As the world is getting overpopulated and over polluted the human being is seeking to utilize new sources of energy that are cleaner, cheaper, and more accessible. Wind is one of these clean energy sources that is accessible everywhere on the planet earth. This source of energy cannot be stored for later use; therefore, environmental circumstances and geographical location of wind plants are crucial matters. This study proposes a model to decide on the optimum location for a wind farm among the demand area. To tackle the uncertainty related to the geographical position of the nominated location such as wind speed; altitude; mean temperature; and humidity; a simulation method is applied on the problem. Other factors such as the time that a plant is out of service and demand fluctuations also have been considered in the simulation phase. Moreover, a probability distribution function is calculated for the turbine power. Then Data Envelopment Analysis (DEA) performs the selection between all the nominated locations for wind farm. The proposed model takes into account several important elements of the problems. Elements such as land cost; average power received from the wind blowing; demand point population etc. are considered at the same time to select the optimum location of wind plants. Finally, the model is applied on a real case in order to demonstrate its reliability and applicability.

During the past few years, operations research applications in health care operation management have grown quickly. On the other hand blood as a perishable, valuable and lifesaving product is one important asset of any healthcare center. Therefore, designing a blood supply network comes to importance. It also should be noted that a blood supply chain comprises specific modifications. This study intends to locate blood bank components in a network, and to determine the allocations among the network components. The supply chain components considered in this study are donation sites, testing and processing labs, blood banks, and demand points. It is known that demand centers such as hospitals and clinics highly depend on blood products and any deficiency in procurement can even result in a person’s death. Thus, in the last layer of the considered network a transshipment sub-network is considered between demand points. Most of the intricacies in problem formulation of blood supply chain are regarded in this study; cases such as blood wastage, blood product decomposition in lab facilities, and transshipments between demand points. Due to the fact that for such an important and lifesaving supply chain the aim would go beyond minimizing cost, another objective function is presented for the problem. Hence, to obtain a Pareto solution for both objective functions ?-constraint method is utilized. Finally, to demonstrate the applicability of the problem, the model is implemented on a number of problem sets.