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  We consider the maximal covering location-allocation problem with multiple servers. The objective is to maximize the population covered, subject to constraints on the number of service centers, total number of servers in all centers, and the average waiting time at each center. Each center operates as an M/M/k queuing system with variable number of servers. The total costs of establishing centers and locating servers should not exceed a predetermined amount. We present a mathematical model for the problem, and propose a heuristic solution procedure with two local search algorithms for improving the solutions. Finally, some computational results are presented.

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An availability model is developed to optimize the availability of a series repairable system with multiple k-out-of-n subsystems in this paper. There are two types of decision variables that determine the system designer’s decision to allocate the number of repairmen and to allocate the number of redundant components in each subsystem in the presence of weight, volume and cost constraints. As per the nonlinear structure of the objective in the model, the model is located into the nonlinear programming category. A classical Particle Swarm Optimization (PSO) algorithm is proposed to solve seven various instances of the model. The aim of this study is to illustrate the model and to propose an applicable algorithm for the problem. The efficiency of the proposed PSO is illustrated by comparison with Simulated Annealing (SA) method.

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A genetic algorithm is proposed to optimize a tree-structured power distribution network considering optimal cable sizing. For minimizing the total cost of the network, a mixed-integer programming model is presented determining the optimal sizes of cables with minimized location-allocation cost. For designing the distribution lines in a power network, the primary factors must be considered as maximum allowable electrical flow in cables, permitted length of cables, maximum permitted voltage drops, and balance of load. The relationship between rates of electric current and cable sizes with consideration of constraints such as voltage drops and length are our essential data. To create a network with a minimum number of arcs and no closed loop such that all the nodes are covered, a minimum spanning tree technique is utilized. Here, we solve the problem using a genetic optimization algorithm and apply the offered approach to a real problem. By comparing the two extracted results from the proposed approach and an exact method, effectiveness of the genetic algorithm for optimization of power distribution network is shown. To demonstrate the validity of the offered model, a case study in Tehran power distribution company in Iran is made.

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     One of the most reliable indicators of the evaluation of the same units is the use of mathematical programming based method called data envelopment analysis (DEA). DEA measures the efficiency score of a set of homogeneous decision making units (DMUs) based on observed input and output. The DEA method has been added to the literature by integrating Farrell's method in such a way that each evaluation unit has multiple inputs and multiple outputs. With the advancement and evolution of this approach, DEAis now one of the active areas of research in measuring performance and has been dramatically welcomed by world researchers. Charnes, Cooper, and Rhodes (CCR) [1] first proposed DEA method to evaluate the relative efficiency for not-for-profit organizations. So far, many studies and researches have been carried out in various associations and universities around the world about DEA and its applications. The simplicity of understanding and implementing the DEA method, along with its high precision and wide application in various political, cultural, social and economic fields has led many researchers to use this method to achieve their goals. So far, more than 50,000 articles, books, theses and more have been published on DEA theories and applications, calculations and issues.

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Resource allocation is a problem that commonly appears in organization with a centralized decision making (CDM), who controls the units. The aim of central decision making is to allocate resources in such a way that the organization get the most benefit. Some Data Envelopment Analysis (DEA) researchers presented DEA-based resource allocation models by paying attention to energy saving and environmental pollution reduction. In this paper, we expanded a resource allocation model for 25 branches of an Iranian Tejarat bank, so that determined how much decision making (DM) can save on energy and manpower hours, so that undesirable outputs like non-performing loans are significantly reduced in a way that achieve the minimum reduction of desirable outputs while unchanged the performance of each unit after re-allocation. The result of the implementation of the model shows that in total with a 10% and 23% reduction in staff and costs respectively can result in the 0.09% reduction of deposits and 56% of non-performing loans.

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Nowadays, airline industries should overcome different barriers regarding the fierce competition and changing consumer behavior. Thus, they attempt to focus on joint decision making which enables them to set pricing and capacity allocation to maximize their profits. In this research, we develop a model to optimize pricing and capacity allocation in a duopoly of single-flight leg for two competitive airlines. The problem considers actual assumptions about flexible partitions in flight’s cabins and additionally demand uncertainty. There is a flexible partitioning of business and economy cabins and demand is assumed price-dependent with additive uncertainty. The capacity and pricing decisions are simultaneously determined through indirect channels. Moreover, a numerical study is developed to investigate how market components and competition conditions change pricing, capacity, and profit levels. The results show that increasing market volume like decreasing price sensitivity provides higher levels of price and profits. Moreover, intensified competition never leads to higher prices. Thus, a competitive network of airlines provides better impact on market mechanism to achieve competitive prices for both economy and business classes.

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Nowadays, due to population increase and expansion of the transportation networks, the monitoring of this network, control, and prevention of accidents and crimes are very important. The main tasks of traffic patrols, like other emergency-security facilities, are the monitoring and prevention of crime as well as handling and fining in the case of committing crimes. Traffic patrols should be present in the scene and also in high traffic congestion, to reduce accidents and crimes. This paper presents a bi-objective and stochastic optimization model to design an emergency-security system. This network includes traffic patrol vehicles and manpower in patrol vehicles. The objective of the proposed model is to maximize the number of vehicles passing through patrols and minimize the costs according to different scenarios. To solve the model, the epsilon-constraint method is used which simultaneously determines the location of the patrols, allocation of demand points to patrols, and determining the number of existing manpower in patrols. To evaluate and analyze the proposed model, a numerical example is used.

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Hubs are facilities that can decrease the cost of many-to-many distribution systems by acting as an interconnector between the demand and supply nodes. This type of facility can reduce the number of direct links needed in a logistics network. Hub location problems (HLP) have been discussed by many authors for more than four decades, and different approaches have been developed for modeling and solving this problem. We propose a fuzzy type I and II programming approach for a new model presented in the literature, i.e., the single allocation ordered median problem. The level of flow among the nodes will be considered as a fuzzy parameter. In the fuzzy type I approach, a linear programming problem with fuzzy parameters is used, while for the fuzzy type II approach, the rules of interval arithmetic are developed to simplify the problem to the fuzzy type I case. Finally, we apply our method on Kalleh Dairy Co. data of transportation as a case study and compare crisp and fuzzy situations. We show that the results of the fuzzy approach could be 2% better than the crisp approach and also discuss the pros and cons of fuzzy type I and type II approaches.

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Nowadays, the capability of cloud management suppliers is one of the important advantages for suppliers that can improve the performance and flexibility and reduce costs in companies through easy access to resources. Also, the environmental impacts of suppliers are a significant issue in today’s industrialization and globalization world. This paper analyzes these subjects by fuzzy multi-objective scenario-based stochastic model. Its objective functions are minimizing the total cost, environmental impacts of suppliers, and maximizing the capability of cloud management of suppliers. Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-objective Simulated Annealing meta-heuristic (MOSA) are developed to settle this problem. Five computational experiments analyze the performance of the solution algorithms. The results illustrate that the NSGA-II algorithm provides better solutions than the MOSA algorithm for the presented model.
 

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Enhancing the efficacy and productivity of transportation system has been on the most common issues in recent decades, noteworthy to the industrial managers and expert so that the products are delivered to the clients at right time and the least costs. Therefore, there are two important issues; one is to create hub as the as intermediaries for streaming from multiple origins to multiple destinations and also responding to the tours of every hub at the proper time. The other is a route where the vehicles should pay at time window of each destination node. On the other hand, these problems may cause cost differences between hub and interruption of their balance. Accordingly, this paper presents a model dealing with cost balancing among the vehicles as well as reducing the total cost of the system. Given the multi-objective and NP-Hard nature of the issue, a multi-objective imperialist competitive algorithm (MOICA) is suggested to provide Pareto solutions. The provided solutions are at small, average and large scales are compared with the solutions provided by Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm. Then, its performance is determined using the index for evaluating the algorithm performance efficacy to solve the problem at large dimensions.