Web services have become quite popular over the last few years as they allow easier development and integration of business applications. In this paper, we consider a web service pricing problem where two providers compete through dynamic pricing. Each provider offers access to a web service with different quality classes where users may buy their required web service through a reservation system. They would like to adjust the prices of their web services over a pre-specified time horizon to manage demand and to maximize profit. Users have the right with no obligation to cancel their services as long as they pay a penalty. We consider a dynamic setting where the web service classes share a capacity. We first develop a time continuous model for competitive pricing of a web service and then we provide some insights about the equilibrium condition of the problem using open-loop differential game and propose an algorithm to obtain the optimal pricing policy for providers. Moreover, we conduct numerical analyses to examine the impacts of some parameters on control and state variables.

The article presents solution procedure of geometric programming to solve the structural model with imprecise coefficients. We have considered a single objective structural optimization model with weight as an objective function. Geometric programming provides a powerful tool for solving a variety of imprecise optimization problems. Here we use nearest interval approximation method to convert a triangular fuzzy number to an interval number. In this paper, we transform this interval number to a parametric interval-valued functional form and then solve the parametric problem by geometric programming technique. The advantage of this technique is that we can find directly optimal solution of the objective function without solving two-level mathematical programs. Numerical example is given to illustrate the model through this approximation method.

Many firms try to optimize their supply and production levels separately, but this method could limit influence profitability, negatively. Thus, it is becoming more important to analyze these two levels, simultaneously. In this paper, an integrated supply-production planning is considered, simultaneously. We develop a mathematical model, which calculates the optimal inventory lot sizing for each supplier and minimizes the total cost associated with the process of procuring raw material, transferring and holding raw materials and manufacturing. The problem is formulated as a nonlinear programming and heuristic genetic algorithm (GA) method is developed to solve the resulted problem. We examine the performance of the proposed model for a case study conducted in Iran. Experimental results show that such a model can reduce the costs of the case study, substantially.

Portfolio selection is one of the important problems encountered by any investor. The purpose of this paper is to solve a real stock portfolio selection problem in Iran. According to the uncertain environments in which financial decisions are made, most of the recent works in this field use fuzzy sets theory in order to incorporate these uncertainties into their analysis. The problem is to determine how to allocate a limited fund among the stocks of some pharmaceutical companies in Tehran stock exchange. For this purpose we apply two fuzzy analytic hierarchy process (FAHP) methods to this problem. Finally, the results obtained from the two methods are compared in terms of the solution quality.

This paper studies inventory and pricing policies in a non-cooperative supply chain with one supplier and several retailers who are involved in producing, delivering and selling a single product. We consider inventory policies in an information-asymmetric vendor managed inventory. The study consists of different scenarios where a supplier produces the product at the wholesale price to multiple retailers. The retailers also distribute the product in dispersed and independent markets at retail selling prices. The demand rate for each market is a non-decreasing concave function of the marketing expenditures of both local retailers and the manufacturer, but a non-increasing and convex function of the retail selling prices. The primary purpose is to determine wholesale price, marketing expenditure for supplier and retailers, replenishment cycles for the product, and backorder quantity to maximize the total profit for both groups of supplier and retailers. All scenarios are modeled as a Stackelberg game where the manufacturer is the leader and the retailers are the followers. A numerical study has been presented to demonstrate the influences of decision variables and/or parameters in various scenarios. Finally, sensitivity analysis is performed to study the impact of different parameters on total profitability.