A Dynamic Pricing Model for a Maritime Transportation Service Provider

Yanık, Seda; Coşgun, Özlem; Ekinci, Yeliz

doi:10.1080/18756891.2012.747664

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Sun, Gauri, and Webster (2011) investigate the effectiveness of alternative forecasting methods for cruise lines. Uğurlu, Coşgun, and Ekinci (2012) consider the dynamic pricing optimization problem faced by a maritime transportation service provider company and aim to find optimal prices for each level of unsold seats in each journey by using probabilistic dynamic programming. Their results show the necessity of applying a dynamic pricing policy instead of fixed pricing and the diversification of optimal policies under different conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Evidence of Revenue Management in the Cruise Line Industry

Ayvaz-Cavdaroglu

Gauri

Webster

2017

Journal of Travel Research

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Revenue management (RM) has received considerable attention from both academic and business professionals. It encompasses several techniques regarding capacity allocation, pricing, and resource management of fixed, time-sensitive capacity. RM can be roughly divided into two categories defined by the control mechanism that increases revenue: capacity allocation or price optimization. Our work falls in the latter category. In our model, we allow for partial substitutability among products (e.g., a customer making a purchase decision may consider multiple alternatives—different departure dates, different destinations, different cabin types). We also include marketing expense in addition to prices as a lever for increasing revenue. These features are relevant to dynamic pricing in practice. The method is illustrated with booking data from a cruise company, yielding optimal advertising and prices for 300 products. The application of the model results in an increase in revenue in the range of 8%–20%.

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Section: Literature Reviewmentioning

confidence: 99%

Empirical Evidence of Revenue Management in the Cruise Line Industry

Ayvaz-Cavdaroglu

Gauri

Webster

2017

Journal of Travel Research

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Dynamic pricing model for less-than-truckload carriers in the Physical Internet

2016

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International audienceThis paper investigates a less-than-truckload dynamic pricing decision-making problem in the context of the Physical Internet (PI). The PI can be seen as the interconnection of logistics networks via open PI-hubs. In terms of transport, PI-hubs can be considered as spot freight markets where LTL requests with different volumes/destinations continuously arrive over time and only remain for short periods. Carriers can bid for these requests using short-term contracts. In a dynamic, stochastic environment like this, a major concern for carriers is how to propose prices for requests to maximise their revenue. The latter is determined by the proposed price and the probability of winning the request at that price. This paper proposes a dynamic pricing model based on an auction mechanism to optimise the carrier’s bid price. An experimental study is conducted in which two pricing strategies are proposed and assessed: a unique bidding price (one unique price for all requests at an auction), and a variable bidding price (price for each request at an auction). Three influencing factors are also investigated: quantity of requests, carrier capacity, and cost. The experimental results provide insightful conclusions and useful guidelines for carriers regarding pricing decisions in PI-hubs

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