چكيده به لاتين
Abstract:
In recent years, there has been a general application of web services by software companies. Through web services, the various businesses would be able to develop and integrate different systems by paying very little costs. Due to the fact that the users of a web service have different computational needs, it is necessary to match the quality of the offered services with the type of user. In other words, it is essential to offer different classes of resources with different service levels in order to better manage the used resources. Otherwise, firstly the sales revenue of resources will be reduced. Secondly, the service providers will gradually lose their customers.
Dynamic pricing strategies is very common in the literature of pricing and revenue management, particularly in industries such as aviation industry, tourism, internet services, production management and inventory control. In this kind of pricing, the seller changes the prices over time to manage the demand and increase the profit. In so doing, when supply is greater than demand, the provider reduces the prices for the demand to increase and reach balance, and when demand is greater than supply, the prices are raised and the supply is reduced to get to equilibrium. Dynamic pricing is a suitable pricing strategy due to the existance of web service demand information, the easiness of changing prices over time, and the possibility of using proper analytic tools.
The present study includes the development of five time-continuous models for determining the web service pricing strategy which is analyzed by optimal control theory, robust optimization and differential games. After giving the definition of the problem, the review of literature section is offered, discussing issues such as web services, optimal control, differential games and web service pricing. The first model of pricing strategies designates a web service provider who pre-sells their web service and the users can cancel their orders during the pre-sale period. In the second model, the limitation of reservation level at any point of pre-sale range is added to the first model. Such a limitation increases the complexities of solving the model. In the third model, the demand of web services is taken uncertain and after robust optimization, the indeterministic model is turned into a deterministic model. The obtained deterministic model for this model is similar to the first model. In the fourth model, the assumption of developing every web service by two providers is also considered in the model. Finally, in the fifth model, the demand of web services for each of the two providers is considered indeterministic. This model is also turned into a deterministic model using an approach similar to that of the third model. The obtained deterministic model is similar to the fourth model. Models 1,2, and 3 are analyzed using optimal control theory, and models 4 and 5 are investigated by open-loop differential games. It is worth noting that in the present study, an algorithm is offered to determine the optimal result for each of the existing models. Finally, using numerical analysis, the effect of some of the important parameters on pricing strategies, cancelation revenue, sales revenue, total revenue, and profit is studied for each of the presented models. The presented algorithms consider optimal price in a time function where the service providers can determine pricing strategies at any point in time without modeling and solving the problem again. The analytic and numerical results indicated that the maximum increase of demand leads to a rise in prices, profit, and web service sales. Failing to take the possibility of cancelling orders by the service provider into account will make the optimal price considered by the service provider have less efficieny. Moreover, the results of the investigation showed that increasing the cancelation rate by the users will decrease the sales revenue and profit.
Keywords: Web Service, Dynamic Pricing, Optimal Control Theory, Differential Game, Robust Optimization, Revenue Management, Pricing, E-Commerce.
