Managing Hotel Reservations with Uncertain Arrivals

Bitran, Gabriel R.; Gilbert, Stephen M.

doi:10.1287/opre.44.1.35

Cited by 124 publications

(60 citation statements)

References 9 publications

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“…The analysis by Brumelle and McGill (1993) characterizes the optimal rationing policy for an airline seat allocation problem in which a fixed seat capacity must satisfy demand for multiple fare classes. The following papers generalize these results by incorporating cancellations and/or overbooking: Bitran andGilbert (1996), Subramanian et. al.…”

Section: Related Literaturementioning

confidence: 82%

Dynamic Capacity Management with Substitution

Shumsky

Zhang

2009

Operations Research

120

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We examine a multiperiod capacity allocation model with upgrading. There are multiple product types, corresponding to multiple classes of demand, and the firm purchases inventory of each product before the first period and thereafter replenishment is not possible. 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 solution is a simple two-step algorithm: first use any available inventory to satisfy same-class demand and then upgrade customers until inventory reaches a protection limit. We describe how to find optimal protection limits by backward induction and show that the protection limits are monotonic in current inventory and in time. The monotonicity results lead to simple bounds for the optimal protection limits, and we demonstrate that heuristic protection limits based on the bounds are effective in solving large problems.For a model with two time-periods and two products (dedicated and flexible), we compare the optimal initial capacity under our dynamic model with the optimal capacity under a singleperiod 'static' model. For a given capacity level, the marginal value of the dedicated capacity is always greater under the dynamic model than under the static model. Numerical experiments show that the optimal level of dedicated capacity is greater under the dynamic model than under that static model, although the optimal flexible capacity can be smaller, or greater, under the dynamic model.

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Section: Related Literaturementioning

confidence: 82%

Dynamic Capacity Management with Substitution

Shumsky

Zhang

2009

Operations Research

120

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“…In this environment, the key decision variables are normally the prices charged to each demand class as well as the possible rationing levels (i.e., booking limits) to impose. Some examples include Belobaba (1989) who examines booking limits for airline seats with different price classes, and Bitran and Gilbert (1996) who develop heuristic rationing procedures for managing hotel reservations. In these problems, the capacity or inventory level is fixed so the decision of how much inventory to order and when to replenish are not relevant.…”

Section: Literature Reviewmentioning

confidence: 99%

A Threshold Inventory Rationing Policy for Service-Differentiated Demand Classes

Deshpande¹,

Cohen

Donohue³

2003

Management Science

230

222

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Motivated by a study of the logistics systems used to manage consumable service parts for the U.S. military, we consider a static threshold-based rationing policy that is useful when pooling inventory across two demand classes characterized by different arrival rates and shortage (stockout and delay) costs. The scheme operates as a (Q, r) policy with the following feature. Demands from both classes are filled on a first-comefirst-serve basis as long as on-hand inventory lies above a threshold level K. Once on-hand inventory falls below this level, low priority (i.e., low shortage cost) demand is backordered while high priority demand continues to be filled. We analyze this static policy first under the assumption that backorders are filled according to a special threshold clearing mechanism. Structural results for the key performance measures are established to enable an efficient solution algorithm for computing stock control and rationing parameters (i.e., Q, r, and K). Numerical results confirm that the solution under this special threshold clearing mechanism closely approximates that of the priority clearing policy. We next highlight conditions where our policy offers significant savings over traditional 'round-up' and 'separate stock' policies encountered in the military and elsewhere. Finally, we develop a lower bound on the cost of the optimal rationing policy. Numerical results show that the performance gap between our static threshold policy and the optimal policy is small in environments typical of the military and high technology industries.

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“…Some researches on yield management mainly based on the price-setting problem, because the price policies always carried out in conjunction with other options such as inventory control (Curry, 1990;Wollmer, 1992;Brumelle, 1990;Robinson, 1994), revenue management (Bassok & Ernst1, 1995;Feng & Xiao, 2000;Bitran & Gilbert, 1996), and so on. A few papers discuss the system approaches to solve manufacturing problems (Doniavi, Mileham & Newnes, 1996) and to determine the effects (Sack, 1998).They analysis the workflow of the manufacturers(see figure 4) and involve a series of models on the integrated system and the discrete subsystems, then solve the problem step by step by mathematical method.…”

Section: System Dynamics Simulationsmentioning

confidence: 99%