A Stochastic Location-Allocation Model for Specialized Services in a Multihospital System

Rising costs, increasing demand, wasteful spending, and limited resources in the healthcare industry lead to an increasing pressure on hospital administrators to become as efficient as possible in all aspects of their operations including location-allocation. Some promising strategies for tackling these challenges are joining some hospitals to form multihospital systems (MHSs), specialization, and using the benefits of pooling resources. We develop a stochastic optimization model to determine the number, capacity, and location of hospitals in a MHS offering specialized services while they leverage benefits of pooling resources. The model minimizes the total cost borne by the MHS and its patients and incorporates patient service level, patient retention rates, and type of demand. Some computational analyses are carried out to gauge the benefits of optimally sharing resources for delivering specialized services across a subset of hospitals in the MHS against complete decentralization (CD) and full centralization (FC) policies

Identifying and Ranking the Factors Affecting the Acceptance of Artificial Intelligence in the Public and Private Sectors

One of the most important issues in the development of artificial intelligence is the adoption of the use of artificial intelligence by the private and public sectors. In other words, in order for artificial intelligence to be used in a country or industry, it is necessary to identify and evaluate the important factors of adoption. The purpose of this study is to identify and rank the factors affecting admission in the public and private sectors in Iran. For this purpose, first, a set of models and factors affecting the adoption of technology were extracted from the literature and opinions of experts and were classified into three categories: technological, organizational and environmental factors Then, the most important factors in each category were determined through a collection questionnaire, and using nonparametric Friedman test for each category with the most important and least important criteria. In order to weight and prioritize the factors, the quantitative approach and BWM technique have been used. The statistical population of the study included 37 experts in artificial intelligence in the public sector and 45 experts in the private sector. According to the obtained results, in the public sector, 3 important factors of admission are the support of senior managers, the existence of the required infrastructure for artificial intelligence and the existence of specialized and capable forces in the field of artificial intelligence. Efficiency and productivity with the use of artificial intelligence, cost savings with the use of artificial intelligence and ease of use and learning has been easy

Evaluation of Indicators for Measuring and Accepting Mobile Commerce

With the increasing influence of the media in society, the need to use mobile commerce has become more important than ever. Due to the novelty of this field, the adoption of its use by individuals has faced various challenges. Therefore, it is necessary to identify the factors affecting the adoption of mobile commerce and their effects on each other to be carefully examined. To conduct the study, the factors affecting adoption were first identified through the literature and localized by expert review, and then analyzed using fuzzy cognitive mapping method. The data used for the analysis were collected by 15 experts in the field of mobile business, 7 of whom were in the field of academia and 8 of whom were active in the industry through an online questionnaire. The results showed that Customer satisfaction with the performance of mobile commerce, customer loyalty to the use of this technology, expanding wireless network coverage by mobile operators, increasing customer trust in online vendors, increasing management skills in using mobile commerce have a higher priority than other factors. Finally, according to the results, policy recommendations for the development of mobile commerce are provided to organizations, government, telecom operators, financial service providers, and mobile application developers

Dynamic Pricing of a Web Service in an Advance Selling Environment

Consider a web service with different quality of service levels where users may purchase their required web service through a reservation system. The service provider adjusts prices of web service classes over a prespecified time horizon to manage demand and maximize profit. Users may cancel their services as long as they pay a penalty. One of the important challenges for service providers is capacity limitation of the resources employed in offering the web service. Thus, taking this important proposition into account makes pricing strategies considered by the provider has more credit. Another important factor in determining pricing strategies discussed in the present paper is the market influence which can increase or decrease the price that the provider offers. This paper develops a continuous time optimal control model for identifying pricing strategies for the web service classes. We study the optimality condition of the considered model based on maximum principal and propose an algorithm to obtain the optimal pricing policy. Moreover, we perform numerical analyses to evaluate the effect of some parameters on control and state variables and objective function. In addition, we compare the proposed algorithm with genetic algorithm (GA) and simulated annealing (SA) available in Matlab