In this research, different strategies were developed to establish an agricultural service center in terms of the number and capacity of services provided. A separate location model was developed to locate agricultural service centers for each solution. For this purpose, the location-allocation model was used to develop the location models and the Branch and bound Algorithm to solve the models. The main purpose of these models was to minimize the cost of deploying agricultural service centers (including start-up and shipping costs to the point of service). In the model solved in this study, measures such as cost of establishment, cost of service start-up, distance between service center to customers, transportation costs and number of trips required to meet each customer's demand were considered. In this study, in order to show the practical capabilities of developed methods and models, a case study was conducted in Razan city, in Hamadan Province. After implementation of the location-allocation model, the optimal locations to establish agricultural service centers in all villages of the area were determined separately. The results of this study can be used by national and regional managers and planners in locating agricultural service and planning centers to provide services to agricultural users.

In this paper, the problem of scheduling the job shop production is considered, which, while being applicable as one of the complex issues in the field of combinatorial optimization, has been raised and this has caused the researchers to pay more attention to this problem. In this problem, given the existence of a number of workshops, the number of jobs should be processed in the workshop with a known production path that is already known. In order to solve this problem, in this paper, the problem of production scheduling is firstly modeled as a mixed integer programming. Then, due to the limitations of the exact solving using this model, the surface cutting method is proposed to create inequalities this is valid for the problem in the framework of Branching and cutting Algorithm. The aim of adding this set of inequalities is to create a lower bound, which, while the feasible solutions are not eliminated by them, accelerates the solution in the framework of Branch & cut Algorithm. To solve the problem optimally, the results show that the lower bound created by the re-formulated model is stronger than the lower bound created by the original relaxed model.

Transportation Discrete Network Design Problem (TDNDP) aims at choosing a subset of proposed projects to minimize the users’ total travel time with respect to budget constraint. Because TDNDP is a hard combinatorial problem, recent research has widely addressed heuristic approaches and ignored the exact solution. This paper is going to explore how application of parallel computation can affect the performance of an exact Algorithm in TDNDP. First, we show that the Branch-and-Bound (B&B) Algorithm proposed by LeBlanc is well adapted to a parallel design with synchronized Master-Slave (MS) paradigm. Then we develop a parallel B&B Algorithm and implement it with two search strategies of Depth-First-Search (DFS) and Best-First-Search (BFS). Detailed results over up to 16 processing cores are reported and discussed in an illustrative example of the Chicago Sketch network. The results suggest an almost linear speedup for both strategies which slightly drops as more processing cores are added. When using 16 processing cores the speedup values of 11.80 and 12.20 are achieved for DFS and BFS strategies respectively. Furthermore, the BFS strategy reveals a very fast parallel performance by finding the optimal solution via the minimum computational effort.

Uncertainty plays a signicant role in modeling and optimization of real world systems. Among uncertain approaches, fuzziness describes impreciseness while for ambiguity another denition is required. Vagueness is a probabilistic model of uncertainty being helpful to include ambiguity into modeling different processes especially in industrial systems. In this paper, a vague set based on distance is used to model a ow-shop scheduling problem being an important problem in assembly production systems. The vagueness being used as octagon numbers are employed to represent vague processes for the manufacturing system. As a modeling effort, first a ow-shop scheduling problem is handled with vagueness. Then, for solving and analyzing the proposed vague ow-shop scheduling model, a modified Branch and Bound Algorithm is proposed. As an implementation, an example is used to explain the performance and to analyze the sensitivity of the proposed vague approach. The validity of the proposed model and modified Algorithm is demonstrated through a robust ranking technique. The outputs help the decision makers to counteract the vagueness and handle operational decisions in ow-shop scheduling problems within dynamic environments.

In 2005 Avella and Vasil'Ev [2] presented an e, cient cutting plane Algorithm for solving an integer binary programming formulation of the university course timetabling problem (UCTP). Here, we present a new and e, cient adjustment of the Branch and price Algorithm for solving the same formulation of UCTP. In every iteration of the Branch and price Algorithm, the column generation Algorithm is used for solving the linear programming relaxation. For the , rst time, in this paper the set packing constraints of the UCTP formulation are chosen as the spe-cially structured constraints of the column generation Algorithm. Then, a new e, cient two phase heuristic method is presented for solving the set packing problem. The resulting adjusted column generation is used within a Branch and price Algorithm and a comparison is performed with the cutting plane Algorithm presented by Avella and Vasil'Ev. The nu-merical results show that the computing time of the presented Branch and price Algorithm is always less than that of Branch and cut algo-rithm. Moreover, the number of subproblems and master problems of the presented Branch and price Algorithm depends on the structure of the problem. Finally, for all test instances the number of variables is very large that justi, es the use of the Branch and price Algorithm for solving the problem.

Nowadays, we are witnessing financial markets becoming more competitive, and banks are facing many challenges to attract more deposits from depositors and increase their fee income. Meanwhile, many banks use performance-based incentive plans to encourage their employees to achieve their short-term goals. In the meantime, fairness in the payment of bonuses is one of the important challenges of banks, because not paying attention to this issue can become a factor that destroys the motivation among employees and prevents the bank from achieving its short-term and mid-term goals. This article is trying to tackle the problem of optimizing the coefficients of Branch performance evaluation indicators based on their business environment in one of the state banks of Iran. In this article, a two-objective genetic Algorithm is proposed to solve the problem. This article is comprised of four main sections. The first section is dedicated to the problem definition which is what is our meaning of optimizing the importance coefficients of Branches based on the business environment. The second section is about our proposed solution for the defined problem. In the third section, we are comparing the performance of the proposed two-objective genetic Algorithm on the defined problem with the performance of four well-known multi-objective Algorithms including NSGAII, SPEAII, PESAII, and MOEA/D. And finally, the set of ZDT problems which is a standard set of multi-objective problems is taken into account for evaluating the general performance of the proposed Algorithm comparing four well-known multi-objective Algorithms. Our proposed solution for solving the problem of optimizing Branch performance coefficients includes two main steps. First, identifying the business environment of the Branches and second, optimizing the coefficients with the proposed two-objective genetic Algorithm. In the first step, the k-means clustering Algorithm is applied to cluster Branches with similar business environments. In the second step, to optimize the coefficients, it is necessary to specify the fitness functions. The defined problem is a two-objective problem, the first objective is to minimize the deviation of the real performance of the Branches from the expected performance of them, and the second objective is to minimize the deviation of the coefficients from the coefficients determined by the experts. To solve this two-objective problem, a two-objective genetic Algorithm is proposed. In this article, two approaches are adopted to compare the proposed solution performance. In the first stage, the results of applying the proposed two-objective genetic Algorithm have been compared with the results of applying four well-known multi-objective genetic Algorithms on the problem of optimizing the coefficients. The results of this comparison show that the proposed Algorithm has outperformed the other compared methods based on the S indicator and run time, and it is also ranked second after the NSGAII Algorithm in terms of the HV indicator. Finally, for evaluating the performance of the proposed Algorithm with other well-known methods, the set of ZDT problems including ZDT1, ZDT2, ZDT3, ZDT4, and ZDT6 has also been taken into consideration. At this stage, the performance of the proposed Algorithm has been compared with the four mentioned Algorithms based on four key indicators, including GD, S, H, and run time. The results show, the proposed Algorithm has outperformed significantly in terms of run time in all five ZDT problems. In terms of GD indicator, the performance of our proposed Algorithm is located in the first or second rank among all considered Algorithms. In addition, in terms of S and H indicators in many cases, the proposed Algorithm outperformed the other well-known Algorithms.

Introduction: In this research, a part of the requirements for the establishment of a network of consultancy, agricultural engineering and technical services in the agricultural sector, which is related to the location of these centers, has been reviewed. The location of these centers has been done through the determination of the field of operation and the appropriate establishment of consulting, engineering, and agricultural consulting companies based on regional capacities and taking into account the distance between the types of customers of such centers. Materials and Methods: In the issue of locating service centers three main types of customer can be classified. First-class customers, which have the largest number among different types of customers, are farms and units that produce agricultural products. Each point of demand for these categories of customers may require different types of services at different times. Due to the large number and dispersion, these category of customers are considered as a focal point for ease of modeling in rural areas where they are located. Also, due to various reasons, including access to various facilities, security, traffic congestion and etc., the nominations for deployment of service centers are also considered in the same rural areas. In order to transport agricultural products from the place of production, the current location is considered to be the distance from the manufacturer's place, and the destination of the product is not studied in this issue. Second and third-type customers are demanding access to services at their own place. These types of customers may exist in some areas and agricultural supply chains. These two groups of customers include refineries, warehouses and silos mainly operating in the post-harvest of agricultural production. To meet the demand for each of the different demand points of different types of customers, the number of different trips from service centers to customer premises or vice versa is required. Each service center does not offer the same type of service to its customers. A total of 127 service packages are available for provision at a service center. Results and Discussion: The main basis for choosing the optimal location for covering models is the placement of demand points in the defined coverage radius for the candidate points. Different radius were tested to find the perfect coverage radius in each of the studied villages. For this purpose, a radius of five to 160 kilometers was examined. In some coverage radius, not only does the optimal location not change, but the number of served points is also fixed. The location of different types of customers is different, so that the first type of customers are fully located in the village, but second and third type customers are widespread in the Hamedan province. Conclusions: To conclude, it is necessary to consider the demand of customers located in the further distances of the service center due to the nature of the agricultural service, which requires inevitable traffic over long distances, when adjusting the operational plans of the agricultural service centers. To provide sufficient justification for the distance, though within the radius of coverage. Thus, the results of this research show that if all service centers cover 130 kilometers of radius, the largest number of customers will be covered. It should be noted that for the full coverage of all customers, the coverage radius of the service centers varies, but with the same radius, the 130 km radius is the largest coverage of the agricultural service centers in the Razan city.

Background and Objective: The aim of this study was to measure the shear Bond strength of composite resin to human dentin surface Bonded with three different Bonding systems.Subjects and Methods: The crown surface of 66 extracted human third molar teeth were abraded with trimer in a wet environment until the dentin surface was exposed, and they were polished with silicon carbide paper 400 and 600 grit. Then, all the smooth smear layer samples were randomly classified into three equal groups. We used Bonding according to the instruction of the manufacturer (the first group Single Bond, the second group BC Plus and third group Charm Bond).Then, the composite resin was Bonded to the tooth surface by plastic tube (diameter: 1.8 mm, height: 5mm).Finally, shear Bond strength was determined by universal testing machine (LMT 100).Results: The variance analysis (ANOYA) P value=0.000 revealed the mean of shear Bond strength is different between three groups. The mean shear Bond far first, second and third groups are 15.25 MP, 6.84 MP, 8.59 MP, respectively. The multiple comparison with THKEY test showed that there is different significance between group one and the other two groups which did not differ significantly.Conclusion: All in all, we found out that greatest shear Bond strength to dentin is the American Single Bond, which is better than Korean product (BC Plus and charm Bond).