Background: Decision making is inseparable from management and it is considered as base for decision making. Decision making has an important role in the strategic level. Thus, strategies, models and variety METHODs are established to help managers which decision making is one of them.Objective: There are different kinds of relations among the elements of decision making, particularly among criteria, for this purpose ANP METHOD has proposed for involving all relations between elements of the problem.METHOD: ANP METHOD is somehow sophisticated and time consuming that makes us to use soft wares to solve the problems even a simple problem that often is concerned to strategist that needs a rapid reaction and less patience. The reason that strategies in most cases prefer AHP more than ANP in strategic situation is simplicity of the ANP posses in achieving to result but AHP doesn’t consider the dependency of the criteria's which results to wasting opportunities and capital because of choosing inappropriate strategy.Results: The META-HEURISTIC proposed METHOD in this paper is named SIMANP that with it's simple mechanism, high accuracy, fast giving result, less time consuming and satiety to software solves the network problems accurate and easily.Conclusion: The SIMANP METHOD will endow strategists and strategy managers who need accuracy, simple and fast giving result with the advantages of ANP Compared with AHP.

It is important to maintain the vacuum in the space between the rotor and the body of a gas centrifuge machine during the gasification and enrichment process. For this reason, a molecular pump is connected to the upper part of the body. The pump has grooved that particles collide with as they move. Depending on the direction of the grooves, the particles are deflected and return to the rotor. One METHOD of analyzing the gas flow inside the molecular pump is by solving the flow equations using the Sickafus METHOD and his colleagues. This METHOD is useful because it reduces calculation time. In this article, four META-HEURISTIC algorithms were used to determine the optimal geometric and operational parameters of the molecular pump. The results indicate that among the META-HEURISTIC algorithms, optimization with the humpback whale algorithm (WOA) produces the highest compression ratio for the molecular pump. The average absolute deviation value of the logarithm of the pressure density ratio, when compared to values predicted by the upgraded Sickafus METHOD with optimal parameters, shows a very good agreement between the experimental METHOD and the upgraded Sickafus METHOD using the META-HEURISTIC WOA algorithm.

Parameters of META-HEURISTIC algorithms are very effective in their performance and are usually done experimentally, which is very time-consuming. In this research, a hybrid METHOD for selecting the optimal parameters of META-HEURISTIC algorithms is presented. The proposed METHOD is a combination of data envelopment analysis METHODs and response surface METHODology and is called DSM. In fact, this METHOD can be used to optimize multi-objective problems and its main advantage is to create and optimize one performance response procedure instead of optimizing multiple output response procedures. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the proposed DSM METHOD has been used to adjust the parameters of the cuckoo optimization algorithm to optimize the standard and experimental Aklay and Rastrigin functions. In the hybrid DSM METHOD, first, the efficiency value is calculated using data envelopment analysis for each set of META-HEURISTIC algorithm parameters, then the response procedure for performance is determined according to the META-HEURISTIC algorithm parameters using the response surface METHODology. Finally, by optimizing the efficiency surface, the optimal values of the cuckoo algorithm parameters are obtained. In order to validate, the results of the proposed METHOD have been compared with a similar METHOD. The results show better performance of the hybrid algorithm in terms of solution time, number of iterations, and accuracy of the optimization function compared to other similar METHODs.

In the science of operation research and decision theory, selection is the most important process.Selection is a process that studies multiple qualitative and quantitative criteria, related to the science of management, which are mostly incompatible with each other. The multi criteria selection of a renewable energy portfolio is one of the main issues considered in multi criteria literature. In the present study to form a portfolio of renewable energy, first, the KOHONEN neural network algorithm was used, and then each portfolio was evaluated using multi criteria decision-making METHODs.Further, through META HEURISTIC multi objective algorithms Pareto rank analysis was conducted and social acceptance of renewable energy production METHODs was assessed. Finally, the portfolio for studied energies was composed. The results indicated that Cuckoo Search Algorithm and Grey Relational Analysis are effective and efficient for the selection of optimal Pareto portfolio of renewable energy.

Network interdiction problems are a group of problems in which two actors face each other with conflicting goals. In these matters, the benefit of one actor damages the other actor. In the case of maximum capacity path interdiction, a defender in the role of leader applies his interdicting actions according to the available budget. At the next level several attackers, as followers observing the defender's interdiction action, optimize the maximum capacity path problem from the origin to the destination. Interdiction is attacking the network arcs to destroy them and reduce the capacity of the arc. In this research, first, a two-level binary mathematical programming model for the problem is described. Then, due to the complexity of solving two-level problems, a hybrid algorithm including a revised Dijkstra algorithm and a simulated annealing algorithm is proposed to solve the problem. The revised Dijkstra algorithm always finds an optimal solution to the maximum capacity problem. Therefore, the hybrid algorithm can find good solutions in the search space. Then, the efficiency of the proposed algorithm was evaluated up to the size of 100 nodes and 150 arcs, which shows the ability of the algorithm to solve problems in different sizes. Based on the conclusions, increasing the defender budget results in the objective function being improved to a certain extent. The coefficient of attackers in the objective function is inversely related to the quality of the attackers' path and increases or decreases the maximum capacity of the attackers' path.

Cross docking is a product-handling and distribution concept in which product items are sorted out and recognized based on customer demands and move directly from the receiving dock to shipping dock, without being held as inventory in the warehouse. Cross docking systems can decrease the storage and products retrieval costs in comparison with traditional warehouses by synchronizing the flows of inbound and outbound trucks. Generally, cross docking is a good strategy for companies that distribute large volume of products and/or serve a large number of stores. Cross docking facilities include four main functions of warehousing. These four major functions are receiving, storage, order picking, and shipping. Among these four functions, storage and order picking are typically the most costly. Storage function is costly because of inventory holding costs. On the other hand, order picking is expensive because it needs labour work. Cross docking minimizes the storage and order picking functions of a warehouse while it still allows receiving and shipping the product items. This paper proposes a novel META-HEURISTIC based METHOD for scheduling vehicles in cross docking problems with considering temporary storage. In other words, products can be stored in a temporary storage and wait for loading in an outbound truck. The objective of the proposed METHOD of this paper is to minimize makespan. Makespan in this study is deFined as the total operating time of the cross docking operation. The makespan covers the interval between the moment of unloading the first product from an inbound truck into receiving dock and the moment of loading the last product into shipping truck. In order to evaluate the performance of the proposed METHOD, different problems are provided and analyzed. Moreover, the proposed METHOD of this paper is compared to a well-known METHOD of literature. The computation results reveal that the proposed METHOD of this paper outperforms the compared METHOD.

The purpose of this article is to review the literature on the topic of deterministic vehicle routing problem (VRP) and to give a review on the exact and approximate solution techniques. More specifically the approximate (META-HEURISTIC) solution techniques are classified into: tabu search, simulated annealing, genetic algorithm, evolutionary algorithm, hybrid algorithm, and Ant Colony Optimization. Each of these solution techniques is briefly discussed and a case study from the literature is presented.

This paper presents a novel META-HEURISTIC METHOD for solving an extended Markowitz Mean–Variance portfolio selection model. The extended model considers Value-at-Risk (VaR) as risk measure instead of Variance. Depending on the METHOD of VaR calculation its minimizing METHODology differs. if we use Historical Simulation which is applied in this paper then the curve would be nonconvex.On the other hand the Mean-VaR model here includes three sets of constraints: bounds on holdings, cardinality and minimum return which cause a Mixed Integer Quadratic Programming Problem. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio’s equal to a predefined number.Because of above mentioned reasons, in this paper, we propose a new META- HEURISTIC approach based on combined Ant Colony Optimization (ACO) METHOD and Genetic Algorithm (GA). The computational results show that the proposed Hybrid Algorithm has the ability to optimized Mean-VaR portfolio for small portfolio.

Bus urban public transport is one of the most considered systems and the buses have received more attention because of their special privileges compared to other systems. In most cities, the bus system is responsible due to the lack of advanced transportation systems such as subways, trams, and light-rail. Due to the positive attributes and advantages of this system, the improvement of their capabilities needs planning and consulting with experts. In this paper, a linear programming model for optimizing the public bus transportation system is proposed to minimize the total cost of setting up the lines, maintenance costs and fuel consumption as well as minimizing the bus arrival times in order to increase the welfare of the passengers. The proposed model is simulated in GAMS software and the sensitivity analysis is performed over the parameters. Due to the time complexity of the proposed model, a hybrid META-HEURISTIC METHOD based on the Ant Colony Optimization (ACO) and Particle Swarm Intelligence (PSO) name PS-ACO is also developed and simulated in Matlab for solving the proposed model. The computational results show that the fuel cost and distance between stations have the most effect on operational cost, and the time distance between the stations is the most effective parameter on the passenger's welfare. Besides, the number of the stations highly affects the model complexity and solution time.