ahmad sadegheih

Insurance is a financial service designed to manage risks. Insurance always helps the country's growth by preventing uncertain losses and providing security for economic growth. In developed and civilized societies, the insurance industry is known as one of the main economic institutions and the most important support institution for other economic institutions, organizations, companies and families. In Iran's twenty-year vision document, development is mentioned as one of the most important goals, and the insurance industry, with its support, plays an important and key role in its realization. One of the most important measures that can be taken to identify complications and improve the performance of insurance companies; Continuous performance evaluation of different parts of this industry. Among the various departments of insurance companies, the insurance sales network is in close contact with the customer more than the other departments and generates income for the insurance company by selling insurance products and receiving the relevant premiums. The insurance sales network with its optimal performance can create a great competitive advantage for the insurance company. Among the different parts of the sales network, insurance agencies are of double importance due to their large number and geographical spread throughout the country. Therefore, it is very important to evaluate the performance and rating of insurance agencies in order to know the quality of their performance. Evaluating the performance of insurance agencies leads to continuous improvement of their performance. With the help of performance evaluation results, insurance agencies can increase their efficiency and find better performance, and as a result, their sales increase and customer satisfaction increases. One of the most useful methods in evaluating the performance and rating of the insurance industry is the DEA method. The basic models of DEA, despite having many advantages, also have disadvantages. The main defects of these methods are not paying attention to internal processes, not taking into account expert opinions and not fully ranking the units. The integrated model of two-stage DEA and BWM considers the internal process and experts' opinion in the ranking process, but this model is not always able to completely rank the units. By adding an ideal virtual unit to the integrated model of two-stage DEA and BWM, this research has simultaneously fixed the three aforementioned problems and developed the model. Adding the ideal virtual unit to the integrated model makes the ranking of the units complete and provides better results. In a case study, 36 representatives of selected insurance companies in Yazd province were evaluated and ranked using the developed model. In order to validate the model with the SAW method, the ranking of agencies was also done. The results of the correlation coefficient of the ranking of SAW model with other models presented in this research showed that the ranking results with the integrated two-stage model with the virtual ideal unit have a higher correlation with the SAW model than the other models of this research. The value of this correlation is 94%. In the end, based on the efficiency rating of the first and second stages, management strategies to improve the performance of each of the insurance company's agencies were stated.

This study focuses on the development of a stochastic bi-objective hub location-routing model for a railway rapid transit network design problem. Due to the use of railway rapid transit systems in the hub-level sub-network (i.e., the network among the hub nodes) and the spoke-level sub-network (i.e., the network that connect spoke nodes to each other and to hub nodes), the decisions to make concern the location of hub nodes, spoke nodes, hub edges and spoke edges, and the determination of hub and spoke lines, the percentage of satisfied demands, and the way of routing the demands, simultaneously. Uncertainty is assumed for demands represented by a finite set of scenarios. The problem is formulated through a two-stage stochastic modeling framework. The aim is to maximize the total expected profit and to minimize the total expected service time. The performance of the model is evaluated through computational tests using the well-known AP dataset. The computational results confirm the importance of considering the stochastic model and the conflicting profit and time objectives for the given problem. Some managerial insights are also provided through the analysis of the resulting networks under various parameter settings and the investigation of the effect of these settings on the characteristics of the obtained solutions and the interactions among the different aspects of the studied complex decision problem

Compared to coal and other fossil fuels, renewable energy (RE) sources emit significantly less carbon dioxide (CO2). In this sense, switching to such sources brings many positive effects to the environment through mitigating climate change, so the terms green energy and clean energy, have been derived from these constructive environmental impacts. Given the utmost importance of RE development, the primary objective of this study was to identify and prioritize the effective RE development strategies in Mazandaran Province, Iran, using different methods, including the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, along with other decision-making techniques. Recruiting a team of 11 industrial and academic experts, the strategies to implement in this region were developed in line with the RE development plans. For this purpose, the Multi-Criteria Decision-Making (MCDM) methodologies were utilized within the gray fuzzy environment to manage the existing uncertainties. The Gray-Additive Ratio Assessment System (Gray ARAS) was further applied to rank the main factors at each level. According to the SWOT analysis and the Stepwise Weight Assessment Ratio Analysis (SWARA) outcomes, among the major factors shaping RE development in Mazandaran Province, Iran, the economic criterion, with the final weight of 0.24, was ranked first; and then the geographical and environmental criteria, having the final weights of 0.23 and 0.19, were put in the second and third places, respectively. In this regard, appropriate location, with the final weight of 0.226, was ranked first; and subsequently pollution reduction and energy production costs, receiving the final weights of 0.103 and 0.094, were the second and third sub-criteria, respectively. As a final point, the validation results based on the Gray-Weighted Aggregated Sum Product Assessment (Gray-WASPAS) and ranking obtained through the Gray-ARAS were confirmed.

Due to the importance of the health field, the problem of determining the shift scheduling of care providers has been addressed in many studies, and various methods have been proposed to solve it. Considering different skills and contracts for care providers is one of the essential issues in this field. Given the uncertainty in patients' demands, it is a crucial issue as to how to assign care providers to different shifts. One area facing this uncertainty is the provision of services to cancer patients. This study develops a stochastic programming model to account for patient demand uncertainty by considering different skills and contracts for care providers. In the first step, care providers are assigned to work shifts, then, in the second step, the required overtime hours are determined. The sample average approximation method is presented to determine an optimal schedule by minimizing care providers' regular and overtime costs with different contracts and skills. Then, the appropriate sample size is 100, determined based on the Monte Carlo and Latin Hypercube methods. In the following, the lower and upper bounds of the optimal solution are calculated. As the numerical results of the study show, the convergence of the lower and upper bounds of the optimal solution is obtained from the Latin Hypercube method. The best solution is equal to 189247.3 dollars and is achieved with a difference of 0.143% between the upper bound and lower bounds of the optimal solution. The Monte Carlo simulation method is used to validate the care provider program in the next stage. As shown, in the worst case, the value of the objective function is equal to 197480 dollars.

Biomass supply chain network design decisions are the most important part of the strategic level of supply chain management decisions, which include determining the location of facilities, their number and capacity, their allocation to different resources and markets, and the integration flow among facilities. An appropriate design has a significant influence on flexibility, efficiency and consequently the performance of the biomass supply chain. In this paper, a mathematical model with a robust optimization approach is presented to design a resilient and sustainable biomass supply chain under uncertainty in bioenergy demand and disruption in bioenergy refinery. By determining resilience factors and sustainability indicators, the relationship between resilience factors was determined and then the resilience factors were prioritized using fuzzy TOPSIS. Resilience factors with high priority are considered in the mathematical model. The first objective function considers profit maximization by considering all sustainability costs and the penalty of shortage or surplus of bioenergy demand and minimizing resilience factors . In addition, Robust method is proposed to overcome the uncertainty in bioenergy demand and the results of model solving with GAMS software are presented to show the model capability and sensitivity analysis of basic parameters. One of the innovations of this paper is to provide a way to measure resiliency based on the residual capacity after the disruption compared to before the disruption, which is presented in the first constraint. Finally, the parameters in the mathematical model are tested using a case study in the Organization of Renewable Energy and Energy Efficiency through numerical, feasibility and applicability of the proposed research approach. The obtained results show the valuable efficiency of the proposed model in increasing the biomass supply chain performance.</em>

the main problem in the earthquake crisis is to provide relief to the final victims with proper accuracy and speed. In simple logistics models, due to the lack of social, cultural and security dimensions, large volumes of goods and services that have entered the earthquake area do not reach the real victims. In this research, a humanitarian aid model is introduced using a new approach (using dynamic systems techniques). It should be noted that the advantage of using this method in relief logistics modeling is that we can include limitations such as the impact of security and social problems in the final model and lead the model to become more realistic. Then the model is created by creating feedback loops and finally different scenarios are created and investigated .In the proposed model of this researchThe results showed that the simultaneous use of risk reduction strategies before, during and after the disaster and the improvement of security and physical protection values create an optimal logistics situation.

In this research, spare parts logistics in the aviation industry has been investigated. Since each aircraft consists of several components, the availability, ordering, and delivery of them are important during aircraft repair and maintenance operations and delays in supplying component spare parts will have a direct impact on the delivery of aircraft. This paper aims to optimize a supply of component spare parts using a programming model considering minimize the holding and purchasing costs. Moreover, two critical constraints including the purchase budget and repair capacity which are rarely mentioned in the previous papers have been noted in this research. FARSCO aviation maintenance & overhaul center as the biggest dedicated MRO center in the Middle East has been considered in this paper as the case study. Based on its capabilities, FARSCO provides services to all airlines and 90% of domestic airlines send their aircraft to this center for performing heavy checks and maintenance. The proposed model was verified and solved by using CPLEX solver and is constructed for the case by considering the numerical data. The results are obtained in two average demand and pessimistic modes (worst case scenario). Finally, a sensitivity analysis of the model is carried out to investigate the applicability of the problem. Results demonstrate the optimal number of maintenance jobs that can be completed to deliver at each period, as well as the order quantity of spare parts and the shortages of spare parts which are important for managers to deliver aircraft to the customers on time.

The health insurance system can play an effective role to control health expenditures. The purpose of this study is to provide a model for estimating the physician visit tariffs. To achieve this goal, a hybrid model was used. fuzzy logic is the most appropriate tool for controlling systems and deriving rules for the relationship between inputs and outputs. So, the output of the data mining techniques enter the fuzzy logic as an input variable. The data were collected from the Health Insurance Organization of Iran in two sections including the physicians' costs and physicians' deductions. Owing to the techniques used in this model, NN had the least error, as compared to other data mining techniques (0.0034 and 0.0013, respectively). After defining the variables, membership functions and fuzzy logic rules, the accuracy of the whole control model was confirmed by random data. This research has dealt with the domains of health insurance , their connections and defining effective variables better and more extensively than the other studies in the field.

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.

Today, due to the intelligent nature of each agent, agent-based simulation has become an effective tool for predicting many complex systems between independent agents. These complex systems exhibit behaviors that cannot be inferred from the behavior of the components alone, and each experience of the system may lead to different results.In this study, the Agricultural Supply Chain (ASC) is examined as one of these systems in which agents try to make the best decisions to maximize their benefits through learning from the environment. Agents of this study include farmers, wholesalers, and sellers who independently seek to achieve their individual goals in competing with other agents. The price of crops is determined in a competitive bidding price mechanism. Each farmer can allocate his resources to cultivate a particular crop based on his own and other neighboring farmers experience. They can also become a contract farmer with the nearest wholesaler. Wholesalers decide on a similar mechanism for their contract operation. Eventually, sellers try to meet their demand at the lowest cost. The statistical analysis results showed that as the attractiveness of conventional agriculture in the supply chain decreases, they gradually lose their financial resources and go bankrupt in price fluctuations due to the impact of uncertainties in the market and the environment. These results also showed that the creation of supportive prices and the effects of behavioral and social patterns of agents play an important role in price stability and control of fluctuations in ASC.

The Aircraft Maintenance, Repair, and Overhaul (MRO) has always played a key role in the aviation industry which is responsible for restoring aircraft to flight conditions based on a set of technical instructions and implementation of repair processes. According to researches, the most important reasons why airlines choose MRO centers to provide services are quality, turnaround time (TAT), and cost, respectively. In this research paper, the application of lean, its principles and techniques, and in particular the study of its compatibility and effectiveness in order to reduce TAT in the aircraft repair and overhaul industry has been considered. Accordingly, at the beginning and after the comprehensive review and analysis of the literature, the subject of a comprehensive list of principles of lean production has been studied and the indicators have been considered according to the characteristics of the aircraft repair and overhaul industry. Then, based on the obtained indicators, a questionnaire was prepared, and based on it, more than 25 hours of interviews with experts, including deputies, managers, and experts of the aircraft repair and overhaul industry in the case study were conducted. Based on the Yin analysis method (2003), the data of the questionnaire were analyzed and evaluated in order to identify the influential factors of service time during the aircraft maintenance check. Based on the research findings, the existence of defects in some areas, such as the lack of inventory management and alternative planning for serviceable components have created significant effects in increasing the service time of aircraft.

The problem of wheat production sustainability is an important issue that quarantines the availability of people's food at present as well as the nutrition of the next generations. Food is the essential human's need and has been used from human beings' creation until the end of its life. The existence of systems satisfying such requirements and the sustainability of them is always essential for the survival of the human race. Therefore, this study aims to investigate the sustainability of the wheat production system in Iran.  

In this research, using the system dynamics approach, the sustainability of Iran's wheat production has been studied. The literature review section of this paper concentrates on several research papers in this context, considering the systems dynamics approach. In this study, an introduction has been expressed to the human's need for food and the position of wheat as the primary source of food in meeting this need in Iran. System dynamics is one of the most commonly used approaches for modeling and simulating environmental and socio-economic phenomena. The wheat production system covering environmental, economic, and social subsystems has been taken into consideration as a case to model the problem. Key factors affecting wheat production have been collected based on a literature review. 50 years historical data for essential factors such as rain, wheat harvesting area, agricultural technology, fertilizers, population, wheat imports, and exports have been used in modeling and hence in the mathematical formulation of the problem. Based on the history of these factors and related research, the dynamic hypothesis of the problem has been defined, and the causal diagrams of the relationships between critical factors and the wheat production have been developed. After modeling and formulation, the problem has been simulated and validated. Then, various scenarios have been proposed and simulated for the sustainability of the wheat production system, and the results have been addressed. The scenarios for changing the machinery level, reducing chemical fertilizer's use, increasing organic fertilizer use, and rainfall fluctuations have been simulated one by one, and their combinations have been simulated, respectively.

The simulation results indicated that the production of wheat is highly sensitive to precipitation and technology levels in the field. Therefore, it is better to focus more on such two factors and to have more concentration on them. Since water is the most crucial resource in wheat production, it would be better to concentrate the research and development efforts on water management technologies in the field. Due to the high sensitivity of wheat production to precipitation fluctuations and technology levels, more investment and better plans should be provided for the better and more efficient use of the two sources.  

While numerous factors such as pesticides, seeds, planting and harvesting, irrigation methods, management, human resources, and related requirements affect wheat production, due to the large scale of this research, only the most critical factors were selected for the study. Examining the behavior of each of the above-mentioned factors will result in a better awareness of the existing reality and better planning for wheat production.  

The wheat production system includes environmental, economic, and social subsystems as well as numerous and complex relationships between the human and the environment. The systemic nature of such interdependencies and interactions needs systematic approaches and integrated assessment tools. Identifying and modeling correctly the intrinsic characteristics of the wheat production system assure preserves or increases its essential results over the time and help governmental organizations and institutes to move towards sustainable development and to set policies that encourage positive changes.

Since the proposed model is expected to help the government and agricultural institutions in planning wheat production efficiently, it will make the country move towards self-sufficiency in wheat production, which in turn results in psychological and social security in terms of food and increases social sustainability.

To the best knowledge of the authors, there is no comprehensive investigation on the sustainability of wheat production in Iran. The literature review indicates that the agricultural context is almost limited to a particular zone and this problem has not been addressed on the national scale.  Thus, this is the first research that examines such a problem.

The majority of sustainability assessments of the bio based industries are primarily focused on the environmental and economic aspects, while social impacts are rarely considered. While overlooking social dimension can have a serious harmful impact across supply chains. To address this issue, this study proposes a modified systemic approach for a social sustainability impact assessment of the technology treatment for converting municipal solid waste to bioenergy based on a review on the common methodologies for assessing social impacts. To show the applicability and efficiency of the proposed framework, a sample of 8 experts were used to evaluate and prioritize social sustainability criteria, using a multi-criteria decision-making method called the ‘best worst method’ (BWM). The criteria are ranked according to their average weight obtained through BWM. The results of this study help bio industry managers, decision-makers and practitioners decide where to focus their attention during the implementation stage, to increase social sustainability in their bioenergy supply chains derived waste and move towards sustainable development.

One of the main challenges of the governments of each country is the increase in pollution caused by the production of products by manufacturers. Increased pollution leads to a significant growth in the number of patients with heart failure, respiratory failure and other diseases. One of the ways to deal with this situation is to produce green products with the least degree of pollution caused by the production. The green supply chain is one of the necessary solutions to improve the current situation. Pollution is not only due to the production of the product by the manufacturers, but also the transportation of products by non-standard means of transport is a factor in the production of pollution. In this study, a supply chain with a manufacturer and a third-party logistics company (3PL) that the government monitors the supply chain is considered. The government monitors on the supply chain by setting tariffs for the producer. The results show that the presence of a 3PL, although it increases the selling price of products, but reduces pollution and also increases the amount of demand.

In this paper, a single-product, single-machine system under Markovian deterioration of machine condition and demand uncertainty is studied. The objective is to find the optimal intervals for inspection and preventive maintenance (PM) activities in a condition-based maintenance planning with discrete monitoring (CBMDM) framework. At first, a stochastic dynamic programming model whose state variable is the machine status is presented. This model whose objective is minimizing inspection, preventive maintenance and lost production costs due to the difference in actual capacity with nominal capacity, does not take into account demand. Then, demand is appended to the state variable in the second model and the average cost of lost production which is due to the difference in actual production capacity with demand is replaced in the first model correspondingly. Finally, to validate and analyze the proposed models, an application of the models in wind turbines is prepared. The numerical results show that replacing demand by nominal capacity in simultaneous inspection and preventive maintenance planning, the average total costs consist of inspection and preventive maintenance in the planning horizon are reduced in the planning horizon.

The majority of sustainability assessments of the bio based industries are primarily focused on the environmental and economic aspects, while social impacts are rarely considered. While overlooking social dimension can have a serious harmful impact across supply chains. To address this issue, this study proposes a modified systemic approach for a social sustainability impact assessment of the technology treatment for converting municipal solid waste to bioenergy based on a review on the common methodologies for assessing social impacts. To show the applicability and efficiency of the proposed framework, a sample of 8 experts were used to evaluate and prioritize social sustainability criteria, using a multi-criteria decision-making method called the ‘best worst method’ (BWM). The criteria are ranked according to their average weight obtained through BWM. The results of this study help bio industry managers, decision-makers and practitioners decide where to focus their attention during the implementation stage, to increase social sustainability in their bioenergy supply chains derived waste and move towards sustainable development.

For reducing risk effects in a supply chain, the appropriate risk assessment and ranking by the use of multi-criteria decision-making methods (MCDM) is important. Failure to properly assess and rank the risks makes the supply chain less efficient and competitive. Given the existence of both qualitative and quantitative criteria in a supply chain, the use of verbal preferences, given by authorities for determining the priority of qualitative factors, has higher reliability than that of the Crisp numbers. Fuzzy concept plays an important role in solving the problem of complexity of assigning quantitative fixed numbers to the values of verbal preferences. In the proposed method of this study, a comparison was made among the decision-making methods in the fuzzy environment for selecting a suitable method. To validate the proposed method, we compared it to some case studies from the literature. The results show that the proposed method has high validity and reliability in assessing the risks of a supply chain.

In this paper, an integrated mathematical model of the dynamic cell formation and production planning, considering the pricing and advertising decision is proposed. This paper puts emphasis on the effect of demand aspects (e.g., pricing and advertising decisions) along with the supply aspects (e.g., reconfiguration, inventory, backorder and outsourcing decisions) in developed model. Due to imprecise and fuzzy nature of input data such as unit costs, capacities and processing times in practice, a fuzzy multi-objective programming model is proposed to determine the optimal demand and supply variables simultaneously. For this purpose, a fuzzy goal programming method is used to solve the equivalent defuzzified multi-objective model. The objective functions are to maximize the total profit for firm and maximize the utilization rate of machine capacity. The proposed model and solution method is verified by a numerical example.

In this paper, a new integrated mathematical model of the production planning and dynamic cellular manufacturing system (DCMS) wherein the product mix and/or volume is different from one period to another has been developed. So far, literature review indicates that the key role of Material Handling Equipment (MHE) has not been considered in the developed model, while ignoring such role will lead to wrong results. In other words, ignoring characteristics such as MHE capacity and inter and intra-cell movement times cannot be justifiable especially in shops in which, the movement times for parts are considerable compared to their processing times. The proposed model covers concepts such as inter/intra-cell movement, reconfiguration, subcontracting, inventory and backorder, lead time for subcontracted parts, optimal lot sizing in each period, number of inter/intra-cell MHE assigned to manufacturing system, number of MHE purchased and sold in each period and price of purchasing/selling for each inter/intra-cell MHE. A numerical example and sensitivity analysis have been used to verify the proposed mathematical model.  

Due to the global market competition, the manufacturing systems are changing from traditional configurations such as flow shop and job shop toward structures such as Cellular Manufacturing System (CMS). On the other hand, customer demands are different from one period to another. In such conditions, companies that use CMS should change their cell configurations every period. In other words, a new Dynamic Cell Formation Problem (DCFP) is needed to be performed for each period. The objective is to handle a DCFP together with a production planning policy by manufacturers. This integrated problem was proposed by Bulgak and Bektas (2009) for the first time. They developed a mixed integer nonlinear mathematical model and solved several computational examples by CPLEX. In another study, Safaee and Tavakkoli Moghaddam (2009) studied an integrated model of DCFP and production planning. Their model included the outsourcing and lead time concepts together. Then, other studies proposed DCFP and production planning together with others subjects such as worker assignment, machine breakdown, company layout, etc. In this paper, the roles of inter/intra cell Material Handling Equipment (MHE) and DCFP and production planning are studied, simultaneously.  

First, a new mixed integer nonlinear mathematical model is proposed considering DCFP, production planning and the role of inter/intra cell MHE. Due to the complexity of nonlinear models, a transformation is occurred from the nonlinear developed model to a linear one. Then, the linear model is coded in commercial software named ‘GAMS’. Several examples are run on GAMS to validate the proposed model. Finally, the sensitivity analysis is performed on a number of important parameters.  

In order to illustrate the effect of MHE on the DCFP and production planning, two numerical examples were investigated with and without MHE. The first difference between these two examples was in objective function value as represented in Table 3 regardless of MHE and in Table 5 regarding MHE. The second difference was in production planning as it addressed in Table 4 regardless of MHE and Table 6 regarding MHE. The third difference was in cell configuration as represented in Figure 2. Finally the forth difference was in the number of MHE used in manufacturing system regardless and regarding MHE as addressed in Tables 7 and 8, respectively. All Tables and Figures proved that MHE management can play an effective role in a manufacturing system.  

In this paper, the integrated model of DCFP, production planning and MHE was investigated. A mixed integer nonlinear mathematical model was developed and then transformed into a linear one. To validate the proposed model, a numerical example was presented and this example was solved without and with MHE. Finally, a sensitivity analysis was performed on a number of important parameters. 

Swarm intelligence-based algorithms are soft computing techniques, which have already been applied to solve a broad range of optimization problems. Generally, clustering is the most common technique, which, balances the energy consumption among all sensor nodes and minimizes traffic and overhead during data transmission phases of Wireless Sensor Networks. The performance scope of the existing clustering protocols is fixed and hence, cannot adapt to all possible areas of applications. In this paper, a multi-objective swarm intelligence algorithm – which is based on Shuffled Frog-leaping and Firefly Algorithms (SFFA) – is presented as a clustering-based protocol for WSNs. The multi-objective fitness function of SFFA considers different criteria such as cluster heads’ distances from the sink, residual energy of nodes, inter- and intra-cluster distances and finally overlap and load of clusters to select the most proper cluster heads at each round. The parameters of SFFA in clustering phase can be adapted and tuned to achieve the best performance based on the network requirements. The simulation outcomes demonstrated an average lifetime improvement of up to 49.1%, 38.3%, 7.1%, and 11.3% compared to LEACH, ERA, SIF, and FSFLA in different network scenarios, respectively.

Introdution: Intensity- modulated radiation therapy is one of the treatment methods for cancer tumors. The effectiveness of this method is dependent on the accuracy and treatment planning quality. Therefore, there is a need for a plan to select the angle and intensity simultaneous optimum of radiation.
In this study, an mixed integer linear programming model was proposed for simultaneous optimization of angles and intensity in the GAMS programming environment.To implement the model, after the patient's CT was prepared, the organ cantoring was performed by CERR software and the Influence Matrix was obtained for each organ. After collecting the inputs of the problem, in order to obtain the desired outputs, was used from The GAMS software from the CPLEX solver.
Finally, the actual case of head and neck cancer is analyzed to demonstrate the effectiveness of the model. From the angle of the candidate, ¡ is chosen as the optimal radiation angles. The maximum dose received by the brainstem was 3. 999, Mandible 70, LeftOrbit 0.026, RightOrbit 0.440, Parotid Gland 0.881, OpticChiasm 0.177, OpticNerves 0.167, spinalcord 9.929 Gray and the minimum dose received by the tumor is 70 Gray. Also, the optimal amount of intensity for implementing the treatment plan on the patient is achieved.
The dose received by each organ was significantly improved compared to prescribing doses. Similarly, the comparison of the Dose Volume Histogram obtained by solving a common problem with the model and software CERR, Represents the optimal performance of the model, which improves the security rate and reduces the cost for healthy tissues.

Hubs are centers for collection, rearrangement, and redistribution of commodities in transportation networks. In this paper, non-linear multi-objective formulations for single and multiple allocation hub maximal covering problems as well as the linearized versions are proposed. The formulations substantially mitigate complexity of the existing models due to the fewer number of constraints and variables. Also, uncertain shipments are studied in the context of hub maximal covering problems. In many real-world applications, any link on the path from origin to destination may fail to work due to disruption. Therefore, in the proposed bi-objective model, maximizing safety of the weakest path in the network is considered as the second objective together with the traditional maximum coverage goal. Furthermore, to solve the bi-objective model, a modified version of NSGA-II with a new dynamic immigration operator is developed in which the accurate number of immigrants depends on the results of the other two common NSGA-II operators, i.e. mutation and crossover. Besides validating proposed models, computational results confirm a better performance of modified NSGA-II versus traditional one.

In this paper, the single machine replacement problem is being modeled into the frameworks of stochastic dynamic programming and control threshold policy, where some properties of the optimal values of the control thresholds are derived. Using these properties and by minimizing a cost function, the optimal values of two control thresholds for the time between productions of two successive nonconforming products is determined. If this time exceeds the first threshold, the production continues. If it is less than the second one, inspection, repair, or replacement occur. However, if it falls within the control thresholds, then the process of sampling continues. At the end, the application of the proposed methodology is demonstrated using a numerical illustration.