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A revenue management approach for tourism logistics optimization

Authors: Di Puglia Pugliese L.; Guerriero F.; Miglionico G.;

A revenue management approach for tourism logistics optimization

Abstract

AbstractTourism is a very complex industry and represents one of the most profitable activity in the world. A tourist product requires the execution of several multifaceted activities. Indeed, transportation, accommodation, entertainment, food, and beverages represent only some of the products/services required by a tourist. It is evident that a single enterprise cannot provide all the components of a tourist product, but several interrelated actors should collaborate. This leads to a very complex supply tourist chain that needs to be optimized. The relevant theories and methods of logistics can be used to efficiently manage all the flows that are generated in a tourist chain. The definition of appropriate policies, at the different nodes of the chain, can improve the performance of all the actors involved in tourism logistics. In this paper, we concentrate our attention on tour operators that are relevant in the touristic logistic chain since they are involved in several activities. We introduce different revenue management policies to support tour operators in the decision of accepting the most profitable tourist requests. A request consists of flights and hotel booking, characterized by a starting time of the trip and the length of stay at the destination. We allow for various combinations of flight legs and multiple categories of hotels to accommodate a variety of customer preferences and needs. A computational study is carried out by considering different scenarios, and the performance of the considered revenue management policies is analyzed in detail.

Country
Italy
Keywords

buy-up, tourism logistics, revenue management, bid price policy, booking limit policy, holiday package, Operations research, mathematical programming

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green