An overview of pricing models for revenue management

Bitran, Gabriel R.; Caldentey, René

doi:10.1109/emr.2016.7792415

Cited by 142 publications

(193 citation statements)

References 57 publications

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“…The multiple product case has received much less attention in the literature, presumably because of the higher degree of complexity of multiple-product formulations (Bitran and Caldentey, 2003). In this section, we modify our earlier approach in order to solve the case with multiple products.…”

Section: Generalitiesmentioning

confidence: 99%

“…As argued in Bitran and Caldentey (2003), the rapid evolution of information technologies and the corresponding growth of the Internet and e-commerce make a static assumption potentially costly to a decision-maker. In many markets today, it is possible to collect valuable information (about demand, inventory levels, competitors' strategies, etc.)…”

Section: Dynamic Policymentioning

confidence: 99%

“…The sets L and H are often called "abundant capacity" and "scarce capacity", respectively, in the existing literature (Bitran and Caldentey, 2003). We will assume below that all possible i * (defined above as the maximizers of p i (λ i ) λ i ) belong to the set H; if at least one such i * belongs to the set L, it is optimal to sell the item at price p i * for that i * throughout the selling horizon and the problem is trivial.…”

Section: Theorem 4 (One Sale Time)mentioning

confidence: 99%

“…Further, the reader is referred to Bitran and Caldentey (2003) for a survey on dynamic pricing models in revenue management. Bertsimas and Perakis (2006) present an optimization approach to dynamically set prices in order to maximize revenue in both competitive and non-competitive settings, and suggest that a decision-maker does better by incorporating realized demand information as time evolves into the policy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust timing of markdowns

Dziecichowicz

Caro²,

Thiele

2015

Ann Oper Res

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We propose an approach to the timing of markdowns over a finite time horizon in a continuous setting that does not require the precise knowledge of the underlying probabilities, instead relying on range forecasts for the arrival rates of the demand processes, and that captures the degree of the manager's risk aversion through intuitive budget of uncertainty functions. These budget functions bound the cumulative deviation of the arrival rates from their nominal values over the lengths of time for which a product is offered at a given price. A key issue is that using lengths of time as decision variables introduces non-convexities when budget functions are concave. In the single-product case, we describe a tractable and intuitive framework to incorporate uncertainty on customers' arrival rates, formulate the resulting robust optimization model, describe an efficient procedure to compute the optimal sale times, and provide theoretical insights. We then describe how to use the solution of the static robust optimization model to implement a dynamic markdown policy. We also extend the robust optimization approach to multiple products and suggest the idea of constraint aggregation to preserve performance for this type of problem structure.

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Section: Generalitiesmentioning

confidence: 99%

Section: Dynamic Policymentioning

confidence: 99%

Section: Theorem 4 (One Sale Time)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust timing of markdowns

Dziecichowicz

Caro²,

Thiele

2015

Ann Oper Res

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“…As manufacturing firms changed from "vertically integrated" to "globally decentralized" in the 1990s, many OM researchers examined various planning and contracting issues arising from decentralized supply chains (Tayur et al 1998, Simchi-Levi et al 2004, Lariviere 2016. Since 2000, many OM researchers investigated various OM issues arising from service operations (Maglio et al 2010, Karmarkar 2015, healthcare operations (Brandeau et al 2004, Green 2012, revenue management (Gallego andvan Ryzin 1994, Bitran andCaldentey 2003), innovations (Girotra and Netessine 2014), marketing and operations interfaces (Ho and Tang 2004), finance and operations interfaces (Birge et al 2007, Babich andKouvelis 2015), and human resource management and operations interfaces (Boudreau et al 2003). More recently, there are new research trends in OM research exploring issues arising from emerging markets (Iyer et al 2013), humanitarian operations (Starr and van Wassenhove 2014), supply chain risk management (Sodhi et al 2012), sustainability (Tang and Zhou 2012, Drake and Spinler 2013, Girotra and Netessine 2013, and the sharing economy (Cachon et al 2017, Taylor 2016.…”

mentioning

confidence: 99%

OM Forum—Three Simple Approaches for Young Scholars to Identify Relevant and Novel Research Topics in Operations Management

Tang

2017

M&SOM

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The revolution in information technology has provided the research community in operations management (OM) with new areas to explore and many new avenues to develop. In recognition of this, many editors of OM journals strive to publish new ideas. These two forces generate strong motivation, but many young OM scholars find it difficult to find new OM research ideas. To address this challenge, I describe three simple thought processes (or approaches) that I have learned and used to identify new research topics over the last 35 years. These approaches are as follows: (1) observe and learn to develop “problem-based” research, (2) ask “whys” to develop “phenomenon-based” research, and (3) sketch graphs to develop “insight-based” research. Clearly, these simple approaches are neither complete nor optimal; however, I share these personal thought processes with the hope of contributing to discussions as to how the next generation of OM researchers can build upon and expand the remarkable impact that our field has had and will continue to have.

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Market competition and pricing strategies in retail supply chains

2020

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This study provides a better explanation for the continued prevalence of high–low (Hi–Lo) pricing strategy. We investigate the impact of market competition on adopting two different pricing strategies in the retail industry: everyday low price (EDLP) strategy and Hi–Lo strategy. We developed two analytic models using a game‐theoretic modeling approach: the profit maximization model and the sales revenue maximization model. We then conducted an econometric analysis based on retail store‐level dataset. The result shows that an EDLP player's equilibrium price depends highly on the cost level rather than competitor's price whereas the Hi–Lo player's equilibrium price depends mainly on the range of promotional basket as well as the cost level.

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An overview of pricing models for revenue management

Cited by 142 publications

References 57 publications

Robust timing of markdowns

Robust timing of markdowns

OM Forum—Three Simple Approaches for Young Scholars to Identify Relevant and Novel Research Topics in Operations Management

Market competition and pricing strategies in retail supply chains

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