One of the new issues that have been raised in recent years is the hub network design problem. The hubs are collection and distribution centers that are used for the purpose of less connections and more of indirect than direct communications. They are interface facilities which are used as switch centers to collect and distribute flows in the network. They determine routes and organize traffic between source-destination in order to provide high performance and be more inexpensive. In the hub LOCATION problem, the aim is to find a suitable LOCATION for the hub and routes for sending information from a source to a destination, in order to reduce costs and gain desired purpose by multiple transfers between the hubs. In this paper, teaching and learning based OPTIMIZATION, particle swarm OPTIMIZATION and imperialist competitive algorithm were studied for locating optimally hubs and allocating nodes to the nearest located hub nodes. Experimental results show that optimal LOCATION for hubs by using cluster-based OPTIMIZATION algorithm (TLBO) successfully has been performed with extreme accuracy and precision.

Decision-making concerning the LOCATION of critical resource on the geographical network is important in many industries. In the healthcare system, these decisions include LOCATION of emergency and preventive care. The decisions of LOCATION play a crucial role due to determining the travel time between supply and demand points and response time in emergencies. Organs are considered as highly perishable products, whose variety of each product has a specific perish time. Despite the importance of this field, only a small proportion of healthcare sector is dedicated to this field. Matching and finding the best recipient for a donated organ is one of the major problems in this field, which is also crucial for the overall organ transplantation process. Balancing the demand and supply in a transplant organ supply chain in order to decrease the waiting list needs certain scheduling and management. The main contribution of this paper consists of considering recipient regions as another component of the supply chain; in addition, importance of transportation time and waiting lists has led us to consider a bi-objective model. In addition, uncertainty of input data has led us to consider a stochastic approach.

Despite the fact that many governments try to set rules that guarantee having resistant buildings, there are many vulnerable structures in the world. Hence, establishing earthquake relief centers is an important issue in order to control the effect of an earthquake. Iran is a country in middle east which is severely vulnerable against earthquake. Yazd is a central city in Iran. Since there is no such a study for Yazd city, this city is considered in this study.  The parcels' layer of the GIS map of Yazd city has been used as the input of the problem. Since the LOCATION alLOCATION of relief centers is a problem with huge complexity and cannot be solved in polynomial time, Whale OPTIMIZATION Algorithm (WOA) has been used to solve the problem. The Whale OPTIMIZATION Algorithm or The WOA is a particle based heuristic algorithm which is suitable for solving hard problems. The main contributions of the research are modifying WOA function for the problem and designing a new method for creating whales. In order to reduce the time of reaching to the reasonable solution an innovative whale generating method has been  designed.  The results show that average distance of each parcel from its relief center is 1541 meters and the standard deviation of 114

This research describes an OPTIMIZATION and rejuvenation of the heat treatment process for a nickel base superalloy grade GTD111 after long-term service. The aging heat treatment variables examined in this study included primary aging temperature, primary aging time, secondary aging temperature, and secondary aging time. The resulting materials were examined using Taguchi method design of experiments to determine the resulting material hardness test and observed with the hot tensile test, scanning electron microscopy, and energy dispersive X-ray spectroscopy. The experimental results showed what happens following OPTIMIZATION with the heat treatment parameters of a primary aging temperature of 1120 °C, primary aging time of 3 h, secondary aging temperature of 845 °C, and secondary aging time of 24 h. The material, after rejuvenation heat treatment via OPTIMIZATION with γ′ particle characteristics, had a coarse square shape, spherical shape of γ′, and fine γ′ precipitate distributed on the parent phase, which affects the mechanical properties of the material. fine γ′ precipitate distributed on parent phase, which affects the mechanical properties of the material.

The focus of many researchers in the operations research field has been on supply chain design problems during past decades. Previous works have widely investigated production-inventory planning, vehicle routing, and LOCATION-alLOCATION problems. This paper aims to consider these problems simultaneously and present a new integrated production planning-LOCATION-routing problem for a three-echelon supply chain, considering several real-world assumptions. The studied supply chain includes multiple production centers, distribution centers, and customers. The distribution centers use a set of non-homogeneous vehicles to deliver the products to the customers. Several features, such as regular and overtime production, production reliability, time-window constraints, and capacity constraints, are incorporated to provide a more realistic problem. The bi-objective model aims to determine the optimal LOCATION, alLOCATION, production, and routing decisions to optimize the total cost and servicing time objective functions. Concerning problem's complexity, the non-dominated sorting genetic algorithm-II (NSGA-II) is designed and implemented as a solution approach. The results reveal that this algorithm can solve the model in an acceptable time interval. In addition, the results demonstrate that the NSGA-II algorithm is reliable in finding solutions, and there is no significant difference between the average solution and the best solution of the algorithm in several runs.