metaheuristic algorithms

Production planning, scheduling, and sequencing form the core functions of manufacturing companies. The evolving and fluctuating market demands have turned production into a challenge, as companies must deliver high-quality products using minimal resources while responding to uncertain market demands. Therefore, the need for efficient production planning, scheduling, and sequencing has become a crucial research area for both companies and researchers in recent decades. This paper addresses the modeling and solution of a production planning and scheduling problem related to human-robot collaboration under fuzzy conditions. The proposed model aims to determine optimal decisions such as production quantity, human-robot allocation for product manufacturing on each line, processing time, and product production scheduling. To achieve integrated decisions for production planning and scheduling in human-robot collaboration, three objective functions are considered: maximizing the net present value, minimizing the maximum completion time of product manufacturing, and minimizing the total early and tardy times.

Since demand quantity and processing time are considered uncertain parameters in this problem, a pessimistic fuzzy programming approach is used to handle these parameters. To solve the three-objective model, the epsilon-constraint method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-objective Particle Swarm Optimization (MOPSO), and Multi-objective Whale Optimization Algorithm (MOWOA) are applied. Thus, for solving the problem in small sizes and performing sensitivity analysis of the mathematical model, the epsilon-constraint method is used, while for solving larger-sized problems, metaheuristic algorithms are employed.

The analysis of the mathematical model under uncertainty reveals that reducing the maximum completion time of product manufacturing decreases both the net present value and the total early and tardy times. Controlling the model using fuzzy programming and the uncertainty rate also shows that increasing this parameter leads to a reduction in net present value and an increase in the maximum completion time of product manufacturing. Furthermore, analyzing various numerical examples of different sizes indicates that the solution quality of the MOWOA, NSGA-II, and MOPSO algorithms is superior to that of the epsilon-constraint method. Among these algorithms, MOWOA achieves the highest number of efficient solutions with the smallest branch distance metric and the shortest distance from the ideal point.

The analyses indicate that the highest total early and tardy times occur when the uncertainty rate is set at 0.5. Additionally, sensitivity analysis of the bank interest rate shows that a 4% increase in the interest rate results in a 15.68% reduction in the net present value. The bank interest rate has no impact on the maximum completion time of product manufacturing or the total early and tardy times. The analysis of numerical examples with various sizes also demonstrates that the epsilon-constraint method is incapable of solving larger numerical examples, and the quality of the results obtained from metaheuristic algorithms is superior to that of the exact method. Moreover, the number of efficient solutions, the widest spread, and the solution time are better in the metaheuristic algorithms than in the epsilon-constraint method. Among the metaheuristic algorithms, MOWOA exhibits superior performance compared to other solution methods.

Land use allocation optimization, as an interdisciplinary field, plays an important role in natural resource management, urban planning and sustainable development with the help of spatial information systems (GIS). The aim of this study is to use Scientometrics approach to examine the status of published scientific papers to provide a comprehensive and accurate insight into trends, key concepts, and commonly used methods in research in this field and to clarify future directions for the scientific and practical development of this field.

This study, using data extracted from the Web of Science database, examined a collection of 584 papers published between 1991 and 2024. Statistical analyses of the data were performed using Excel software and publication trends, geographical distribution of papers, and active publishers were determined. In addition, VOSviewer software was used to identify frequent keywords, clustering, and analyze their thematic structure.

The results show that since 2010, the production of papers in the field of land use allocation optimization has received more attention and this production has been increasing until 2022. In this regard, China, the United States of America and Iran have had the largest share of scientific production in this field, presenting 302, 103 and 41 papers, respectively. Among the publishers, Elsevier Publishing has been the most active publisher in this field, publishing more than 38 percent of the papers. Also, the identification of keywords showed that the word GIS is the most frequent phrase and metaheuristic algorithms such as genetic algorithm, particle swarm optimization and ant colony optimization have been used as common methods for solving complex land use allocation optimization problems. Keyword clustering also identified four main areas, which include metaheuristic algorithms, spatial modeling, multi-objective optimization and resource allocation in spatial information systems.

The findings of this study show the growing trend of research in the field of optimal land use allocation. It also identifies the active countries and publications in this field. In addition, it introduces the common methods used in this field, which include meta-heuristic algorithms such as genetic algorithms. Thus, this study can be used as a valuable reference in future studies of land use allocation optimization by researchers.

Type 1 diabetes is one of the most important chronic metabolic diseases that necessitates accurate and continuous control of blood glucose levels. In this study, a hybrid framework based on fuzzy logic and metaheuristic algorithms is proposed for insulin-resistant control under uncertainty. The proposed model, using two algorithms, the bat (BA) and the greedy man (GMOA), optimizes the fuzzy control structure including membership functions and rules in such a way that the accuracy of glucose regulation is maximized and insulin consumption is minimized. The designed control system is tested based on simulated and real data and its performance in the face of sudden fluctuations is investigated. The results show that the GMOA algorithm has a more accurate performance than BA in adjusting insulin dose and reducing glucose fluctuations. Also, comparing the model output with the exact solution in the GAMS environment confirms the validity of the proposed structure. This framework can be considered a suitable basis for the development of real-time diabetes control systems and the design of smart wearable systems.

The purpose of this research is evaluation of effective criteria on the desirability of financial stability integration based on the comparison of metaheuristic algorithms banks listed in Tehran Stock Exchange. This study is considered to be a combined and applied methodology. In this way, firstly, through the systematic content screening process, the effective criteria on the desirability of financial stability integration are used to evaluate banks listed in Tehran Stock Exchange. Then, relying on the two algorithms of Particle Swarm Optimization and Gray Wolf and extracting data related to the criteria identified between 2017 and 2018 effort is made to determine the optimal point of desirability of financial stability integration of banks listed in Tehran Stock Exchange. In this process, according to the expansion of the mathematical equations of each Metaheuristic Algorithms and the command codes of the MATLAB software, necessary action is taken to answer the research questions. The results of the study showed that both innovative algorithms used in this study have the necessary capability to determine the desirability of the financial stability of banks listed in Tehran Stock Exchange. But based on the Wilcoxon Signed-Rank Test coefficients, the gray wolf algorithm is more accurate than the particle swarm optimization algorithm for predicting the function of the identified criteria in determining the desirability of financial stability of banks listed in Tehran Stock Exchange.

The supply chain management of an organization has a critical role in its success. In the past decades, competition between companies has transformed into the competition between their supply chains. Due to food safety and health concerns in today's society, livestock and poultry medicines are produced and distributed for prevention and treatment. The use of vaccines and timely access to appropriate drugs can reduce disease outbreaks and increase productivity in livestock and poultry industries. The key to success in this matter lies in having an effective and efficient pharmaceutical supply chain. A two-level supply chain of livestock and poultry medicine has been examined in this study. Also, three levels of decisions in the supply chain problem have been considered in this study. The strategic level involves location decisions; the tactical level involves inventory management; and the operational level involves routing. The possibility of drug expiration has been taken into account and sustainability has been also considered due to the drug disposal effects and transportation on the environment. The demand in this network has been assumed to be non-deterministic. Furthermore, quantitative and time-dependent discounts have been considered simultaneously to control the inventory by changing the customers' behaviour.Design/methodology/approach: Three dimensions of sustainability have been optimized by utilizing three objective functions, including minimization and maximization. The economic aspect of a sustainable supply chain is taken into account by minimizing costs. Holding cost, shortage cost, expiration cost, transportation cost, and fixed opening cost have been considered in this study. The focus of the social dimension has been on creating job opportunities and reducing life risks during transportation. Air pollution and greenhouse gas emissions have been considered as the third objective. Robust optimization has been applied to cope with uncertainty. Two meta-heuristic algorithms have been applied to solve the model which has been explained in detail in this paper. A robust counterpart of the multi-objective model presented in this paper was solved by epsilon constraint in CPLEX and by two metaheuristic algorithms, NSGA-II and PBMOSA in the larger size model. The Taguchi settings were applied to both algorithms, and each algorithm was run 20 times with its parameters adjusted to compare efficiency. After comparing 20 executions of the two algorithms using four criteria, it was found that the NSGA-II algorithm performed better. However, there was no significant difference in such an advantage. In addition, the results of the algorithms were compared under deterministic and non-deterministic conditions. In deterministic conditions, objective functions were better, as expected.Practical implications: In this research, a method was proposed to manufacturing companies to plan and make decisions such as distribution locations, appropriate discounts and determining the optimal route. Before this study, in the distribution chain company, only costs and profitability were considered, while according to the concerns of this company, sustainability issues were considered for the first time. Because in this research, location and routing were both considered, social and environmental issues were examined, which are directly related to the issues. Also, before this research, the uncertainty in demand was not considered, which imposed costs on the company. However, by considering the uncertainty, the profit and cost got closer to reality and made the research more practical.Social implications: Since in this paper, the social dimension of the sustainable supply chain was considered as a job opportunity and life risk issue, it seems that this research has a significant social impact.Originality/value: According to the literature, this research ensured the three levels of decision-making in the supply chain, i.e. strategic, tactical, and operational, in terms of location, inventory, and routing. Also, the uncertainty of the supply chain was taken into account, and the expiration of the medicine was considered. Both time-dependent and quantity discounts were considered in the model and all three dimensions of a sustainable supply chain were taken into account.

The study aims to present a mathematical model for the reduction of the system costs through the proper location of the required warehouses and the routing of vehicles that carry the products from the warehouses within a time window.

With regard to the specific nature of a location-routing problem, the consumption of fuel and the depreciation of vehicles are directly affected by the distance covered. The model proposed in this research seeks to minimize the undue length of the distance that vehicles have to travel. Moreover, for the approximation of the model to real-world conditions as much as possible, the concept of ‘time window’ is employed to determine the maximum allowable time for the distribution of goods.

Three metaheuristic algorithms including NSGA-II, PAES and MOICA are used to solve the proposed model. To evaluate the efficiency of the solutions, several problems of different sizes are introduced and solved, and then the results are compared in terms of the SM, MID and QM criteria. The comparative results suggest the superiority of the MOICA algorithm for big-size problems.

Setting a time window for the reduction of the distance traveled by the vehicles gets the model close to real-world conditions. It also makes it possible to estimate the costs more accurately.

The aim of the present research was to optimize the competencies of strategic managers in state organizations. This applied research was conducted quantitatively to collect the data. A researcher-made questionnaire was used to determine the level of consensus among respondents on the priority of the proposed indicators for the competency of strategic managers. The relationship between the components of the metaheuristic model was determined by 122 senior and middle managers who were selected by random stratified sampling from the studied organizations: Tehran Governor's Office, Administrative and Recruitment Affairs Organization, Supreme Audit Court, Welfare Organization, Iran Health Insurance Organization (IHIO). Data were analyzed using the metaheuristic approach method based on genetic algorithm and decision tree, using the WEKA and RAPIDMINER software programs. Based on the results of the Shannon’s method, ethics earned the highest priority. Furthermore, 11 out of the 16 extracted factors affecting competency were selected as the optimal characteristics of strategic managers in state organizations using metaheuristic algorithms. These were knowledge competencies, skill competencies, basic competencies, psychological competencies, ethics, general competencies, interactions and communications, resource management, strategic thinking, transformism, and leadership. The findings also show that the most effective factors must be examined to identify and evaluate the competencies of strategic managers in state organizations.

Nowadays, recommender systems have reshaped the ways of information filtering between websites and the users in order to identify the users’ interests and generate product suggestions for the active users. Recommender systems are generally divided into three groups: Content-based, Knowledge-based, and collaborative-based, and in some cases hybrid. The main idea of collaborative filtering is that they predict a user’s interest in new items based on the recommendations of other people with similar interests. This Approach does not require having knowledge about items. Collaborative filtering has two main types: Memory-based and Model-based. Memory based Collaborative filtering makes use of user rating dataset to compute similarity index between set of users or set of items. The main purpose of this article is to offer a Memory-based Collaborative recommender system in order to optimize the results of Collaborative filtering algorithm. In the proposed method, the combination of fuzzy Grey Wolf Optimizer algorithm and Lion Optimization Algorithm is used to find the most similar users to the target user. The results of the proposed method confirmed a significant increment in Precision, Recall and F-measure in comparison with baseline methods.

Prediction and analysis of stock market movements are an important topic for researchers, traders and have got an important role in today’s economy. Variety in policies, such as government policies and economic policies affect the stock market and cause stock price changes. The predicting stock price movement on a daily basis due to the non-linear and chaotic stock price movements is a difficult task. There are several ways for predicting in stock market. Artificial intelligence techniques have been widely used to predict data with nonlinear and chaotic structure. One of these techniques is neural network. If neural network is trained correctly, then it has minimum error in predicting. In this research, we will train the multi layer perceptron neural network with 8 meta heuristics algorithms and we predict Tehran Exchange Dividend Price Index (TEDPIX). The Results show that grey wolf optimization has the minimum error in training of neural network.

In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have been considered. The main innovation of this research is to consider the failure and repair rates of components with non-exponential distribution function in the process of optimization. The parameters affecting the under-study system in this paper make it impossible to calculate the availability using mathematical relations. Therefore, the present study has used simulation method to calculate system availability. Since the simulation has no optimization capability, this research tries to represent the results of the simulation as a mathematical function, which explains the way decision variables affect the system's availability. Further, due to the high degree of difficulty of developed mathematical function, the genetic metaheuristic algorithm was used to solve it. Finally, the efficiency of the genetic algorithm was measured against particle swarm algorithm and simulated annealing algorithm. To compare fairly, the parameters affecting the algorithms are adjusted using the Taguchi method and the algorithms are in their best practice. The computational results prove the high ability of the genetic algorithm in optimizing the concerned problem.

This paper proposes a bi-objective model for the waste collection problem and considers the location, routing and inventory of the system simultaneously. Considering the reverse flow of the system is another feature of the current study. In the proposed model, the total costs of the system are minimized. In addition, the related risks of opening new centers and transportaion are included as the second objective function of the problem. Considering the delivery time and cpacity of vehicels constraints, are the other features of the model. Due to the NP-hardness of the model, two metaheuristic algorithms namely a non dominated sort ordering genetic algorithm (NSGA-II) and a multi objective particle swarm optimization algorithm (MOPSO) are applied to solve the problem. According to the results, NSGA-II is able to reach better answers in all the propsed metrics. According to sesitivity analysis, foreign transportation fleets make a great impact on the costs of the system.

Decisions on the process of assessment and selection of suppliers should be made by examining all possible options, otherwise the organization will encounter many problems during the implementation and implementation phases.The purpose of the present study was to determine the indifference points of assessors of the water and wastewater company in Zanjan province.The method of this study was descriptive. The data of this study is related to supplier assessment of one of the projects of the city water and wastewater company Zanjan province.The data was collected based on the views of 10 experts with at least a bachelor's degree and at least 5 years of work experience in the company based on the "supplier assessment form".The data has been analyzed using the 2014 version of MATLAB software.A total of 10 cases of matrix matched with the initial decision matrix are identified and generated separately for each method.TOPSIS-GA = 2 and TOPSIS-PSO = 3 and AHP-GA = 2 and AHP-PSO = 3. A total of 10 cases of matrix matched with the initial decision matrix are identified and generated separately for each method.

The process of evaluating and selecting suppliers should be done by examining all possible options and scenarios for each contractor, otherwise the organization will face a lot of difficulties in the implementation and implementation phase of the commitments. The purpose of this study was to determine the indifference points of the suppliers of gas companies in Zanjan province.This study is descriptive. The data of this study is related to the evaluation of suppliers of Zanjan province gas company projects. The data was collected based on the views of 10 experts with at least a bachelor's degree and at least 5 years of work experience in the company based on the "supplier assessment form". The data has been analyzed using the 2014 version of MATLAB software.A total of 6 items of matrix matched with the initial decision matrix are identified and generated for each method.TOPSIS-GA = 2 and TOPSIS-PSO = 3 and AHP-GA = 2 and AHP-PSO = 3. From the results of the ranking of options, the third-party contractor has ranked first among the other options. Depending on the indeterminate matrices identified, different scenarios are set for the third contractor. Considering the company's budget and expectations that are more in line with the fourth indifference point (OUT PUT-5-AHP-PSO), the third contractor is being asked to strengthen his capabilities so that he will succeed in the contract with his chance for future cooperation. Increase.

Portfolio optimization is one of the most important issues in investment. Harry Markowitz was the first person who applied risk with this regard. This issue was later studied from different perspectives, using various risk measures, optimization methods, and considering transaction costs. In this research, we aim to use the Krill Herd metaheuristic algorithm in portfolio optimization, and examine its possible advantages. In the present study, we try to solve the portfolio optimization problem and to find the efficient frontier using Krill Herd’s novel algorithm. We also consider three different measures for risk: variance, semi-variance, and expected shortfall. Our data consists of adjusted returns of the top fifty stocks in Tehran Stock Exchange from 2012 to 2018. Atfirst, the efficient frontiers of the optimal portfolios, using different measures for risk were plotted. The relative similarity of the three plots indicates the stability of the Krill Herd Algorithm in obtaining efficient frontiers. Then, we observed that the Sharpe Ratios of this algorithm are higher than those of Imperialist Competitive and Particles Swarm Algorithms. The Krill Herd Algorithm has a better performance finding efficient frontier and optimized portfolios in comparison to the other common algorithms; therefore, it can be used instead of the other algorithms to obtain better results