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.


Effects of the number of rooms required by LTS.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4493130&req=5

pone.0128574.g007: Effects of the number of rooms required by LTS.

Mentions: In this example, the LTS customers intend to reserves n rooms of the hotel for 10 days, and the reservation lead time is 10 days before check-in time, that is, n = {1,2,…,20}, T = 10, and T0 = 10. In addition, the unit penalty cost is set as twice that of room price (i.e., 500). We still use the room sale statuses under two situations in Table 1. By calculating, we obtain the expected profit without accepting LTS and the expected profit by accepting LTS under different numbers of rooms required by LTS, as shown in Fig 7.


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)

Effects of the number of rooms required by LTS.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0128574.g007: Effects of the number of rooms required by LTS.
Mentions: In this example, the LTS customers intend to reserves n rooms of the hotel for 10 days, and the reservation lead time is 10 days before check-in time, that is, n = {1,2,…,20}, T = 10, and T0 = 10. In addition, the unit penalty cost is set as twice that of room price (i.e., 500). We still use the room sale statuses under two situations in Table 1. By calculating, we obtain the expected profit without accepting LTS and the expected profit by accepting LTS under different numbers of rooms required by LTS, as shown in Fig 7.

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.