Limits...
A Multi-Period Optimization Model for Service Providers Using Online Reservation Systems: An Application to Hotels.

Xu M, Jiao Y, Li X, Cao Q, Wang X - PLoS ONE (2015)

Bottom Line: LTS may guarantee a specific amount of demand and generate opportunity income for a certain number of periods, meanwhile with risk of punishment incurred by overselling.By developing an operational optimization model and exploring the effects of parameters on optimal decisions, we suggest that service providers should make decisions based on the types of customers, number of products required, and duration of multi-period to reduce the loss of reputation and obtain more profit; at the same time, multi-period buying customers should buy products early.Finally, the paper conducts a numerical experiment, and the results are consistent with prevailing situations.

View Article: PubMed Central - PubMed

Affiliation: College of Tourism and Service Management, Nankai University, Tianjin, 300074, China.

ABSTRACT
This paper presents a multi-period optimization model for high margin and zero salvage products in online distribution channels with classifying customers based on number of products required. Taking hotel customers as an example, one is regular customers who reserve rooms for one day, and the other is long term stay (LTS) customers who reserve rooms for a number of days. LTS may guarantee a specific amount of demand and generate opportunity income for a certain number of periods, meanwhile with risk of punishment incurred by overselling. By developing an operational optimization model and exploring the effects of parameters on optimal decisions, we suggest that service providers should make decisions based on the types of customers, number of products required, and duration of multi-period to reduce the loss of reputation and obtain more profit; at the same time, multi-period buying customers should buy products early. Finally, the paper conducts a numerical experiment, and the results are consistent with prevailing situations.

No MeSH data available.


Example demand curves.
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pone.0128574.g003: Example demand curves.

Mentions: For hotel rooms on a particular day, the demand of customers is related to the room price and reservation time. We follow the demand model proposed by Guo et al. [38]. At time t before the target day, the demand curve shows the number of rooms demanded, qt, which is given by the decreasing relationship of the room rate p, that is, qt = at-f(p), where at = g(a0,t) is the sale saturate market demand of hotel rooms, increasing with the time closing to the target day; and f(p) is an increasing function of p, in relation to consumer sensitivity. However, due to the uncertainty, we regard each room at a particular day as a number of limited products, and the demand of customers of the hotel each day is an independent, identically distributed random variable. The room demand per day can be described asqt=at−f(p)+εt,(1)where εt is a random variable related with time (see Fig 3).


A Multi-Period Optimization Model for Service Providers Using Online Reservation Systems: An Application to Hotels.

Xu M, Jiao Y, Li X, Cao Q, Wang X - PLoS ONE (2015)

Example demand curves.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493130&req=5

pone.0128574.g003: Example demand curves.
Mentions: For hotel rooms on a particular day, the demand of customers is related to the room price and reservation time. We follow the demand model proposed by Guo et al. [38]. At time t before the target day, the demand curve shows the number of rooms demanded, qt, which is given by the decreasing relationship of the room rate p, that is, qt = at-f(p), where at = g(a0,t) is the sale saturate market demand of hotel rooms, increasing with the time closing to the target day; and f(p) is an increasing function of p, in relation to consumer sensitivity. However, due to the uncertainty, we regard each room at a particular day as a number of limited products, and the demand of customers of the hotel each day is an independent, identically distributed random variable. The room demand per day can be described asqt=at−f(p)+εt,(1)where εt is a random variable related with time (see Fig 3).

Bottom Line: LTS may guarantee a specific amount of demand and generate opportunity income for a certain number of periods, meanwhile with risk of punishment incurred by overselling.By developing an operational optimization model and exploring the effects of parameters on optimal decisions, we suggest that service providers should make decisions based on the types of customers, number of products required, and duration of multi-period to reduce the loss of reputation and obtain more profit; at the same time, multi-period buying customers should buy products early.Finally, the paper conducts a numerical experiment, and the results are consistent with prevailing situations.

View Article: PubMed Central - PubMed

Affiliation: College of Tourism and Service Management, Nankai University, Tianjin, 300074, China.

ABSTRACT
This paper presents a multi-period optimization model for high margin and zero salvage products in online distribution channels with classifying customers based on number of products required. Taking hotel customers as an example, one is regular customers who reserve rooms for one day, and the other is long term stay (LTS) customers who reserve rooms for a number of days. LTS may guarantee a specific amount of demand and generate opportunity income for a certain number of periods, meanwhile with risk of punishment incurred by overselling. By developing an operational optimization model and exploring the effects of parameters on optimal decisions, we suggest that service providers should make decisions based on the types of customers, number of products required, and duration of multi-period to reduce the loss of reputation and obtain more profit; at the same time, multi-period buying customers should buy products early. Finally, the paper conducts a numerical experiment, and the results are consistent with prevailing situations.

No MeSH data available.