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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.


Hotel room demand based on lead time
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pone.0128574.g005: Hotel room demand based on lead time

Mentions: To illustrate the optimal decision rule and explore the effects of the parameters on optimal decisions, we present the results of numerical experiments in this section. We assume that the hotel has Q = 500 rooms, and the variable cost of each room per day is c = 50. The fixed cost per day is ignored. In addition, for obtaining the room sale statuses under different situations, we adopt a simple algorithm to generate random numbers to represent the demand on each day by meeting the following demand function,qt=1000e−0.008t−180lnp±200e−0.01t,(11)which is similar to demand function proposed in Guo et al. [38]. We consider that demand on one day changes within a certain range, and set the room price as a constant value at 250. In reality, hotels charge the same price during a number of days. For instance, from January 22 to February 3, 2015, V Hotel Lavender in Singapore charges $104.2 per room, and the Upper House in Hong Kong charges $471.2 per room. The demand function can be changed toqt=1000e−0.008t−180ln250±200e−0.01t,(12)which is shown in Fig 5.


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)

Hotel room demand based on lead time
© Copyright Policy
Related In: Results  -  Collection

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

pone.0128574.g005: Hotel room demand based on lead time
Mentions: To illustrate the optimal decision rule and explore the effects of the parameters on optimal decisions, we present the results of numerical experiments in this section. We assume that the hotel has Q = 500 rooms, and the variable cost of each room per day is c = 50. The fixed cost per day is ignored. In addition, for obtaining the room sale statuses under different situations, we adopt a simple algorithm to generate random numbers to represent the demand on each day by meeting the following demand function,qt=1000e−0.008t−180lnp±200e−0.01t,(11)which is similar to demand function proposed in Guo et al. [38]. We consider that demand on one day changes within a certain range, and set the room price as a constant value at 250. In reality, hotels charge the same price during a number of days. For instance, from January 22 to February 3, 2015, V Hotel Lavender in Singapore charges $104.2 per room, and the Upper House in Hong Kong charges $471.2 per room. The demand function can be changed toqt=1000e−0.008t−180ln250±200e−0.01t,(12)which is shown in Fig 5.

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.