Modeling the Transient Nature of Dynamic Pricing with Demand Learning in a Competitive Environment

Kachani, Soulaymane; Perakis, Georgia; Simon, Carine

doi:10.1007/0-387-71134-1_11

Cited by 9 publications

(16 citation statements)

References 23 publications

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“…Maximum likelihood estimation is also quite commonly used (Broder and Rusmevichientong 2012, Carvalho and Puterman 2005, den , Boer and Zwart 2011, 2014. Other approaches used include linear least squares estimation (Bertsimas and Perakis 2006, Besbes and Zeevi 2014, Cooper et al 2013, Kachani et al 2007, Keskin and Zeevi 2013, 2014, and simple empirical estimation which is quite different from all other approaches and is described in detail below when we review the corresponding papers (Besbes and Zeevi 2009, 2011, Chen and Farias 2013, Wang et al 2014. Unlike the Bayesian approach, these approaches do not require prior knowledge of an unknown parameter.…”

Section: Parametric Problemsmentioning

confidence: 99%

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Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information

Chen

2015

Production and Operations Management

176

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Dynamic pricing enables a firm to increase revenue by better matching supply with demand, responding to shifting demand patterns, and achieving customer segmentation. In the last 20 years, numerous success stories of dynamic pricing applications have motivated a rapidly growing research interest in a variety of dynamic pricing problems in the academic literature. A large class of problems that arise in various revenue management applications involve selling a given amount of inventory over a finite time horizon without inventory replenishment. In this study, we identify most recent trends in dynamic pricing research involving such problems. We review existing research on three new classes of problems that have attracted a rapidly growing interest in the last several years, namely, problems with multiple products, problems with competition, and problems with limited demand information. We also identify a number of possible directions for future research.

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Section: Parametric Problemsmentioning

confidence: 99%

“…For the finite inventory case, a myopic solution approach is proposed. Kachani et al (2007) consider a similar problem and propose a solution approach consisting of three steps in each period. First, assuming that the demand parameters are given, each firm finds Nash equilibrium demand by solving a best-response problem.…”

Section: Parametric Problemsmentioning

confidence: 99%

Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information

Chen

2015

Production and Operations Management

176

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“…Based on (14) and (15), Figure 2 exhibits the optimal profit π * 1 (n) for pattern At the price vector p = {30, 30, 30}, this set of demand functions has the same direct price We, again, address the best response problem firm 1 faces when both of his competitors choose a price of $30 and K = 5000. Based on (18), Figure 3 again exhibits the optimal profit π * i (n) for patterns (I) and (VI), as a function of the permitted number of orders n. Under pattern (I) ((VI)), the optimal price is $40.83 ($39.63) and the optimal number of orders 20 (18). (When K = 1000, it is optimal to place an order in every period under both patterns.…”

Section: ) and The Critical Values R(1) R(m)mentioning

confidence: 98%

Competition under time‐varying demands and dynamic lot sizing costs

Federgruen

Meissner

2008

Naval Research Logistics

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We develop a competitive pricing model which combines the complexity of time-varying demand and cost functions and that of scale economies arising from dynamic lot sizing costs. Each firm can replenish inventory in each of the T periods into which the planning horizon is partitioned. Fixed as well as variable procurement costs are incurred for each procurement order, along with inventory carrying costs. Each firm adopts, at the beginning of the planning horizon, a (single) price to be employed throughout the horizon. Based on each period's system of demand equations, these prices determine a time series of demands for each firm, which needs to service them with an optimal corresponding dynamic lot sizing plan. We establish the existence of a price equilibrium and associated optimal dynamic lotsizing plans, under mild conditions. We also design efficient procedures to compute the equilibrium prices and dynamic lotsizing plans.

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“…in our model. For example, Kachani et al (2007) design an approach that enable to achieve dynamic pricing while learning the price-demand linear relationship in an oligopoly. Caro and Gallien (2007) investigate how demand learning effects impact the dynamic assortment of seasonal consumer goods.…”

Section: The Demand Modelmentioning

confidence: 99%

Dynamic pricing and inventory control: robust vs. stochastic uncertainty models—a computational study

Adida

Perakis²

2010

Ann Oper Res

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In this paper, we consider a variety of models for dealing with demand uncertainty for a joint dynamic pricing and inventory control problem in a make-to-stock manufacturing system. We consider a multi-product capacitated, dynamic setting, where demand depends linearly on the price. Our goal is to address demand uncertainty using various robust and stochastic optimization approaches. For each of these approaches, we first introduce closed-loop formulations (adjustable robust and dynamic programming), where decisions for a given time period are made at the beginning of the time period, and uncertainty unfolds as time evolves. We then describe models in an open-loop setting, where decisions for the entire time horizon must be made at time zero. We conclude that the affine adjustable robust approach performs well (when compared to the other approaches such as dynamic programming, stochastic programming and robust open loop approaches) in terms of realized profits and protection against constraint violation while at the same time it is computationally tractable. Furthermore, we compare the complexity of these models and discuss some insights on a numerical example.

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Modeling the Transient Nature of Dynamic Pricing with Demand Learning in a Competitive Environment

Cited by 9 publications

References 23 publications

Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information

Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information

Competition under time‐varying demands and dynamic lot sizing costs

Dynamic pricing and inventory control: robust vs. stochastic uncertainty models—a computational study

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