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Purpose: This paper aims to propose a flow shop scheduling problem for equipment overhaul. This problem consists of three stages, the separation of the components of an equipment is done in the first stage. Repairs and overhaul operations are carried out on the separated parts of the first stage in the second stage. Finally, the overhauled parts of the previous stage are mounted on each other in the third stage. In the third stage, operations are performed in parallel workshops. The objective function of the problem is the maximum time to complete jobs, and the sequence of processing jobs should be done in such a way that the value of the objective function is minimized. Design/methodology/approach: To solve the problem, a mixed integer programming model has been proposed for small size, which determines the processing sequence of jobs based on the position of each job. A genetic algorithm has been used to solve the problem in large dimensions. By increasing the size of the problem and in different sizes, the results have been examined and analyzed, which shows the efficiency of the model and the proposed algorithm. Findings: To check the accuracy of the model's performance and also the effect of the presence of parallel machines in the third stage, an example was presented in this paper. Accordingly, while the accuracy of the model's performance was checked, the effect of the presence of more machines was determined in the third stage. As the number of machines increased in the third stage, the value of the objective function did not deteriorate. The performance of GAMS in solving the problem in a small size was investigated. Considering that solving the problem for large dimensions is not possible in a reasonable time and the problem is NP-hard, then solving the problem in large dimensions was done using a genetic algorithm. Therefore, solving the problem on a large dimension has been done using the genetic algorithm. According to the obtained results, the efficiency of the genetic algorithm was shown. Due to its low average value, it indicated the convergence of the genetic algorithm. Research limitations/implications: Considering that there are not many published papers in the field of equipment overhaul, it is difficult to access related models and papers. Therefore, in this paper, the model and solution method have been written with many reviews. Also, to check and reduce costs, the number of third-stage machines has been determined using sensitivity analysis. Practical implications: The problem of equipment overhaul is used in many fields in reality. For example, the operations related to the maintenance, repair and overhaul of the aircraft engine have been investigated. Since the planning of maintenance and maintenance operations is difficult, the focus of research has been on improving maintenance operations by finding suitable scheduling for job shop operations in maintenance operations. They emphasized that scheduling can improve maintenance operations and presented a simulation model. Social implications: The purpose of creating a space to start an activity is to reduce costs, earn money and achieve profit. To examine the cost-effectiveness of the equipment overhaul issue, we can refer to the number of third-stage machines. According to the amount of equipment to perform an overhaul on them, the number of third-stage machines can be determined. Therefore, extra machines can be removed to reduce the cost. On the other hand, if the related equipment to the customers is different, to reduce the storage costs or increase customer satisfaction, different goals should be considered. Here, the objective function of maximum completion time is considered for this purpose. If the equipment must be available at a certain time, goals such as the total time to complete the job can be considered. In line with the application of the social implications in the investigated problem in this paper and considering that the investment costs, as well as the ability to respond to the applicants' requests, are related to the number of third-stage machines, the value of the objective function is analyzed based on the number of third-stage machines and analysis has been done. Originality/value: In this paper, a three-stage flow shop scheduling problem in the overhaul industry was studied. Accordingly, a new mathematical model based on the job processing position was proposed, which dealt with the exact solution of the problem in small dimensions. According to the type of problem in the overhaul environment, the combined flow shop problem for equipment overhaul was investigated. Also, the use of parallel machines in the third stage of the equipment overhaul problem is one of the new issues under investigation. [ABSTRACT FROM AUTHOR]

In the present paper, crop pattern criteria have been evaluated relying on sustainable development to increase agricultural water productivity. Seven criteria were selected as the main environmental and economic criteria and were prioritized and reviewed for important and strategic products in the Tajan catchment of Mazandaran province. Criteria prioritization was done using optimization through a genetic algorithm with an objective function based on sustainable development. Then, physical and economic productivity indices were calculated to determine the productivity value. Based on the results, in the selection of the crop pattern, firstly, the category of economic criteria and finally the category of environmental criteria have been given attention to the farmers in the current situation. But in the genetic optimization algorithm, all priorities have a similar order from the environmental point of view and then from the economic point of view although each product has its order of criteria. By this prioritization, the parameters of the cultivated area, the volume of water consumed, and the amount of chemical fertilizers have decreased on average by 26%, 34%, and 21%, respectively, and the parameters of product performance and profitability have increased by 43% and 61%, respectively. In addition to providing environmental standards and increasing sustainable development, this prioritization causes an average increase in physical productivity by 84% and an increase in economic productivity by 72%. [ABSTRACT FROM AUTHOR]

Permanent magnet machines are widely used in wind turbines due to theirs prominent characteristics. The direct-drive wind generators have large size and high manufacturing cost because of low operational speed so that these disadvantages deteriorate when the rated output power increases. Therefore, the main objective of the optimal design proposed in this paper is to minimize the wind turbine system cost maintaining the high efficiency characteristic. Thus, a multiphysics model of the wind generation system is firstly presented to be the basis of the optimal design. One of the paramount feature of the current study is to precisely calculate all of the energy production costs, including the manufacturing cost of generator, power converter, electrical subsystem, and also wind turbine tower cost. In order to obtain the optimum solution, the Genetic Algorithm (GA) optimization method is implemented with consideration of a predetermined proper range for optimal variables. By comparing the optimal design results with those attained by the innerrotor permanent magnet generator, the superiority of the proposed method is justified in terms of the total volume, weight, and annual output energy. Eventually, the finite element analysis has also been carried out to validate the outcomes obtained from the proposed optimal design. [ABSTRACT FROM AUTHOR]

The growing use of internet and the existence of vulnerable points in networks have made the use of intrusion detection systems as one of the most important security elements. Intrusion detection is essentially a classification problem and it is the identification of effective features such as important issues in the classification This paper presents a novel method for selecting effective features in network intrusion detection based on an estimation of distribution algorithm that uses a probabilistic dependency tree to identify important interactions between features. To evaluate the performance of the proposed method, the NSL- KDD dataset is used, in which the packets are divided into five normal types and intrusive types of DOS, U2R, R2L and Prob. The performance of the proposed algorithm has been compared alone and in combination with other feature selection algorithms such as forward selection, backward selection and genetic algorithm. Moreover, the effect of algorithm parameters like population size on intrusion detection accuracy is tested. Based on this analysis and also considering the intra-class accuracy of different feature selection methods studied in this paper, an effective subset of features for intrusion detection is identified. [ABSTRACT FROM AUTHOR]

Time series data may be contaminated with different types of outliers. i.e. additive outliers, innovation outliers, level shift and temporary changes. Of course, the effects of these outliers in univariate and multivariate time series are different and detection of outliers in multivariate data are more complicated. Multivariate time series often, modeled using vector autoregressive moving average (VARMA) model and presence of outliers can violate the stationarity assumptions and may lead to wrong modeling, biased estimation and inaccurate prediction. Thus, detection and properly dealing with these data, especially in relation to modeling and parameter estimation of VARMA model is necessary. By detection of outliers, their effects can be removed or robustified and the adjusted data could be prepared for further analysis. The multivariate detection methods, suggested by Baragona and Battaglia (2007) and Cucina, et al. (2014), could not detect the innovational outliers and temporary changes. In this paper, after introducing the VARMA models, the principles of genetic algorithm and its application in detecting outliers are discussed. Then, the Tsay, Pena and Pankratz (TPP) method (Tsay, et al.(2000)) and the genetic algorithm (GA) for detecting different types of outliers in multivariate time series are described. Also, the efficiency of GA and TPP detection methods is discussed using simulation studies and real data. At the end, a conclusion is presented. Material and methods A genetic algorithm makes uses of techniques inspired from evolutionary biology such as selection, mutation, inheritance and recombination to solve a problem. The most commonly employed method in genetic algorithms is to create a group of individuals randomly from a given population. The individuals thus formed are evaluated with the help of the evaluation function provided by the programmer. Individuals are then provided with a score which indirectly highlights the fitness to the given situation. The best two individuals are then used to create one or more offspring, after which random mutations are done on the offspring. Depending on the needs of the application, the procedure continues until an acceptable solution is derived or until a certain number of generations have passed. For minimization of a function, GA operates by first generating, at random or optionally, several minimal solutions to the function, where this set of solutions called the initial population and each solution as a chromosome. Then, using reproductive operators, we combine chromosomes and make a jump into them. If the function of newly produced chromosomes is lower than the previous chromosomes, these chromosomes can be added to the initial population or replaced with chromosomes with less function in this population. This process is repeated until convergence occurs or the end number of iteration obtained. Furthermore, we introduce another method for detecting outliers, i.e. the Tsay Pena and Pankratz (TPP) method. TPP uses some test statistics based on outlier's size and vector autoregressive (VAR) parameters. This method detects outliers in three stages. In stage I, it detects one by one outliers and remove their effects. Iteration continues until no outlier found. In stage II, for detected outlier in stage I, the estimation of outliers effects is obtained simultaneously. Then, outliers with insignificant effects are removed. The VAR parameters re-estimated based on modified series of this stage. In stage III, we repeated stage I and II with new VAR parameters estimation. In each iteration of TPP, an outlier is detected and removed from the series. Then the parameter estimation is obtained from the modified series and the next outlier detection is continued using these estimates. This may lead to biased estimates and wrong detection of the next outlier point. In other words, in the TPP method, one detected outlier hides another outlier (masking), or one detected outlier reveals the usual observation as an outlier (swamping). This method often undetects the type of outliers. But in each iteration of GA, a random pattern of outliers (for testing) is first generated and a temporary modified series is obtained by removing effect of this pattern from series. Then the estimation of the parameters obtained and the detection of this pattern is tested. This method reduces the effect of the previously identified outliers on the full pattern of them. In fact, if the random pattern of all outliers is correctly generated, almost effect of them will be eliminated in the modified series. Therefore, using this temporary modified series, the GA obtains more accurate estimates and detects outliers more accurately. Results and discussion The simulation results confirm the validity of the GA method and the percentage of correct outlier detection in this method is higher than the TPP method. GA, of course, needs more time to calculate. Also, although the VAR model is used in both detection methods, the percentage of correct outlier detection in the VARMA model data is similar to the VAR model. The Gas-furnace data set, called Series J by Box and Jenkins (1994), contains sequentially recorded measurements of two variables (gas rate and CO2) were analyzed and modeled. It was determined that GA and TPP methods detect similar outliers. Fitting the VAR (6) model to these data shows that the variance of input gas error in modified data of GA is reduced by 17% as compared with TPP and the variance of carbon dioxide error in the modified data of GA is reduced by 43% respectively. Conclusion The following conclusions were obtained from this research: In each iteration of the TPP outlier detection method, an outlier is detected and the effect of this point is adjusted. Then the estimation of the parameters is obtained from the modified series and the next point detection continues using these estimates. This may lead to bias of estimates and misdiagnosis of the next point. In other words, using the TPP method, one detected outlier hides another outlier. In the proposed method based on the genetic algorithm in each iteration, first a random design is generated from all outliers and the modified series is obtained from the same design. This reduces the effect of previously detected outliers on revealing the complete design of the outliers. In fact, if the random design of all outliers are generated correctly, the effect of them will be eliminated in the modified series. Thus, with this modified series, GA obtains more accurate estimates and detects outliers more correctly. The simulation results confirm the accuracy of the GA method and the percentage of correct detection of outlier in this method is higher than the TPP approach. Of course, GA requires more time for calculations. [ABSTRACT FROM AUTHOR]

Nowadays distributed generation has been given considerable attention due to the increase in energy consumption and the need for using renewable energies. So, the concept of the micro grids for connecting and coordinating different distributed resources via the local power control systems has been introduced. A micro grid connects to the network at normal condition of the operation but in order to increase the system reliability and to promote the generated power quality to feed the sensitive loads, they can operate in the islanding mode too. In the islanding mode of operation in order to supply the whole load and to maintain the balance between the supply and demand, these resources use the conventional droop methods. Generally, using the conventional voltage droop control due to the difference in the impedance of the feeder connect to the resources and also the asymmetrical nature of the network, dose not lead to an appropriate reactive power sharing between the resources. In fact, using conventional droop control creates an intrinsic compromise between the accuracy of reactive power sharing and voltage regulation of micro grid buses. Therefore, in the proposed method of this paper, the coefficients of the voltage droops characteristics of the grid resources are determined by solving an optimization problem using the genetic algorithm (GA). Also in order to improve the reactive power sharing, a novel control strategy based on the adaptive virtual impedance is presented. In this method the ideal reactive power sharing is achieved by using the virtual impedance and compensating the voltage drop of the lines. The virtual impedance setting is done through the data of the voltage of PCC bus in microgrid and sending via a low bandwidth communicational link to the local controller of each resource. The proposed method is robust against the network structure changes, presence of local loads, and interruption in the communication link. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. There are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. Every option should be assessment based on the different criteria such as cost, mass, reliability and technology contraint (complexity). In this research, mass and technology constraints, which have a direct impact on the satellite life cycle cost, are considerd as system level objective function to obtain the system optimal solution during the coceptual design phase. The approach adopted in this paper is based on a distributed collaborative optimization (CO) framework. At system level, multiobjective optimization goal is to minimize the dry mass of the satellite and, simultaneously, minimize the system technology complexity which is subject to equality constraints. The use of equality constraints at the system level in CO to represent the disciplinary feasible regions, introduces numerical and computational difficulties as the discipline level optima are non-smooth and noisy functions of the system level optimization parameters.To address these difficulties robust optimization algorithms such as genetic algorithms (GA) are used at the system level. The results show that the CO framework has the same level of accuracy as the conventional All-At-Once approaches.

In this paper, a bi-objective integrated of production distribution planning problem model in a multi echelon supply chain is presented. This model is considered in a four echelon supply chain includes suppliers, manufactures, distribution centers and customers with several types of raw material and product in multi-time period. In addition to minimize the total cost of supply chain including transportation and supply of materials costs, preparation and production of products, inventory holding of materials and products in manufacture and distribution centers, transportation and purchase of products for distributors and customers, and backorder cost, the second objective is to decrease the transfer time of products in order to increasing the service level for customers. Two Pareto-based multi-objective metaheuristic algorithms namely non-dominated sorting genetic algorithm (NSGA-II) and nondominated ranking genetic algorithm (NRGA) are proposed to solve the model. Since the solution quality of all meta-heuristic algorithms severely depended on their parameters, Taghuchi method has been utilized to tune the parameters of algorithms. Finally, computational results obtained by implementing the algorithms on several problems of different sizes demonstrate the performance of proposed methodologies. [ABSTRACT FROM AUTHOR]

In this paper, the continuous Ant Colony Optimization Algorithm (ACOR) is used to investigate the optimum operation of complex multi-reservoir systems. The results are compared with those of the well-known Genetic Algorithm (GA). For this purpose, GA and ACOR are used to solve the long-term operation of a three-reservoir system in Karkheh Basin, southwestern Iran. The solution must determine monthly releases from the three reservoirs and their optimum allocations among the four agricultural demand areas. Meanwhile, a minimum discharge must be maintained within the river reaches for environmental concerns. Review of past research shows that only a few applications of Ant Colony have been generally made in water resources system problems; however, up to the time of initiating this paper, we found no other application of the ACOR in this area. Therefore, unlike GA, application of Ant-Colony-based algorithms in water resources systems has not been thoroughly evaluated and deserves serious study. In this paper, the ACOR is stuided as the most recent Ant-Colony-based algorithm and its application in a multi-reservoir system is evaluated. The results indicate that with when the number of decision variables increases, a longer computational time is required and the optimum solutions found are inferior. Therefore, the ACOR would be unable to solve complex water resources problems unless some modifications are considered. To overcome a part of these drawbacks, a number of techniques are introduced in this paper that considerably improve the quality of the method by decreasing the required computation time and by enhancing optimum solutions found.

This paper presents an approach that uses both genetic algorithm (GA) and fuzzy inference system (FIS), for feature selection for descriptor in a quantitative structure activity relationships (QSAR) classification and prediction problem. Unlike the traditional techniques that employed GA, the FIS is used to evaluate an individual population in the GA process. So, the fitness function is introduced and defined by the error rate of the GA and FIS combination. The proposed approach has been implemented and tested using a data set with experimental value anti-human immunodeficiency virus (HIV) molecules. The statistical parameters q2 (leave many out) is equal 0.59 and r (coefficient of correlation) is equal 0.98. These results reveal the capacity for achieving subset of descriptors, with high predictive capacity as well as the effectiveness and robustness of the proposed approach.

The main purpose of the reliability theory of structural systems is the optimal design based on the reliability concept. Trusses and space structures are widely used, and cost and safety both are important in these structures. Therefore the weight optimization of these structures based on the theory of reliability (probabilistic optimization) using Genetic Algorithms was studied in this paper. The objective function was weight of structure and the constraints were based on the failure probability of elements or nodes or the system. To increase the accuracy of structural safety calculations, the probabilistic form of buckling is presented in this research.The results of this study show that increasing the system allowable failure probability decreases the optimum weight of structure. The process of this weight loss occurred at first with a very fast slope and then with a gentle slope and finally tended to a constant value. Optimum weight was very sensitive to coefficient of variation comparison of allowable failure probability. In the probabilistic optimization, if the system allowable failure probability and coefficient of variation of load and yield stress are considered near zero, and if results are compared to the deterministic optimization, the difference in optimum weight is less than 9%. In this case, convergence for deterministic optimization was very fast and occurred in less than 20 generations but convergence in probabilistic optimization occurred slowly after 85 generations.

Production planning is one of the most important challenges in steel industry. Order planning is one major part of this aspect according to the fact that production is usually planned based on customer orders in this industry. Inventory planning is also vital for maximizing capacity utilization. The proposed model in this paper is capable of selecting order, assigning finish time of each order, and determining open orders in each process by considering order planning and inventory planning simultaneously. The model could have a key role in improving production planning of steel companies by on time delivering of the orders, reducing work-in-process, and balancing utility of capacities. Finally, a heuristic algorithm based on genetic algorithm is proposed and its efficiency evaluated in an empirical case study of Iran alloy steel enterprise. [ABSTRACT FROM AUTHOR]

This paper is intended to see whether the model of genetic algorithm can be used in developing a support system for the proper allocation of resources to help library heads of university. The research population consisted of students, personnel, and faculty members of Tarbyat Modares University in 2011 with 2437, 1105, and 555 people respectively, and the sample consisting of 92 graduate students, 83 post-graduate students, 82 faculty members and 88 personnel. The main tool of the research is a researcher-made questionnaire. The data of the central library of a university as the main core of DSS includes costs and time of accessing information services, and the time spent for preparing them systemically and physically, gathered through the questionnaire. The findings on the questionnaire indicated the present status of library services, and the findings received from the software indicated the optimum status in providing the services. Comparing these two while discovering the strengths and weaknesses can help the managers in optimum allocation of research services. [ABSTRACT FROM AUTHOR]

In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective of this chain is maximizing the total profit. The second objective is minimizing the use of raw materials. In the other words, the second objective is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and Epsilon-constraint method is used for finding the pareto optimal line,. Finally, a numerical example is solved with proposed approach and performance of the model is evaluated for different sizes. The results show that this approach is effective and useful for managerial decisions. [ABSTRACT FROM AUTHOR]

Objective: This paper is an attempt to explain the relationship between social factors and tendency to addiction and determine the relationship of this tendency with social insecurity. In addition, the contribution of each of the social factors that lead to social insecurity is determined. Method: The current research method is descriptive. At first the relationship between the research indices (family, neighborhood, unemployment, etc.) and tendency to addiction was described. Then, the relation of this tendency with social insecurity has been expressed. In the following, the contribution of each of these factors in the estimation of social insecurity has been determined. Results: The results showed that the indices under study are significantly correlated with tendency towards addiction and social insecurity. The most important predictors of social insecurity were respectively as follows: other factors (extent of border with Pakistan and Afghanistan, international and regional trafficking activities, the booming market of drugs, etc.), anomic situation, industrialization, family, friends and peers, access to drugs, unemployment, and finally neighborhood. Conclusion: Such variables as family, neighborhood, friends and peers, anomic situation, access to drugs, industrialization, and unemployment play a significant role in social security.

Ongoing sanctions on Iranian economy have proved to be very harmful and detrimental to Iranian economic affairs and social welfare. Evaluating the unfair impacts of these sanctions on Gross Domestic Product (GDP) and social welfare is the aim of this paper. Firstly, we have developed a generalized growth model in the presence of sanctions while treating exchange rate as a random variable. Secondly, three different forms of sanctions are introduced into the model and their bearings on national product and social welfare is studied. The first tier of the sanctions is imposed on consumption, intermediary and capital goods while exchange rate is assumed to have a random behavior. Then sanctions are also imposed on Iranian oil and gas production. We have devised several scenarios using stochastic Hamilton Bellman Jacobian value function (SHBJ) and genetic algorithm optimization methods. Our results of the first and second scenario imply that the level of social welfare is mostly affected by oil and gas sanctions while goods embargo has targeted goods production. The effects of sanctions on GDP and social welfare is represented by a concave curve. This curvature shows that the impact of sanctions on GDP and social welfare is stronger at the beginning than later on when further sanctions are introduced. In the third scenario oil, gas and goods sanctions are imposed simultaneously. Our results also show that the third scenario effects is stronger than the other two. According to the Gross Domestic Product data acquired for year 1390, oil and gas sanctions have lowered the GDP by 30 percent, while the overall reduction in GDP through all sanction collectively is estimated between 30 to 50 Percent.

Designing timetables, for example course timetables in an institute, is one of the most complicated and time-consuming challenges for personnel. Automating it, not only can help the personnel to manage their work better, but also can be considered as a desired sample to assess the ways of planning and to tackle the constraint satisfaction in artificial intelligence. In this paper, genetic algorithms are primarily studied and then it is applied for optimization of an imaginary faculty course timetable. The new designed algorithm is based on keeping the better chromosomes of the population and employing genetic operators on the others in order to improve the overall quality of genes. Some other amendments are also carried out to develop a more capable genetic algorithm for TT applications, compared to the standard one. According to the tests, the new GA algorithm will be more successful in generating high fidelity TTs which do not break any hard constraint. The proposed ideas, in this approach are applicable in other similar situations.