julo Usaola José L. Fernández Unversdad Carlos III de Madrd Unversdad Carlos III de Madrd Red Elétra de Espana
More informationPricing System Security in Electricity Markets. latter might lead to high prices as a result of unrealistic
1 Pro. Bulk Power Systems Dynams and Control{V, Onomh, Japan, August 2001. Prng System Seurty n Eletrty Markets Claudo A. Ca~nzares Hong Chen Wllam Rosehart UnverstyofWaterloo Unversty of Calgary Dept.
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationProblem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative.
Queston roblem Set 3 a) We are asked how people wll react, f the nterest rate on bonds s negatve. When
More informationTrade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity
Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton
More informationTHE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationPERRON FROBENIUS THEOREM
PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationHow To Calculate The Accountng Perod Of Nequalty
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More informationNorth-South Trade-Related Technology Diffusion: Virtuous Growth Cycles in Latin America
DISCUSSION PAPER SERIES IZA DP No. 4943 North-South Trade-Related Tehnology Dffuson: Vrtuous Growth Cyles n Latn Amera Maure Shff Yanlng Wang May 2010 Forshungsnsttut zur Zukunft der Arbet Insttute for
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationSubstitution Effects in Supply Chains with Asymmetric Information Distribution and Upstream Competition
Substtuton Effects n Supply Chans wth Asymmetrc Informaton Dstrbuton and Upstream Competton Jochen Schlapp, Mortz Fleschmann Department of Busness, Unversty of Mannhem, 68163 Mannhem, Germany, jschlapp@bwl.un-mannhem.de,
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationCartelisation of Oligopoly
Internatonal Conferene on Apple Eonoms ICOAE 29 551 Cartelsaton of Olgopoly Jaek Prokop 224 Abstrat Ths paper onsers possbltes of artelsaton n the olgopolst nustres. We stngush between two mportant ssues.
More informationBERNSTEIN POLYNOMIALS
On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
More informationAnswer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationPeer-to-peer systems have attracted considerable attention
Reputaton Aggregaton n Peer-to-Peer etwork Usng Dfferental Gossp Algorthm Ruhr Gupta, Yatndra ath Sngh, Senor Member, IEEE, arxv:20.430v4 [s.i] 28 Jan 204 Abstrat Reputaton aggregaton n peer to peer networks
More informationWe are now ready to answer the question: What are the possible cardinalities for finite fields?
Chapter 3 Fnte felds We have seen, n the prevous chapters, some examples of fnte felds. For example, the resdue class rng Z/pZ (when p s a prme) forms a feld wth p elements whch may be dentfed wth the
More informationRobust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School
Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management
More informationSolution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.
Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces
More informationOptimal Adaptive Voice Smoother with Lagrangian Multiplier Method for VoIP Service
Optmal Adaptve Voe Smoother wth Lagrangan Multpler Method for VoIP Serve Shyh-Fang HUANG, Er Hsao-uang WU and Pao-Ch CHANG Dept of Eletral Engneerng, Computer Sene and Informaton Engneerng and Communaton
More informationEvaluation of Delay Performance in Valiant Load-balancing Network
Evaluaton of Delay Performane n Valant Load-balanng Network Yngd Yu a, Yaohu Jn a, Hong Cheng a, Yu Gao a, Weqang Sun a, We Guo a, Wesheng Hu a a State Key Laboratory on Fber-Ot Loal Area Networks and
More informationA STUDY OF SOFTBALL PLAYER SWING SPEED *
A STUDY OF SOFTBALL PLAYER SWING SPEED * LLOYD SMITH Shool of Mehanal and Materals Engneerng Washngton State Unversty E-mal: lvsmth@wsu.edu JEFF BROKER Department of Bology Unversty of Colorado, Colorado
More informationPSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12
14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationAddendum to: Importing Skill-Biased Technology
Addendum to: Importng Skll-Based Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our
More informationAwell-known result in the Bayesian inventory management literature is: If lost sales are not observed, the
MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Vol. 10, No. 2, Sprng 2008, pp. 236 256 ssn 1523-4614 essn 1526-5498 08 1002 0236 nforms do 10.1287/msom.1070.0165 2008 INFORMS Dynamc Inventory Management
More informationSection 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
More information+ + + - - This circuit than can be reduced to a planar circuit
MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to
More informationChapter 7: Answers to Questions and Problems
19. Based on the nformaton contaned n Table 7-3 of the text, the food and apparel ndustres are most compettve and therefore probably represent the best match for the expertse of these managers. Chapter
More informationwhere the coordinates are related to those in the old frame as follows.
Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product
More informationStaff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall
SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent
More informationMarginal Revenue-Based Capacity Management Models and Benchmark 1
Margnal Revenue-Based Capacty Management Models and Benchmark 1 Qwen Wang 2 Guanghua School of Management, Pekng Unversty Sherry Xaoyun Sun 3 Ctgroup ABSTRACT To effcently meet customer requrements, a
More informationPower-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts
Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More informationCausal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More informationSection 5.3 Annuities, Future Value, and Sinking Funds
Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme
More informationEconomy-based Content Replication for Peering Content Delivery Networks
Eonomy-based Content Replaton for Peerng Content Delvery Networs Al-Muaddm Khan Pathan 1 and Raumar Buyya Grd Computng and Dstrbuted Systems (GRIDS) Laboratory Department of Computer Sene and Software
More informationValue Driven Load Balancing
Value Drven Load Balancng Sherwn Doroud a, Esa Hyytä b,1, Mor Harchol-Balter c,2 a Tepper School of Busness, Carnege Mellon Unversty, 5000 Forbes Ave., Pttsburgh, PA 15213 b Department of Communcatons
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationForschung zur Entwicklungsökonomie und -politik Research in Development Economics and Policy
Dsusson Paper No. 01/2004 Comparatve advantage of Vetnam s re setor under dfferent lberalsaton senaros A Poly Analyss Matrx (PAM) study Nguyen Manh Ha and Franz Hedhues Department of Agrultural Development
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationThe literature on many-server approximations provides significant simplifications toward the optimal capacity
Publshed onlne ahead of prnt November 13, 2009 Copyrght: INFORMS holds copyrght to ths Artcles n Advance verson, whch s made avalable to nsttutonal subscrbers. The fle may not be posted on any other webste,
More informationThe Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
More informationCyber-Security Via Computing With Words
Cyber-Seurty Va Computng Wth Words John. Rkard Dstrbuted Infnty, In. 4637 Shoshone Drve Larkspur, CO 808 Emal: trkard@dstrbutednfnty.om ABSRAC Cyber-seurty systems must deal wth a hgh rate of observable
More informationHedging Interest-Rate Risk with Duration
FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton
More informationA DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña
Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION
More informationThis paper looks into the effects of information transparency on market participants in an online trading
Vol. 29, No. 6, November December 2010, pp. 1125 1137 ssn 0732-2399 essn 1526-548X 10 2906 1125 nforms do 10.1287/mksc.1100.0585 2010 INFORMS The Effects of Informaton Transparency on Supplers, Manufacturers,
More informationMulti-class kernel logistic regression: a fixed-size implementation
Mult-lass kernel logst regresson: a fxed-sze mplementaton Peter Karsmakers,2, Krstaan Pelkmans 2, Johan AK Suykens 2 Abstrat Ths researh studes a pratal teratve algorthm for mult-lass kernel logst regresson
More informationLecture 3: Force of Interest, Real Interest Rate, Annuity
Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and
More informationPrice Competition in an Oligopoly Market with Multiple IaaS Cloud Providers
Prce Competton n an Olgopoly Market wth Multple IaaS Cloud Provders Yuan Feng, Baochun L, Bo L Department of Computng, Hong Kong Polytechnc Unversty Department of Electrcal and Computer Engneerng, Unversty
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang
More informationIS-LM Model 1 C' dy = di
- odel Solow Assumptons - demand rrelevant n long run; assumes economy s operatng at potental GDP; concerned wth growth - Assumptons - supply s rrelevant n short run; assumes economy s operatng below potental
More informationProduction. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.
Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set
More informationHgh Dmensonal Data Analysis proposeations
Yuntao Qan, Xaoxu Du, and Q Wang Sem-supervsed Herarhal Clusterng Analyss for Hgh Dmensonal Data Sem-supervsed Herarhal Clusterng Analyss for Hgh Dmensonal Data Yuntao Qan, Xaoxu Du, and Q Wang College
More informationQuantization Effects in Digital Filters
Quantzaton Effects n Dgtal Flters Dstrbuton of Truncaton Errors In two's complement representaton an exact number would have nfntely many bts (n general). When we lmt the number of bts to some fnte value
More informationFisher Markets and Convex Programs
Fsher Markets and Convex Programs Nkhl R. Devanur 1 Introducton Convex programmng dualty s usually stated n ts most general form, wth convex objectve functons and convex constrants. (The book by Boyd and
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More informationIn some supply chains, materials are ordered periodically according to local information. This paper investigates
MANUFACTURING & SRVIC OPRATIONS MANAGMNT Vol. 12, No. 3, Summer 2010, pp. 430 448 ssn 1523-4614 essn 1526-5498 10 1203 0430 nforms do 10.1287/msom.1090.0277 2010 INFORMS Improvng Supply Chan Performance:
More informationL10: Linear discriminants analysis
L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss
More informationThe Economics of Two-sided Markets 2. Platform competition!
U. Porto Doctoral Programme n Economcs The Economcs of Two-sded Markets 2. Platform competton! Paul Belleflamme, CORE & LSM! Unversté catholque de Louvan! Aprl 10-13, 2012 Learnng objectves At the end
More informationApplications of the Offset in Property-Casualty Predictive Modeling
Applatons of the Offset n Property-Casualty Predtve Modelng Jun Yan, Ph.D. James Guszza, FCAS, MAAA, Ph.D. Matthew Flynn, Ph.D. Cheng-Sheng Peter Wu, FCAS, ASA, MAAA Abstrat: Generalzed Lnear Model [GLM]
More informationStaffing Call Centers with Uncertain Arrival Rates and Co-sourcing
Vol., No., xxxx xxxx 215,. 1 17 ISSN 159-1478 EISSN 1937-5956 15 1 OI 1.1111/oms.12332 214 Produton and Oeratons Management Soety Staffng Call Centers wth nertan Arrval Rates and Co-sourng Yasßar Levent
More informationThe Stock Market Game and the Kelly-Nash Equilibrium
The Stock Market Game and the Kelly-Nash Equlbrum Carlos Alós-Ferrer, Ana B. Ana Department of Economcs, Unversty of Venna. Hohenstaufengasse 9, A-1010 Venna, Austra. July 2003 Abstract We formulate the
More informationCautiousness and Measuring An Investor s Tendency to Buy Options
Cautousness and Measurng An Investor s Tendency to Buy Optons James Huang October 18, 2005 Abstract As s well known, Arrow-Pratt measure of rsk averson explans a ratonal nvestor s behavor n stock markets
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More information21 Vectors: The Cross Product & Torque
21 Vectors: The Cross Product & Torque Do not use our left hand when applng ether the rght-hand rule for the cross product of two vectors dscussed n ths chapter or the rght-hand rule for somethng curl
More informationEconomic Models for Cloud Service Markets
Economc Models for Cloud Servce Markets Ranjan Pal and Pan Hu 2 Unversty of Southern Calforna, USA, rpal@usc.edu 2 Deutsch Telekom Laboratores, Berln, Germany, pan.hu@telekom.de Abstract. Cloud computng
More information2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet
2008/8 An ntegrated model for warehouse and nventory plannng Géraldne Strack and Yves Pochet CORE Voe du Roman Pays 34 B-1348 Louvan-la-Neuve, Belgum. Tel (32 10) 47 43 04 Fax (32 10) 47 43 01 E-mal: corestat-lbrary@uclouvan.be
More informationTechnical Memorandum Number 815. Bigger Slice or Larger Pie? Optimal Marketing Strategies for New Firms. John Angelis Moren Lévesque
Techncal Memorandum Number 815 Bgger Slce or Larger Pe? Optmal Marketng Strateges for New Frms by John Angels Moren Lévesque June 26 Department of Operatons Weatherhead School of Management Case Western
More informationFaraday's Law of Induction
Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy
More informationIntra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error
Intra-year Cash Flow Patterns: A Smple Soluton for an Unnecessary Apprasal Error By C. Donald Wggns (Professor of Accountng and Fnance, the Unversty of North Florda), B. Perry Woodsde (Assocate Professor
More informationHigh Correlation between Net Promoter Score and the Development of Consumers' Willingness to Pay (Empirical Evidence from European Mobile Markets)
Hgh Correlaton between et Promoter Score and the Development of Consumers' Wllngness to Pay (Emprcal Evdence from European Moble Marets Ths paper shows that the correlaton between the et Promoter Score
More informationA Lyapunov Optimization Approach to Repeated Stochastic Games
PROC. ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING, OCT. 2013 1 A Lyapunov Optmzaton Approach to Repeated Stochastc Games Mchael J. Neely Unversty of Southern Calforna http://www-bcf.usc.edu/
More information