Results 1  10
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27
Dynamic assortment with demand learning for seasonal consumer goods
 MANAGEMENT SCI
, 2007
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A partially observed markov decision process for dynamic pricing
 Management Science
, 2002
"... In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework, to study a dynamic pricing problem faced by sellers of fashionlike goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer i ..."
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Cited by 40 (1 self)
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In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework, to study a dynamic pricing problem faced by sellers of fashionlike goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings together various types of uncertainties about the demand, some of which are resolvable through sales observations. We develop a rigorous upper bound for the seller’s optimal dynamic decision problem and use it to propose an activelearning heuristic pricing policy. We conduct a numerical study to test the performance of four different heuristic dynamic pricing policies, in order to gain insights into several important managerial questions that arise in the context of revenue management.
Dynamic pricing for nonperishable products with demand learning
"... Abstract A retailer is endowed with a finite inventory of a nonperishable product. Demand for this product is driven by a pricesensitive Poisson process that depends on an unknown parameter, θ; a proxy for the market size. If θ is high then the retailer can take advantage of a large market chargi ..."
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Cited by 21 (0 self)
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Abstract A retailer is endowed with a finite inventory of a nonperishable product. Demand for this product is driven by a pricesensitive Poisson process that depends on an unknown parameter, θ; a proxy for the market size. If θ is high then the retailer can take advantage of a large market charging premium prices, but if θ is small then price markdowns can be applied to encourage sales. The retailer has a prior belief on the value of θ which he updates as time and available information (prices and sales) evolve. We also assume that the retailer faces an opportunity cost when selling this nonperishable product. This opportunity cost is given by the longterm average discounted profits that the retailer can make if he switches and starts selling a different assortment of products. The retailer's objective is to maximize the discounted longterm average profits of his operation using dynamic pricing policies. We consider two cases. In the first case, the retailer is constrained to sell the entire initial stock of the nonperishable product before a different assortment is considered. In the second case, the retailer is able to stop selling the nonperishable product at any time to switch to a different menu of products. In both cases, the retailer's pricing policy tradesoff immediate revenues and future profits based on active demand learning. We formulate the retailer's problem as a (Poisson) intensity control problem and derive structural properties of an optimal solution which we use to propose a simple approximated solution. This solution combines a pricing policy and a stopping rule (if stopping is an option) depending on the inventory position and the retailer's belief about the value of θ. We use numerical computations, together with asymptotic analysis, to evaluate the performance of our proposed solution.
Dynamic product assembly and inventory control for maximum profit. ArXiv
, 2010
"... Abstract — We consider a manufacturing plant that purchases raw materials for product assembly and then sells the final products to customers. There are M types of raw materials and K types of products, and each product uses a certain subset of raw materials for assembly. The plant operates in slott ..."
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Cited by 8 (7 self)
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Abstract — We consider a manufacturing plant that purchases raw materials for product assembly and then sells the final products to customers. There are M types of raw materials and K types of products, and each product uses a certain subset of raw materials for assembly. The plant operates in slotted time, and every slot it makes decisions about restocking materials and pricing the existing products in reaction to (possibly timevarying) material costs and consumer demands. We develop a dynamic purchasing and pricing policy that yields time average profit within of optimality, for any given > 0, with a worst case storage buffer requirement that is O(1/). The policy can be implemented easily for large M, K, yields fast convergence times, and is robust to nonergodic system dynamics. Index Terms — Queueing analysis, pricing, optimization I.
Blind network revenue management
 Operations Research
, 2012
"... We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem i ..."
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Cited by 7 (0 self)
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We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time, but does not know the underlying demand function which maps prices into instantaneous demand rate. We introduce a family of “blind ” pricing policies which are designed to balance tradeoffs between exploration (demand learning) and exploitation (pricing to optimize revenues). We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori and prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.
A Monopolistic and Oligopolistic Stochastic Flow Revenue Management Model
, 2006
"... This paper studies a oneshot inventory replenishment problem with dynamic pricing. The customer arrival rate is assumed to follow a geometric Brownian motion. Homogeneous customers have an isoelastic demand function and do not behave strategically. We find a closedform optimal pricing policy, whic ..."
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Cited by 5 (0 self)
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This paper studies a oneshot inventory replenishment problem with dynamic pricing. The customer arrival rate is assumed to follow a geometric Brownian motion. Homogeneous customers have an isoelastic demand function and do not behave strategically. We find a closedform optimal pricing policy, which utilizes current demand information. Under this pricing policy the inventory trajectory is deterministic, and a retailer sells all inventory. We show that dynamic pricing coordinated with the inventory decision achieves significantly higher profits than does static pricing. Furthermore, under oligopolistic competition we establish a weak perfect Bayesian equilibrium for the price and inventory replenishment game. We find the pricing equilibrium to be cooperative even in a noncooperative environment, but that inventory competition results in overstock and damages profits. Finally, we examine the tradeoff between dynamic pricing and price precommitment and find that flexible pricing is still beneficial, provided competition is not too intense.
Information acquisition for capacity planning via pricing and advance selling: When to stop and act
 Oper. Res
"... In this paper, we investigate a capacity planning strategy that collects commitments to purchase before the capacity decision, and uses the acquired advance sales information to decide on the capacity. In particular, we study a profitmaximization model in which a manufacturer collects advance sale ..."
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Cited by 5 (1 self)
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In this paper, we investigate a capacity planning strategy that collects commitments to purchase before the capacity decision, and uses the acquired advance sales information to decide on the capacity. In particular, we study a profitmaximization model in which a manufacturer collects advance sales information periodically prior to the regular sales season for a capacity decision. Customer demand is stochastic and pricesensitive. Once the capacity is set, the manufacturer produces and satisfies customer demand (to the extent possible) from the installed capacity, during the regular sales period. We study scenarios in which the advance sales and regular sales season prices are set exogenously and optimally. For both scenarios, we establish the optimality of a control band policy that describes when to stop acquiring advance sales information and how much capacity to build. A numerical study shows that advance selling can improve the manufacturer’s profit significantly. We generate insights into how operating conditions (such as the capacity building cost) and market characteristics (such as demand variability) affect the value of information acquired through advance selling. From this analysis, we identify the conditions under which advance selling for capacity planning is most valuable for the manufacturer.
Dynamic pricing and learning: historical origins, current research, and new directions. Working paper. Available at http://ssrn.com/abstract=2334429
"... The topic of dynamic pricing and learning has received a considerable amount of attention in recent years, from different scientific communities. We survey these literature streams: we provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, p ..."
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Cited by 5 (2 self)
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The topic of dynamic pricing and learning has received a considerable amount of attention in recent years, from different scientific communities. We survey these literature streams: we provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an indepth overview of the available literature on dynamic pricing and learning. Our focus is on the operations research and management science literature, but we also discuss relevant contributions from marketing, economics, econometrics, and computer science. We discuss relations with methodologically related research areas, and identify directions for future research.
Simple Policies for Dynamic Pricing with Imperfect Forecasts
, 2011
"... We consider the ‘classical ’ single product dynamic pricing problem allowing the ‘scale ’ of demand intensity to be modulated by an an exogenous ‘market size ’ stochastic process. This is a natural model of dynamically changing market conditions. We show that for a broad family of Gaussian market si ..."
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Cited by 4 (1 self)
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We consider the ‘classical ’ single product dynamic pricing problem allowing the ‘scale ’ of demand intensity to be modulated by an an exogenous ‘market size ’ stochastic process. This is a natural model of dynamically changing market conditions. We show that for a broad family of Gaussian market size processes, simple dynamic pricing rules that are essentially agnostic to the specification of this market size process perform provably well. The pricing policies we develop are shown to compensate for forecast imperfections (or a lack of forecast information altogether) by frequent reoptimization and reestimation of the ‘instantaneous ’ market size. 1.
Blind nonparametric revenue management: Asymptotic optimality of a joint learning and pricing method. Working Paper
, 2006
"... We consider a general class of network revenue management problems in which multiple products are linked by various resource constraints. Demand is modeled as a multivariate Poisson process whose instantaneous rate at each point in time is determined by a vector of prices set by the decision maker. ..."
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Cited by 4 (0 self)
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We consider a general class of network revenue management problems in which multiple products are linked by various resource constraints. Demand is modeled as a multivariate Poisson process whose instantaneous rate at each point in time is determined by a vector of prices set by the decision maker. The objective is to price the products so as to maximize expected revenues over a finite sales horizon. The decision maker observes realized demand over time, but is otherwise “blind ” to the underlying demand function which maps prices into the instantaneous demand rate. Few structural assumptions are made with regard to the demand function, in particular, it need not admit any parametric representation. We introduce a general method for solving such blind revenue management problems: first a learning phase experiments with a “small ” number of prices over an initial “short ” time interval; then a simple optimization problem is solved using an estimate of the demand function obtained from the previous stage, and a nearoptimal price is fixed for the remainder of the time horizon. To evaluate the performance of the proposed method we compare the revenues it generates to those corresponding to the optimal dynamic pricing policy that knows the demand function a priori. In a regime where the sales volume grows large, we prove that the gap in performance is suitably small; in that sense, the proposed method is asymptotically optimal.