optimal dynamic pricing for perishable assets with nonhomogeneous demand we consider a dynamic pricing model for selling a given stock of a perishable product over a finite time horizon customers whose reservation price distribution changes over time arrive according to a nonhomogeneous poisson process we show that at any given time the optimal price decreases with inventory we also identify a sufficient condition under which the optimal price decreases over time for a given inventory level this sufficient condition requires that the willingness of a customer to pay a premium for the product does not increase over time in addition to shedding managerial insight these structural properties enable efficient computation of the optimal policy numerical studies are conducted to show the revenue impact of dynamic price policies price changes are set to compensate for statistical fluctuations of demand and to respond to shifts of the reservation price for the former our examples show that using optimal dynamic optimal policies achieves 2 4 7 3 revenue improvement over the optimal single price policy for the latter the revenue increase can be as high as 100 these results explain why yield management has become so essential to fashion retailing and travel service industries

the hotel yield management with two types of room prices overbooking and cancellations abstract we consider a problem for hotel room allocations with early discount cancellations and overbooking in our model customers can book a room in discounted price if they reserve before a certain deadline the model takes overbooking into account we have expressed the expected total sale function proved it is unimodal on the number of allocated rooms for early discount and for overbooking respectively under a condition and clarified the range where the optimal solution exists we have derived an optimal allocation for early discount in a specified model without cancellations and overbooking

optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices abstract consider a continuous time inventory problem in which a retailer sets the price on a fixed number of a perishable asset that must be sold prior to the time at which it perishes the retailer can dynamically adjust the price between any of a finite number of allowable prices demand for the product is poisson with an intensity that is inversely related to the price the optimal policy is piecewise constant the maximum expected revenue is nondecreasing and concave in both the remaining inventory and the time to go for a given inventory level the optimal price declines as the time at which the products perish approaches at any given time the optimal price is nonincreasing in the number of items remaining unsold these results are extended to i the case in which the prices and corresponding demand intensities depend on the time to go and ii the case in which the retailer can restock to meet demand at a unit cost after the initial inventory has been sold

pricing and capacity rationing for rentals with uncertain durations we consider a rental firm with two types of customers contract customers pay fixed prenegotiated rental fees and expect a high quality of service walk in customers have no contractual relations with the firm and are shopping for price given multiple contract and walk in classes the rental firm has to decide when to offer service to contract customers and what fees to charge walk in customers for service we formulate this rental management problem as a problem in stochastic control and characterize optimal policies for managing contract and walk in customers we also consider static myopic controls that are simpler to implement and we analytically establish conditions under which these policies perform optimally complementary numerical tests provide a sense of the range of systems for which myopic policies are effective

real time dynamic pricing with minimal and flexible price adjustment we study a standard dynamic pricing problem where the seller a monopolist possesses a finite amount of inventories and attempts to sell the products during a finite selling season despite the potential benefits of dynamic pricing many sellers still adopt a static pricing policy because of 1 the complexity of frequent reoptimizations 2 the negative perception of excessive price adjustments and 3 the lack of flexibility caused by existing business constraints in this paper we develop a family of pricing heuristics that can be used to address all these challenges our heuristic is computationally easy to implement it requires only a single optimization at the beginning of the selling season and automatically adjusts the prices over time moreover to guarantee a strong revenue performance the heuristic only needs to adjust the prices of a small number of products and do so infrequently this property helps the seller focus his effort on the prices of the most important products instead of all products in addition in the case where not all products are equally admissible to price adjustment due to existing business constraints such as contractual agreement strategic product positioning etc our heuristic can immediately substitute the price adjustment of the original products with the price adjustment of similar products and maintain an equivalent revenue performance this property provides the seller with extra flexibility in managing his prices

analysis of deterministic lp based booking limit and bid price controls for revenue management we study the performance of two popular and widely used heuristics for revenue management known as the booking limit and bid price controls in contrast to a recent result in the literature where frequent re solvings of a certain heuristic are shown to significantly reduce revenue loss we show that the asymptotic revenue loss of either booking limit or bid price control cannot be reduced regardless of the choice of re solving times and the frequency of re solving moreover we also show that further variations within the policy classes such as nested instead of partition booking limit or certainty equivalent instead of additive bid price are simply indistinguishable in terms of their order of revenue loss under frequent re solvings this negative result highlights the limitation of re solving deterministic linear programs when the solution is interpreted as either a booking limit or a bid price finally we briefly discuss how to modify the traditional booking limit control to make it more responsive

the induced innovation hypothesis and energy saving technological change we develop a methodology for testing hicks s induced innovation hypothesis by estimating a product characteristics model of energy using consumer durables augmenting the hypothesis to allow for the ine uence of government regulations for the products we explored the evidence suggests that i the rate of overall innovation was independent of energy prices and regulations ii the direction of innovation was responsive to energy price changes for some products but not for others iii energy price changes induced changes in the subset of technically feasible models that were offered for sale iv this responsiveness increased substantially during the period after energy efficiency product labeling was required and v nonetheless a sizable portion of efficiency improvements were autonomous

dynamic capacity management with substitution we examine a multiperiod capacity allocation model with upgrading there are multiple product types corresponding to multiple classes of demand and the firm purchases capacity of each product before the first period within each period after demand arrives products are allocated to customers customers who arrive to find that their product has been depleted can be upgraded by at most one level we show that the optimal allocation policy is a simple two step algorithm first use any available capacity to satisfy same class demand and then upgrade customers until capacity reaches a protection limit so that in the second step the higher level capacity is rationed we show that these results hold both when all capacity is salvaged at the end of the last demand period as well as when capacity can be replenished in the latter case an order up to policy is optimal for replenishment although finding the optimal protection limits is computationally intensive we describe bounds for the optimal protection limits that take little effort to compute and can be used to effectively solve large problems using these heuristics we examine numerically the relative value of strictly optimal capacity and dynamic rationing the value of perfect demand information and the impact of demand and economic parameters on the value of optimal substitution

permanent and transitory oil volatility and aggregate investment in malaysia abstract this paper investigates the relation between aggregate investment and oil volatility and its permanent and transitory components for a developing country malaysia in the paper the components generalized autoregressive conditional heteroskedasticity cgarch model is utilized to decompose conditional oil volatility into permanent oil volatility and transitory oil volatility respectively reflecting fundamental driven and random shifts in oil volatility they are expected to exert differential effects on aggregate investment adopting a vector autoregression var framework to allow feedback effects between aggregate investment and its determinants the paper documents evidence supporting the adverse effects of conditional oil volatility permanent oil volatility and transitory oil volatility on aggregate investment and real output interestingly contrary to the findings for the developed markets us and oecd the real effects of permanent oil volatility tend to be stronger these findings are reasonably robust to variable specification and measurements in the var system hence there is an indication that heightened oil volatility accounts for the slumps in malaysia s aggregate investment after the asian financial crisis

performance of an lp based control for revenue management with unknown demand parameters we consider a standard network revenue management rm problem and study the performance of a linear program lp based control the probabilistic allocation control pac in the presence of unknown demand parameters we show that frequent re optimizations of pac without re estimation suffice to shrink the asymptotic impact of estimation error on revenue loss if in addition to re optimizations we also frequently re estimate the parameters we prove that the performance of pac in the unknown parameters setting is almost as good as the performance of pac in the known parameters setting our numerical experiments show that pac yields a revenue improvement of order 0 5 1 5 relative to lp based booking limit and bid price in most cases given the small margin in rm industries such as the airline industry about 2 this level of improvement can easily translate into a significant increase in profit

optimal dynamic auctions for revenue management we analyze a dynamic auction in which a seller withc units to sell faces a sequence of buyers separated intot time periods each group of buyers has independent private values for a single unit buyers compete directly against each other within a period as in a traditional auction and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller the number of buyers in each period as well as the individual buyers valuations are random the model is a variation of the traditional single leg multiperiod revenue management problem in which consumers act strategically and bid for units of a fixed capacity over time for this setting we prove that dynamic variants of the first price and second price auction mechanisms maximize the seller s expected revenue we also show explicitly how to compute and implement these optimal auctions the optimal auctions are then compared to a traditional revenue management mechanism in which list prices are used in each period together with capacity controls and to a simple auction heuristic that consists of allocating units to each period and running a sequence of standard multiunit auctions with fixed reserve prices in each period the traditional revenue management mechanism is proven to be optimal in the limiting cases when there is at most one buyer per period when capacity is not constraining and asymptotically when the number of buyers and the capacity increases the optimal auction significantly outperforms both suboptimal mechanisms when there are a moderate number of periods capacity is constrained and the total volume of sales is not too large the benefit also increases when variability in the dispersion in buyers valuations or in the number of buyers per period increases

a new bid price approach to dynamic resource allocation in network revenue management abstract firms selling perishable products use a variety of techniques to maximize revenue through the dynamic control of their inventories one of the most powerful and simple approaches to address this issue consists of assigning threshold values bid prices to each resource and to accept requests whenever their revenue exceeds the sum of the bid prices associated with its constituent resources in this context we propose a new customer choice based mathematical program to estimate time dependent bid prices in contrast with most approaches from the current literature ours is characterized by its flexibility indeed it can easily embed technical and practical constraints that occur in most central reservation systems crs to solve the model we develop a column generation algorithm in which the np hard subproblem is addressed via an efficient heuristic procedure our computational results illustrate the performance of the method through comparisons with alternative proposals

multiperiod airline overbooking with a single fare class consider a multiperiod airline overbooking problem that relates to a single leg flight and a single service class passengers may cancel their reservations at any time including being no shows at flight time at that time the airline bumps passengers in excess of flight capacity and pays a penalty for so doing we give conditions on the fares refunds and distributions of passenger demand for reservations and cancellations in each period and on the bumping penalty function that ensure that a booking limit policy is optimal i e in each period the airline accepts reservation requests up to a booking limit if the number of initial reservations is less than that booking limit and declines reservation requests otherwise the optimal booking limits are easily computed we give conditions under which the optimal booking limits are monotone in the time to flight departure the model is applied to the discount allocation problem in which lower fare classes book prior to higher fare classes

revenue management for operations with urgent orders this article is motivated by the case of a company manufacturing industrial equipment that faces two types of demand on the one hand there are the so called regular orders for installations or refurbishing of existing facilities these orders have a relatively long lead time on the other hand there are urgent orders mostly related to spare parts when a facility has a breakdown the delay in such case is much shorter but higher margins can be obtained we study the order acceptance problem for a firm that serves two classes of demand over an infinite horizon the firm has to decide whether to accept a regular order or equivalently how much capacity to set aside for urgent orders in order to maximize its profit we formulate this problem as a multi dimensional markovian decision process mdp we propose a family of approximate formulations to reduce the dimension of the state space via aggregation we show how our approach can be used to compute bounds on the profit associated with the optimal order acceptance policy finally we show that the value of revenue management is commensurate with the operational flexibility of the firm

market incentives and pharmaceutical innovation i study the impact of the orphan drug act oda which established tax incentives for rare disease drug development i examine the flow of new clinical drug trials for a large set of rare diseases among more prevalent rare diseases the oda led to a significant and sustained increase in new trials the impact for less prevalent rare diseases was limited to an increase in the stock of drugs tax credits can stimulate rd yet because they leave revenue margins unaffected tax credits appear to have a more limited impact on private innovation in markets with smaller revenue potential

the other ex ante moral hazard in health it is well known that pooled insurance coverage can induce people to make inefficiently low investments in self protective activities we identify another ex ante moral hazard that runs in the opposite direction lower levels of self protection and the associated chronic conditions and behavioral patterns such as obesity smoking and malnutrition increase the incidence of many diseases and consumption of treatments to those diseases this increases the reward for innovation and thus benefits the innovator it also increases treatment innovation which benefits all consumers as individuals do not take these positive externalities into account their investments in self protection are inefficiently high we quantify the lower bound of this externality for obesity the lower bound is independent of how much additional innovation is generated the results show that the externality we identify offsets the negative medicare induced insurance externality of obesity the medicare induced obesity subsidy is thus not a sufficient rationale for soda taxes fat taxes or other penalties on obesity the quantitative finding also implies that the other ex ante moral hazard that we identify can be as important as the ex ante moral hazard that has been a central concept in health economics for decades

static and dynamic effects of health policy evidence from the vaccine industry public policies designed to increase utilization of existing technologies may also affect incentives to develop new technologies this paper investigates this phenomenon by examining policies designed to increase usage of preexisting vaccines i find that these policies were associated with a 2 5 fold increase in clinical trials for new vaccines for several diseases the induced innovation is socially wasteful though small in magnitude in one case however the dynamic social welfare benefits from induced innovation exceed the policies static benefits from increasing vaccination with existing technology these findings underscore the importance of including technological progress in economic analysis of public policy

revenue management by sequential screening abstract using a mechanism design approach we consider a firm s optimal pricing policy when consumers are heterogeneous and learn their valuations at different times we show that by offering a menu of advance purchase contracts that differ in when and for how much the product can be returned a firm can more easily price discriminate between privately informed consumers in particular we show that screening on when the return option can be exercised increases firm profits relative to screening on the size of the refund alone only if the expected gains from trade are higher for consumers who learn later we show that in some settings mean preserving spread the firm can achieve the complete information profits and analyze the optimal contract in other settings first order stochastic dominance in which the first best allocation is not always feasible

dynamic pricing through discounts for optimizing multiple class demand fulfillment in a multiple customer class model of demand fulfillment for a single item we consider the use of dynamic price discounts to encourage backlogging of demand for customer classes denied immediate service customers are assumed to arrive over several stages in a period and customer classes are distinguished by their contractual price and sensitivity to discounts through dynamic programming we determine the optimal discounts to offer assuming a linear model for the sensitivity of customers to such inducements we show that customers are served in class order and allocation of inventory to demand is determined by considering the current number of customers backlogged as well as the current inventory position through comparison to a naive supplier allocating inventory first come first served with no discounting we show that profits are primarily influenced by the allocation of capacity and the use of price discounts primarily benefits the second class customers overall fill rate heuristics for implementation of the solution in real time settings are given

robustness of capacity rationing policies a recent paper discusses a capacity rationing policy that allows make to order manufacturing firms encountering expected total demand in excess of available capacity to discriminate between two classes of products one yielding a higher profit contribution per unit of capacity allocated to it than the other the result is a selective rejection of orders for the class with lower unit contribution yielding an increase in total profit when compared to a base case that implements no capacity rationing implementation of the policy requires forecasts of demand parameters for both product classes in this paper we test the sensitivity of the capacity rationing policy to forecast errors in these parameters the results indicate that on average the rationing policy is quite robust in improving profit even when actual demands are approximately twenty percent different from forecast values

revenue management with limited demand information in this paper we consider the classical multifare single resource leg problem in revenue management for the case where demand information is limited our approach employs a competitive analysis which guarantees a certain performance level under all possible demand scenarios the only information required about the demand for each fare class is lower and upper bounds we consider both competitive ratio and absolute regret performance criteria for both performance criteria we derive the best possible static policies which employ booking limits that remain constant throughout the booking horizon the optimal policies have the form of nested booking limits dynamic policies which employ booking limits that may be adjusted at any time based on the history of bookings are also obtained we provide extensive computational experiments and compare our methods to existing ones the results of the experiments demonstrate the effectiveness of these new robust methods

perishable asset revenue management with markovian time dependent demand intensities many industries face the problem of selling a fixed stock of items over a finite horizon these industries include airlines selling seats before planes depart hotels renting rooms before midnight theaters selling seats before curtain time and retailers selling seasonal items with long procurement lead times given a sunk investment in seats rooms or winter coats the objective for these industries is to maximize revenues in excess of salvage value when demand is price sensitive and stochastic pricing is an effective tool to maximize expected revenues in this paper we address the problem of deciding the optimal timing of price changes within a given menu of allowable possibly time dependent price paths each of which is associated with a general poisson process with markovian time dependent predictable intensities we show that a set of variational inequalities characterize the value functions and the optimal possibly random time changes in addition we develop an efficient algorithm to compute the optimal value functions and the optimal pricing policy

pricing and revenue management the value of coordination the integration of systems for pricing and revenue management must trade off potential revenue gains against significant practical and technical challenges this dilemma motivates us to investigate the value of coordinating decisions on prices and capacity allocation in a stylized setting we propose two pairs of sequential policies for making static decisions on pricing and revenue management that differ in their degree of integration hierarchical versus coordinated and their pricing inputs deterministic versus stochastic for a large class of stochastic price dependent demand models we prove that these four heuristics admit tractable solutions satisfying intuitive sensitivity properties we further evaluate numerically the performance of these policies relative to a fully coordinated model which is generally intractable we find it interesting that near optimal performance is usually achieved by a simple hierarchical policy that sets prices first based on a nonnested stochastic model and then uses these prices to optimize nested capacity allocation this tractable policy largely outperforms its counterpart based on a deterministic pricing model jointly optimizing price and allocation decisions for the high end segment improves performance but the largest revenue benefits stem from adjusting prices to account for demand risk this paper was accepted by yossi aviv operations management

capacity rationing decision procedures with order profit as a continuous random variable abstract when expected demand is more than available capacity a make to order manufacturer should take the more profitable orders and reject the less profitable orders in order to better allocate its limited capacity and maximize its profit however various orders will arrive at different times in the future so the manufacturer cannot look at all the order inquiries at the same time and choose the most profitable ones when an order inquiry arrives the manufacturer must promptly determine without knowing exactly what the future orders might be whether to accept the order or to reject the order thus reserving the capacity for future more profitable orders this study focuses on such an order selection decision problem under the following assumptions 1 the arrival of future customer orders is a poisson process 2 the capacity requirement of a future customer order is a continuous random variable and 3 the profit per unit capacity used by an order is also a continuous random variable under the stochastic problem assumptions this study investigates two decision procedures that determine whether to accept or to reject an order inquiry based on the available information including 1 current available manufacturing capacity 2 capacity requirement by the order and 3 the profit per unit capacity of the order one procedure is called static probability capacity rationing decision procedure spcr the other one is called dynamic stochastic capacity rationing decision procedure dscr simulation experiments are conducted to validate the proposed procedures an optimal solution method that uses 0 1 knapsack problem formulation to obtain the decisions with perfect information is used to measure the effectiveness of the proposed procedures the results show that the approach utilizing dscr outperforms other approaches by providing the highest profit and is also very robust under various problem conditions

health insurance and the demand for medical care evidence from a randomized experiment we estimate how cost sharing the portion of the bill the patient pays affects the demand for medical services the data come from a randomized experiment a catastrophic insurance plan reduces expenditures 31 percent relative to zero out of pocket price the price elasticity is approximately 0 2 we reject the hypothesis that less favorable coverage of outpatient services increases total expenditure for example by deterring preventive care or inducing hospitalization

design and evaluation of a dynamic capacity apportionment procedure abstract deploying a decision theory based approach we develop a heuristic for short term constrained capacity allocation to multiple product classes in make to order manufacturing attempting to maximize profit by discriminating between product classes efficacy of the heuristic is evaluated by comparing its performance to a base case and an upper bound under a wide variety of operational conditions such as variations in profit attractiveness capacity tightness customer order size structure customer order rate structure and demand coefficient of variation considering three different scenarios of time variant order rate of products the results indicate that the heuristic is effective in obtaining increased profit when capacity is very tight and products are well differentiated on average it yields an increase of 9 38 in total profit with a concomitant decrease of 0 85 in capacity utilization the results of this study serve to highlight the value of proactively seeking to differentiate products particularly when demand exceeds capacity

revenue management under a general discrete choice model of consumer behavior customer choice behavior such as buy up and buy down is an important phenomenon in a wide range of revenue management contexts yet most revenue management methodologies ignore this phenomenon or at best approximate it in a heuristic way in this paper we provide an exact and quite general analysis of this problem specifically we analyze a single leg reserve management problem in which the buyers choice behavior is modeled explicitly the choice model is very general simply specifying the probability of purchase for each fare product as a function of the set of fare products offered the control problem is to decide which subset of fare products to offer at each point in time we show that the optimal policy for this problem has a quite simple form namely it consists of identifying an ordered family of efficient subsets s 1 s m and at each point in time opening one of these sets s k where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time that is the more capacity or less time available the further the optimal set is along this sequence we also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested that is s 1 s 2 s m moreover we give a characterization of when nesting by fare order is optimal we also develop an estimation procedure for this setting based on the expectation maximization em method that jointly estimates arrival rates and choice model parameters when no purchase outcomes are unobservable numerical results are given to illustrate both the model and estimation procedure

an analysis of bid price controls for network revenue management bid prices are becoming an increasingly popular method for controlling the sale of inventoryin revenue management applications in this form of control threshold or bid prices are set for the resources or units of inventory seats on flight legs hotel rooms on specific dates etc and a product a seat in a fare class on an itinerary or room for a sequence of dates is sold only if the offered fare exceeds the sum of the threshold prices of all the resources needed to supply the product this approach is appealing on intuitive and practical grounds but the theory underlying it is not well developed moreover the extent to which bid price controls represent optimal or near optimal policies is not well understood using a general model of the demand process we show that bid price control is not optimal in general and analyze why bid price schemes can fail to produce correct accept deny decisions however we prove that when leg capacities and sales volumes are large bid price controls are asymptotically optimal provided the right bid prices are used we also provide analytical upper bounds on the optimal revenue in addition we analyze properties of the asymptotically optimal bid prices for example we show they are constant over time even when demand is nonstationary and that they may not be unique

mathematical programming for network revenue management revisited mathematical programming models for airline seat inventory control provide booking limits and bid prices for all itineraries and fare classes e l williamson airline network seat inventory control methodologies and revenue impacts ph d thesis massachusetts institute of technology cambridge ma 1992 finds that simple deterministic approximation methods based on average demand often outperform more advanced probabilistic heuristics we argue that this phenomenon is due to a booking process that includes nesting of the fare classes which is ignored in the modeling phase the differences in the performance between these approximations are studied using a stochastic programming model that includes the deterministic model as a special case our study carefully examines the trade off between computation time and the aggregation level of demand uncertainty with examples of a multi leg flight and a single hub network

induced innovation and energy prices this paper uses u s patent data from 1970 to 1994 to study the impact of energy prices on energy efficient innovations theories of technological change have focused both on demand side factors which spur innovative activity by increasing the value of new innovations and supply side factors such as scientific advancements that make new innovations possible data on demand side factors are easily obtained but data on supply side factors that influence innovation are not readily available this paper uses patent citations as a measure of the usefulness of the existing base of scientific knowledge citations to previous patents are used to construct productivity estimates which measure the usefulness of the existing stock of knowledge to inventors in a given energy field for any given year these estimates are then combined with data on demand side factors to estimate a model of induced innovation in energy technologies the results indicate that both energy prices and the supply of knowledge have strongly significant positive effects on innovation the paper concludes with a discussion of the implication of this work for environmental policy

reoptimization and self adjusting price control for network revenue management we consider a standard dynamic pricing problem with finite inventories finite selling horizon and stochastic demands where the objective of the seller is to maximize total expected revenue we introduce a simple improvement of the popular static price control known in the literature the proposed heuristic only requires a single optimization at the beginning of the selling horizon and does not require any reoptimization at all this provides an advantage over the potentially heavy computational burden of reoptimization especially for very large applications with frequent price adjustments in addition our heuristic can be implemented in combination with a few reoptimizations to achieve a high level revenue performance this hybrid of real time adjustment and reoptimization allows the seller to enjoy the benefit of reoptimization without overdoing it

price discrimination on booking time abstract even if consumers are forward looking and free to choose when to purchase a firm can price discriminate on booking time if consumers learn their valuations at different times and consumers who learn later have higher valuations the model is related to our work on optimal screening with returns contracts akan ata and dana 1 but here we consider a simpler binary valuation distribution and consider more realistic consumer learning assumptions the main contribution is to show that the profitability of screening on time is robust to relaxing the assumption that consumers learn instantaneously in addition to analyzing a bad news model in which information arrives gradually we characterize a general bound on consumer optimism that guarantees that the instantaneous learning results are robust

managing capacity in tightly constrained systems abstract many make to order firms face total expected demand in excess of available capacity this is particularly true in the short range for capital intensive firms such as those in the flow process industry firms making short life cycle products such as those in high fashion apparel industry and printing shops in such situations the manager is left with the difficult task of allocating the available capacity between competing classes of products and or customers at the same time the manager is faced with the challenge of achieving delivery dependability and speed in order to stay competitive in this paper i describe the problem contrast it with a class of problems known as perishable asset revenue management problems review recent literature that presents alternative approaches for managing capacity such as capacity rationing improved coordination mechanisms and subcontracting and discuss fruitful avenues for future research

revenue management without forecasting or optimization an adaptive algorithm for determining airline seat protection levels we investigate a simple adaptive approach to optimizing seat protection levels in airline revenue management systems the approach uses only historical observations of the relative frequencies of certain seat filling events to guide direct adjustments of the seat protection levels in accordance with the optimality conditions of brumelle and mcgill 1993 stochastic approximation theory is used to prove the convergence of this adaptive algorithm to the optimal protection levels in a simulation study we compare the revenue performance of this adaptive approach to a more traditional method that combines a censored forecasting method with a common seat allocation heuristic emsr b

a taxonomy and research overview of perishable asset revenue management yield management overbooking and pricing this paper proposes the term perishable asset revenue management to denote the field that combines the areas of yield management overbooking and pricing for perishable assets after summarizing the characteristics common to problems in this field the paper discusses the objectives and constraints faced by decision makers then it offers a comprehensive taxonomy with 14 different elements and reviews the research that has been done related to each element finally it suggests some important areas of future research that can help bridge the gap between theory and application

health insurance as a two part pricing contract monopolies appear throughout health care we show that health insurance operates like a conventional two part pricing contract that allows monopolists to extract profits without inefficiently constraining quantity when insurers are free to offer a range of insurance contracts to different consumer types health insurance markets perfectly eliminate deadweight losses from upstream health care monopolies frictions limiting the sorting of different consumer types into different insurance contracts restore some of these upstream monopoly losses which manifest as higher rates of uninsurance rather than as restrictions in quantity utilized by insured consumers empirical analysis of pharmaceutical patent expiration supports the prediction that heavily insured markets experience little or no efficiency loss under monopoly while less insured markets exhibit behavior more consistent with the standard theory of monopoly

airline seat allocation with multiple nested fare classes this paper addresses the problem of determining optimal booking policies for multiple fare classes that share the same seating pool on one leg of an airline flight when seats are booked in a nested fashion and when lower fare classes book before higher ones we show that a fixed limit booking policy that maximizes expected revenue can be characterized by a simple set of conditions on the subdifferential of the expected revenue function these conditions are appropriate for either the discrete or continuous demand cases these conditions are further simplified to a set of conditions that relate the probability distributions of demand for the various fare classes to their respective fares the latter conditions are guaranteed to have a solution when the joint probability distribution of demand is continuous characterization of the problem as a series of monotone optimal stopping problems proves optimality of the fixed limit policy over all admissible policies a comparison is made of the optimal solutions

a multiproduct dynamic pricing problem and its applications to network yield management a firm has inventories of a set of components that are used to produce a set of products there is a finite horizon over which the firm can sell its products demand for each product is a stochastic point process with an intensity that is a function of the vector of prices for the products and the time at which these prices are offered the problem is to price the finished products so as to maximize total expected revenue over the finite sales horizon an upper bound on the optimal expected revenue is established by analyzing a deterministic version of the problem the solution to the deterministic problem suggests two heuristics for the stochastic problem that are shown to be asymptotically optimal as the expected sales volume tends to infinity several applications of the model to network yield management are given numerical examples illustrate both the range of problems that can be modeled under this framework and the effectiveness of the proposed heuristics the results provide several fundamental insights into the performance of yield management systems

unanticipated monetary growth output and the price level u k 1946 1977 abstract this paper develops and estimates using annual data from 1946 1977 a three equation model of the u k economy in which output is affected only by unanticipated monetary growth whereas the price level is influenced by both anticipated and unanticipated changes in money supply expectations of monetary growth are assumed to be muth rational the model was estimated using efficient procedures and tests of the over identifying restrictions were generally favourable to model specification some features of the price equation are unsatisfactory and the results in this section must be considered tentative

discrete order admission atp model with joint effect of margin and order size in a mto environment abstract swarming demands and seasonality periodically induce a stringent capacity problem in the made to order mto b2b environment desirable order admission is a tactic with minimal application cost for handling this problem a real time order admission problem with limited major customer population and batch size demand with heterogeneous distributions is modeled as a dynamic and stochastic knapsack problem the optimality of the markovian deterministic reward threshold policy is verified optimal policy basic policy and the capacity rationing method are compared in the designed experiment the numerical results suggest great potential for increasing profit using the optimal order admission policy for mto businesses

structural properties of markov modulated revenue management problems abstract the admission decision is one of the fundamental categories of demand management decisions in the dynamic model of the single resource capacity control problem the distribution of demand does not explicitly depend on external conditions however in reality demand may depend on the current external environment which represents the prevailing economic financial social or other factors that affect customer behavior we formulate a markov decision process mdp to maximize expected revenues over a finite horizon that explicitly models the current environment we derive some structural results of the optimal admission policy including the existence of an environment dependent thresholds and a comparison of threshold levels in different environments we also present some computational results which illustrate these structural properties finally we extend some of the results to a related dynamic pricing formulation

pricing non storable perishable goods by using a purchase restriction with an application to airline fare pricing abstract the aim of this paper is to address the pricing problem of non storable perishable goods or services such as airline seats and hotel rooms by incorporating the use of purchase restrictions such as the advance booking requirement that is commonly used by airlines and hotels we will show that there exists an optimal pricing policy of at most three prices which can be obtained by solving a series of linear programming problems we will demonstrate that when applied to airline fare pricing the monotonicity condition of the model is equivalent to the requirement that business travelers are less price elastic than leisure travelers we will further illustrate the applicability of the model through a numerical example in the context of airline fare pricing by incorporating the impact of various fences

comment upon a new classical model of the postwar uk the results presented by attfield demery and duck 1981 in the european economic review 18 p 367 385 are replicated the model is re estimated to take account of the policy switch to floating exchange rates in 1972 with rather less than favourable results a major problem being misspecification of the monetary growth equation