mahdi alinaghian

Recent facility location allocation problems are engaged with social, environmental and many other aspects, besides cost objectives.Obtaining a sustainable solution for such problems requires development of new mathematical modeling and optimization algorithms. In this paper, an uncapacitated dynamic facility location-network design problem with random budget constraints is considered. Social issues such as public satisfaction as a function of construction time, number of missed jobs incurred in the regions under study and environmental considerations are incorporated in the model.Sincethe proposed model is expected to be capable of dealing with probabilistic network design, a new chance constraint formulation is proposed and manipulated to increase the applicability of the model in uncertain decisions. Moreover, the proposed method enables decision makers to determine the completion rate of projects through a time horizon while this notion is not achievable by applying other methods in the literature. The optimization of the proposed model is performed using anovel bi-section procedure in which a heuristic and a Simulated Annealing (SA) method are applied interactively. The efficiency of the proposed method is verified through a real world application of establishing a set of health care centers and the connecting links in MeshginShahr,Iran. The results of case study showed that all considered sustainable objectives come to a steady status in the fifth year. Also according to geographical data, the results about creating links are regional and the health centers have dispersed geographically in order to serve the demands of the whole under study region.

Transportation sector generates a considerable part of each nation's gross domestic product and considered among the largest consumers of oil products in the world. This paper proposes a heuristic method for the vehicle routing problem with hard time windows while incorporating the costs of fuel, driver, and vehicle. The proposed heuristic uses a novel speed optimization algorithm to reach its objectives. Performance of the proposed algorithm is validated by comparing its results with the results of the exact method and differential evaluation algorithm for small-scale problems. For large-scale problems, the results of the proposed algorithm are compared with those obtained from the differential evaluation algorithm. Overall, results indicate the good performance of the proposed heuristic algorithm.

In Inventory Routing Problem (IRP), which is one of the most important logistics problems, decisions regarding the distribution and inventory management must be made by an integrated managerial approach. In this type of problems, decision maker usually has the option to use several types of vehicles to form a fleet with appropriate size and composition in order to minimize both inventory and transportation costs. Considering the amount of pollution produced, in this problem, may reduce pollution . This paper proposes a new model for green inventory routing problem with heterogeneous fleet. The objectives of the proposed model are to minimize the emissions, the fleet, routing and inventory costs. Due to the NP-hard nature of the assessed problem, a meta-heuristic algorithm based on Quantum Evolutionary Algorithm (QEA) is proposed. To evaluate the performance of the proposed algorithm, its results are compared with the results of exact method and basic Algorithm. The results of these comparisons indicate the good performance of the proposed algorithm.

In recent years, great efforts have been made to reduce greenhouse gas emissions by vehicles. Petroleum products produces greenhouse gas emissions, therefore reducing the use of these products can make a major contribution to reducing pollution. The Fleet Size and Mix Vehicle Routing Problem is one of the most widely used routing branches. In this problem, there are vehicle with different capacities and there is the possibility of choosing vehicles of different types. In this paper, Fleet Size and Mix Vehicle Routing Problem is death considering the reduction of fuel consumption. Since this problem is NP-hard, three novel heuristic methods entitled GROS-I,GROS-II, GGT are presented for the problem. In order to evaluate the proposed heuristics a number of small, medium and large problems are solved. The results show that proposed algorithms have good performance.

Today, field of production and service is faced with competition among the supply chains by changing the competition pattern of the independent companies. Most of the small and medium businesses (SMEs) still use traditional viewpoint for supply, production and distribution planning. It means, each of these SMEs plan their tasks independently, this will increase the total cost of the supply chain in many cases. In this study, a robust model of inventory-locating supply chain is proposed in three-level with uncertain demand. The model has been considered in single-period, multi-product state along with some transportation models with three levels of producers – distributors- retailers in certain and robust mode. Considering some transportation models along with robusting the model and the possibility of sending goods directly from the factory to the retailer is one of the innovations of this study. The objectives of the proposed model are to minimize the total cost of the three-leveled supply chain and to find the amount of safety stock. The certain model is solved by GAMS and the robust model is also solved by GAMS in single-objective mode and then transferred to the augmented ε-constraint method. The results have been discussed after solving the model.

One of the most important measures needed to be done in times of crisis is to optimize the allocation and distribution of resources among individuals. Time is a critical factor effective to increase the number of people rescued by the relief activities.In this paper, we present a relief vehicle routing model in the affected area which uses covering tour approach to reduce total response time. Also, it is too difficult to determine the real amount of demands for essential commodities, e.g. first-aids, drinking water, etc. Therefore, we consider a fuzzy chance constrained programming model based on the fuzzy credibility theory. In order to validate the model, several numerical examples are solved using branch and bound A metaheuristic algorithm based on harmony search algorithm incorporated with stochastic simulation is developed and proposed to solve the problem. The results of the proposed algorithm compared with the results of the exact method shows 1% error for the algorithm. This indicates the efficiency of the proposed algorithm. To evaluate the proposed algorithm on a large scale, the results of the algorithm, has been compared with the results of GRASP method.The experimental results have shown that the proposed algorithms have appropriate performance in a reasonable time.

An integrative approach of Data Envelopment Analysis (DEA) and Malmquist Productivity indicator for performance analysis of projects in Subsea R&D Center (SRDC) is presented. It is noticeable that، an output based model with constant efficiency was used for data analysis. Furthermore، since the DEA approach is merely used for homogeneous units، it was assumed that all the projects according to objectives in the project based organization، i. e. time، cost and acceptable quality were homogenised. However، results of this research are limited to an exact time and are related to special projects for the associated organization. Findings imply feasibility of using this model for measuring productivity improvement of projects in project based organizations. The innovation of this research may consider to integrative of DEA and Malmquist indicatorfor projects analysis and performance sensitivity analysisas well.