genetic algorithm

Railway tracks are among the most critical and costly components of any railroad. Optimal and timely maintenance allocation ensures system reliability and effective resource utilization. This article presents a mathematical model designed for condition-based preventive maintenance of railway tracks. The objective is to minimize total maintenance costs while maintaining system availability and reliability. The cost function considers maintenance and renewal expenses, track possession costs, and unplanned failure costs during the planning horizon. The network is divided into segments, with the standard deviation of the longitudinal level calculated as the track quality index. Prediction and recovery models are developed to assess variations in the track quality index over the planning period. Preventive operations, including inspections, tamping, and ballast replacement, are assigned to track segments based on the allowable index limits and other constraints. The expected number of random failures is estimated using the non-homogeneous Poisson process, and preventive operations aim to reduce the incidence of such failures.

Given the complexity and non-linearity of the mathematical model, a meta-heuristic method based on a genetic algorithm was developed in MATLAB software. Initial random solutions were generated using a heuristic method. As a case study, input parameters were collected and modeled for one kilometer of railway in the Arak region. The efficiency of the proposed genetic algorithm was then compared with the results obtained using GAMS software.

For the case study, the mathematical model was solved for various problem dimensions using both methods. When GAMS software was able to achieve the optimal solution, both methods produced identical results. When GAMS could only report a feasible solution, the genetic algorithm outperformed it. For higher dimensions where GAMS provided a solution, the genetic algorithm produced results with objective function values that were 40% better.

The planning of preventive maintenance operations for railway tracks during the project period is achieved through solving the proposed mathematical model. This allocation is based on the quality index of the tracks and considers the reducing effect of preventive operations on the expected number of random failures, ensuring the required minimum availability and reliability of the system. The variability of the model's input parameters allows for consideration of influencing heterogeneous factors based on the tracks, traffic, and environment, enabling adaptation to different conditions or the implementation of various strategies. Additionally, the proposed genetic algorithm can efficiently solve different dimensions of problem instances.

Clustering is one of the basic techniques in data mining and machine learning, which is used to divide a set of data into homogeneous subsets. There are different methods for clustering, each of which has its own strengths and weaknesses. One of the main challenges in clustering is finding the optimal number of clusters and optimal allocation of data to these clusters. Genetic algorithm, as an optimization method based on natural evolution, has a high ability to solve complex problems and search for large solution spaces and can be used as an effective tool in clustering. The purpose of this article is to investigate the efficiency and accuracy of genetic algorithm in data classification and compare it with traditional clustering methods for classification. In order to evaluate the performance of this algorithm, several insurance data sets are used and the obtained results are analyzed with different criteria such as accuracy. Also, different parameters of the genetic algorithm are examined and their effects on the final performance of the algorithm are studied in order to determine the most optimal settings for data classification.

In this research, to form chromosomes, at first, the number of clusters was determined. Considering that each cluster center had as many features as the number of features in the data set, the length of each chromosome was determined by multiplying the number of clusters by the number of features. New and diverse methods were used for Crossover, Mutation and Survival processes. Also, the evaluation criterion similar to the K-means algorithm was chosen to optimize the clustering performance. This innovative approach led to improving the accuracy and efficiency of the classification process.

By applying the method described in this article to three insurance data sets for fraud detection, we have interesting results with 12% improvement in F1 and 10% increase in accuracy in the first data set, 1% improvement in F1 and 1% improvement in accuracy in the first data set. Second and finally, 1% improvement in F1 and 2% improvement in the accuracy of the third data set compared to the K-means method and other methods have been achieved. Due to the 2-mode data in this data set, the problem is solved for two clusters using the algorithm and the best label for each cluster is selected according to the real labels of the data and the result is presented as the results of classification problems. Additionally, significant improvements in metrics such as ARI and other clustering evaluation criteria have been achieved, and remarkable progress has been made compared to the standard genetic algorithm.

Genetic Algorithm is able to solve complex problems without definite solution and can perform better in data clustering than traditional methods such as K-means. By combining probabilities and randomness, this approach provides the possibility to examine more points as cluster centers and improve clustering performance. The results show that this method works better than the famous methods in some cases and provides a suitable structure for data clustering.

the purpose of this research is tiering of suppliers and grouping of parts. The innovation of this research is to simultaneously consider the grouping of parts and allocation to suppliers based on the required operational capabilities of the parts and the capabilities available to suppliers and the ranking suppliers.

This research is quantitative-qualitative. Due to the use of experts' opinions and previous researches to collect data, it is qualitative and due to the use of a mathematical model, it is of a quantitative type. The method of collecting data and information is field type and checking related documents. The model is a non-linear mixed integer model with three objectives, including minimizing the cost of allocating parts to suppliers, minimizing the cost of interactions between suppliers, and minimizing management complexity. The model was evaluated with a case study in the electric motor industry and solved by the method of genetic algorithm of NSGA- II on MATLAB software; the validation of the results has also been done.

The results of the model implementation in the case study showed that four suppliers were selected as first-tier suppliers, 13 suppliers were selected as second-tier suppliers, and five suppliers could be removed or considered as substitutes. The results related to the allocation of parts and grouping also resulted in four groups of parts.

When two or more organizations seek to enhance their collaboration, they may opt to form strategic alliances. These alliances aim to progress cooperatively toward shared goals through resource sharing, while maintaining their independence. Maturity models are essential tools for aiding manufacturing organizations in developing their partnerships. However, there is a lack of empirical research on creating a strategic alliance maturity model with clear guidelines. Consequently, no existing model effectively measures the maturity of a strategic alliance, particularly one that can address inaccuracies due to human judgment and inherent evaluation uncertainties. This research aims to design a method to assess the maturity level of strategic alliances, providing a better understanding of the current state of cooperation based on strategic alliance maturity criteria.

This research developed a maturity model using fuzzy logic to evaluate the status of a collaboration at a specific point in time. The strategic alliance maturity model, based on fuzzy logic, was created through a clear and precise procedure and a multi-method approach, including literature reviews, interviews, focus groups, and case studies. A weighted fuzzy inference system combined with the fuzzy data envelopment analysis technique was employed to measure maturity levels using specific indicators. Additionally, a genetic algorithm was applied to generate a set of fuzzy rules. Like all maturity models, the one developed in this research consists of two main components: the maturity levels of the strategic alliance and its maturity dimensions.

The research model defines five levels of maturity: ad-hoc, initial, managed, planned, and optimized strategic alliance. A 44-item list of indicators for measuring strategic alliance maturity was compiled from articles, expert interviews, and analysis of successful and unsuccessful alliances. This list was then categorized into 17 criteria across six dimensions. Essential indicators were identified using the content validity ratio technique, and their relative importance was determined through data envelopment analysis. Expert surveys were used to create fuzzy sets for the variables of the fuzzy inference system. Additionally, a set of fuzzy rules was developed by examining examples of strategic alliances both domestically and internationally, and refining them through expert surveys.

The proposed model has been evaluated and validated through a real case study involving collaboration between a manufacturing organization and its business partner. The research results demonstrate that this approach offers a robust and practical diagnostic tool based on a set of strategic alliance maturity indicators. By analyzing the gaps identified by this model, an action plan can be devised to enhance the maturity level of the strategic alliance.

In order to control the supply and demand of energy and the correct planning in directing the consumption, the monthly gas consumption of the country's industries was investigated with artificial neural network and genetic algorithm in this research for 1402. The country's population information, industrial producer price index, gross domestic product at a fixed price of 90 and gas consumption of the country's industries were investigated as influential variables. The results showed that the best artificial neural network combined with the genetic algorithm is a network with a mutation rate of 0.5, an intersection rate of 0.5, the number of repetitions of 150, and the initial population size of 150. The combination of artificial neural network and genetic algorithm showed that in the spring season there was a total of 2957.7 million barrels of crude oil equivalent, in the summer this figure increased to 3502.6, in the autumn to 4329.9 and in the winter with a growth of 8.15 percent. It will reach 4683 million barrels of crude oil equivalent

To reduce the conflict of interests between managers and shareholders, it is crucial to focus on the sharing of benefits. Managerial remuneration is one way to address this conflict and serves as a tool to align managers' perspectives and performance with the goal of increasing shareholder wealth. Cash rewards for managers should be performance-based to ensure their alignment with shareholder interests. When designing the CEO's salary package in companies, the role of institutional investors is significant. Agency theory highlights the problems that arise when owners delegate the management of the company to managers. To mitigate agency conflicts, managerial rewards should be tied to the value created for shareholders. One of the primary methods for measuring managerial performance is through accounting reports, which act as tools for assessing and motivating managerial performance. Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization.
The statistical population of this research comprises all companies listed on the Tehran Stock Exchange from 1390 to 1400. To select the sample, a systematic elimination method was employed, resulting in a sample of 110 companies. Based on the classification of research according to its purpose, the current study is applied in nature. Additionally, it is a quasi-experimental study within the domain of descriptive research (non-experimental survey). Data collection methods for this research include document analysis, internet research, and library study, depending on the specific requirements. In this research, 12 parameters influencing the sensitivity of CEO compensation were selected: institutional ownership, family ownership, comparability of financial statements, profit management, conditional conservatism, income and cost matching, market added value, corporate acquisition, debt contracts, and cost behavior (categorized into three types: changes in asset returns, changes in sales revenue, and changes in operating costs). These parameters were used as inputs for the data mining model. The sensitivity of CEO service compensation was chosen as the output parameter. Three data mining models were created by separating the cost behavior parameter, and for comparison, three linear regression models were also employed.
The results demonstrate the superiority of the deep neural network model in terms of the coefficient of determination and MSE index. This superiority holds true for all three data mining models compared to the three linear regression models. Among the data mining models, the third model, which incorporates the cost behavior parameter of changes in operational costs, produced the best results. The second model, which includes the cost behavior parameter of changes in sales revenue, achieved the next best results. Finally, the first data mining model, which uses the cost behavior parameter of asset return changes, delivered the weakest results.
The application of deep neural networks, optimized by meta-heuristic algorithms, can create predictive models based on real data, which can be used for management decisions and enhancing service compensation processes in organizations. These methods offer the potential to improve CEO performance and the quality of services provided by organizations by leveraging existing data and artificial intelligence algorithms. Furthermore, this research can assist investors and economic decision-makers in more accurately analyzing and predicting the sensitivity of CEO compensation using deep neural networks and meta-heuristic algorithms.

With the passage of four generations of theoretical and experimental development of the smart city, it was expected that by relying on technologies such as the Internet of Things, artificial intelligence, cloud computing, and the like, this theory would be the way to face the problem and solve the complexities of resilience planning. But countless recent researches show the opposite. The root of the problem is not in the application of technology, but in the theoretical integration of smart city management and urban resilience. In the meantime, the resilience approach is a new point of view that instead of focusing on reducing vulnerability and looking at confrontation, it emphasizes increasing the resilience and flexibility of cities against natural and human hazards. The purpose of this research is to evaluate the feasibility of smart urban management to improve the resilience of Urmia city. To achieve this goal, a descriptive-analytical research method was used based on the analysis of a questionnaire that was completed by 30 urban experts and specialists. The results of the statistical analysis show that Urmia city is relatively favorable in terms of social and infrastructural resilience, but it does not have a favorable situation in terms of institutional-management, economic and physical-environmental resilience. In general, the overall resilience of Urmia city is lower than the optimal level according to the experts, so that the calculated value of the overall resilience of Urmia city is 2.90, which is less than the average level (3).

In the procurement process, small and medium-sized enterprises (SMEs) are often in a weak position when facing strong suppliers. Adopting a joint procurement strategy that represents the long-term horizontal cooperation of SMEs in a procurement process, can be considered as an attractive and efficient strategy. Therefore, the purpose of this paper is to propose a mathematical model and optimization algorithm for the decisions related to sourcing and inventory management of SME’s procurement alliance.

In the proposed joint procurement model, SMEs are able to obtain quantity discounts and reduce the total purchase cost by aggregating purchase demands to a relatively larger-scale purchase order. Therefore, the model determines order quantity of each SMEs in the procurement alliance from each of the suppliers in order to minimize the cost of procurement, the number of defective purchased materials and the number of delayed materials delivered. In addition, in this model, the constraints of minimum order quantity, inventory capacity and purchase demand are considered. To examine the validity of the model, a real example using the non-dominant sorting genetic algorithm II (NSGA II) has been optimized.

The results of the numerical experiments showed that the joint procurement model and the proposed optimization algorithm are effective and three objectives are significantly improved in the joint procurement scenario comparing with the independent procurement scenario.

In this article, a multi-objective model for supplier selection and order allocation in the joint procurement process of SMEs is presented. In addition, constrained non-dominant sorting genetic algorithm has been developed to optimize this constrained multi-objective model.

Given the competitive and globalized nature of markets, availability has become a crucial aspect of product design in recent decades. Modern availability includes functional requirements, adherence to standards, design considerations, predictability of availability, modeling, and evaluation. One objective of availability is to design systems with maximum accessibility. System availability is often improved by enhancing the availability of individual components or by allocating redundant components. These improvements are achieved through better materials, improved manufacturing processes, and the application of design principles.  This paper introduces an innovative approach to optimizing multiple parallel-series multi-state systems. Unlike traditional methods that focus on optimizing a single system, this approach simultaneously optimizes multiple systems to enhance their overall efficiency and performance. These systems contain parallel subsystems with multi-state components that can operate in various states, providing different performance outcomes. A significant aspect of this model is the impact of multi-stage failure rates on the systems, analyzed through state diagrams. The model also considers various assumptions, including the capability to select suppliers with different conditions and constraints. Additionally, the effects of technical and organizational activities on continuous optimization intervals are analyzed. The model is refined using a genetic algorithm, showing considerable improvements in system performance. An optimization mathematical model is presented to address the problem under specified assumptions. A numerical example is provided where the state transition distribution function is exponential, and technical and organizational activities have varying performance intensities. In this example, the performance rate of each subsystem equals the sum of the performance rates of its components, and the system's performance is at least as good as the minimum performance rate of its subsystems. Based on these assumptions, the system's availability probability and cost can be calculated using the model's objective function. The example problems are then solved using a genetic algorithm, and the results are reported.  Recent research indicates that scholars in the field of redundancy allocation models for both binary and multi-state systems have continuously aimed to make these problems more realistic by incorporating new assumptions or eliminating simplifying ones. These efforts underscore the importance of developing mathematical optimization models that consider all system conditions and constraints, addressing the broader issues faced by decision-makers. Our research demonstrates that expanding the dimensions of optimization problems related to redundancy allocation can produce models that better reflect real-world conditions.

Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decision-making problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated.  In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market.  In this research, we have used of firms’ performance between 1396-1400 in chemical industries in order to determine portfolio selection. The main advantage of proposed approach is help an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge.  

The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.

Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.

The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.

Originality / Value: 

This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study.

Investment in industrial equipment is a necessity for every industrial unit. In this study, a mathematical model for intelligent monitoring of the performance of industrial equipment of wind turbines using the Internet of Things and unmanned aerial vehicles (UAVs) with an edge computing approach is investigated. In this model, the performance of the UAV for intelligent monitoring of wind turbines is investigated in three stages: detection, computational offloading, and local computations. Considering the bi-objective nature of the final model, the model was solved by a genetic algorithm with non-dominated sorting and the epsilon constraint method using random numbers. According to the findings of the study, the epsilon constraint method loses its efficiency with increasing model dimensions and is not able to find the Pareto frontier in problems with large dimensions. For this purpose, the model was solved using the second version of the genetic metaheuristic algorithm with non-dominated sorting and a new structure for representing chromosomes. This algorithm was able to solve problems in large dimensions that the epsilon constraint method was unable to solve. According to the results, the epsilon constraint method and the genetic algorithm with non-dominated sorting differ only in the time to solve, and have similar performance in other criteria; as a result, the genetic algorithm with non-dominated sorting is proposed as the superior method.

Supply chain optimization involves the integration of supply chain activities and related information flows by improving the state of chain relationships to achieve a secure and sustainable competitive advantage. In this realization, the issue of supply chain optimization has been chosen because to provide goods and services in the economy, we need a mechanism and system to maximize economic efficiency and productivity and provide return goods and services at lower and easier costs and the circular economy to be realized, the main discussion of which is related to the recycling of returned goods and goods that need to be repaired so that they can be reused. This research also emphasizes the use of healthy parts and components in the reproduction of products and this issue is considered for all goods in each part of the supply chain that cannot be used and must be transferred to the reproduction. The results of the implementation of the model presented in the genetic algorithm show that this algorithm was able to reach an almost optimal answer at the right time. This algorithm requires more computational time than LINGO optimization software to solve problems that have very small dimensions but this algorithm needs much less time to solve problems due to the increase in problem dimensions than LINGO optimization software. Therefor in these examples we observed that this algorithm can reach an acceptable answer in a much shorter interval than LINGO optimization software for large scale problems.

Identifying factors that affects students’ performance can help universities and higher education institutions in improving the quality of education. The knowledge acquired from educational data can help decision makers in understanding students’ behaviors and improving students’ performance. In this research, using the data collected from the students, some models for predicting their educational performance (in terms of their average score) are proposed. To develop these models, first, the affecting factors are identified using the best subsets regression and genetic algorithm. Then, using the identified factors, the average score of the students is predicted by the decision three and neural network methods. The results revealed that the predictions from the decision tree algorithm based on the factors obtained from the genetic algorithm were more accurate than from the others. Beside the real data collected from the students, the use of different data mining techniques provides rich information, which made the decisions more sensible.

Pollution due to biomass burial, the possibility of producing clean energy from biomass and the high demand for ‎energy have made the optimization of the biomass supply chain network important and necessary. The purpose of ‎this article is to optimize biomass supply chain network at four levels in order to reduce economic and ‎environmental costs. The most important gap in research, resolved in this article, is the determination of the ‎desirable and undesirable outputs of the masses in the centers. Separating and considering the multi-period, multi-‎product mode with heterogeneous transport means. The research model is a two-objective linear programming of a ‎correct number mixed with uncertainty and disturbance, four scenarios were designed for this purpose. The model ‎was solved with genetic algorithm and MOPSO method and with Python software. Validation of the model was ‎investigated in a real case study in Fars province has been The proposed model has been able to implement ‎sustainability and resilience at the same time, which has reduced costs, reduced carbon emissions, and increased the ‎commercialization of energy production from biomass, thus increasing the willingness of investors to invest in this ‎network. It is supplied from the supply chain. The proposed model makes the amount of energy production 2.1% ‎lower than when the favorable and unfavorable outputs are not considered, which means it is much closer to reality. ‎By performing sensitivity analysis on real data, the efficiency of the model was proved

Cell production is one of the most important applications of group technology that forms production cells in such a way that each family of parts in a cell is processed by a certain group of machines related to that cell (machine cell). In order to have a suitable and coherent plan for a good and quality production, two important steps in production planning should be taken: sequence of operations and scheduling. Sequencing of operations and scheduling is a decision-making process that takes place by allocating resources over time to execute a set of activities in order to optimize one or more objectives. In fact, the main goal in operation sequence scheduling problems is determining the sequence of tasks and allocating resources to tasks in such a way that one or more objectives are optimized. In the proposed model, we intend to design an integrated model of cell formation with cell arrangement and operation scheduling in the cell production system and assigning operators with the aim of minimizing the time to complete the work in addition to paying attention to the non-overlapping of cells and specifying the position. Regarding machines, let's determine the optimal arrangement of the cells independently in our modeling and specify the optimal placement of the cells together. In this research, after nonlinear mathematical modeling and data collection and measurement, the problem is solved by a genetic algorithm, and finally, the best-proposed layout will be selected among the 3 existing scenarios according to the created cost.

Due to the trend of increasing demand for electricity consumption, the use of current fossil fuel-based electricity production technologies has led to frequent blackouts in the summer and fuel burning in the winter. Therefore, the use of renewable energy sources and especially the construction of solar power plants can be a solution to end the blackout and fuel burning crises. The purpose of this research is the multi-objective optimization of the allocation of solar power plant construction to meet the electricity demand. In the objective functions of the problem of system costs, the amount of disturbances in the lines and the amount of greenhouse gases were minimized, as well as the amount of covered areas was maximized. In this research, the LP metric method has been used to solve the problem of multi-objective optimization in GAMS software. In order to validate the presented mathematical model, the correctness of the presented model was checked by GAMS software.Then, by presenting a numerical example, the complexity of the problem, the effectiveness of the multi-objective genetic algorithm in small and large dimensions were investigated. Finally, a case study regarding the construction of a solar power plant in Kashan, Aran and Bidgol region was investigated  and  According to the given information and the input data of the problem, the situation of the candidate areas for the construction of solar power plants was determined and Also, different areas of demand were assigned to the built solar power plants.