meta-heuristic algorithm

Urban development and the increase in intercity and intracity transportation have led to a significant rise in transportation costs. This rise in cost increases the final product price and consequently the product's market price. Additionally, the economic aspect is not the only concern; increased transportation results in higher CO2 emissions. These issues have driven the design of various transportation and vehicle routing models, including the vehicle location-routing problem. Facility location is a strategic decision due to the high costs associated with constructing and locating facilities. On the other hand, routing decisions depend on location decisions and are considered mid-term and short-term decisions.In this study, a mathematical model for vehicle location-routing under uncertainty conditions and considering reliability is developed to simultaneously optimize sustainability objective functions while maintaining minimum reliability levels. The sustainability objectives in this study include minimizing total costs, minimizing CO2 emissions, and maximizing job opportunities based on integrated strategic and tactical decisions. The primary focus of this paper is decision-making for optimal transportation routing, considering time windows and the reliability of facility location based on their failure rates.

Given the uncertainty of the mathematical model parameters, various fuzzy and robust possibilistic programming methods were employed to formulate the model. Four different models were used to manage the uncertainty of demand parameters and transportation costs, and the results were compared. Additionally, two methods—an exact solution approach and a meta-heuristic algorithm—were employed to solve the multi-objective mathematical model. The enhanced epsilon constraint method was used to analyze small-scale mathematical models and conduct sensitivity analyses, while the NSGA-II algorithm was applied to solve larger-scale numerical examples. Furthermore, an initial solution based on prioritization was utilized for the meta-heuristic algorithm.

The results indicate that although an increase in uncertainty rates leads to more job opportunities, it also raises total costs and greenhouse gas emissions. Additionally, the analysis reveals that the RPP-III method achieves the highest model robustness cost with the lowest standard deviation. In the reliability analysis, it was observed that higher facility failure rates result in an increased number of located production and distribution centers. This, in turn, leads to higher total costs, increased CO2 emissions, and more job opportunities. By analyzing 15 numerical examples, it was also found that NSGA-II is highly efficient in solving the mathematical model compared to the enhanced epsilon constraint method.

The findings of this research assist managers in making strategic decisions such as facility location and tactical decisions such as vehicle routing in the context of market uncertainty. Given that the model incorporates various realistic decisions and assumptions, the mathematical model can be effectively applied in distribution industries, particularly for pharmaceutical and electronic goods.

Recently, the supply chain of perishable goods, have been considered due to their impact on human life. On the other hand, in the packaging industry, considering paper as a primary and perishable material due to the nature of water absorption, severe rotting in front of sunlight, flammability and turning to ash and finally affecting the final quality of the product produced from this issue. It is not excluded, it has attracted more attention. The high level of speed of changes and ambiguity in decisions has made it impossible to predict the future conditions of supply chains. Therefore, the design and use of a mathematical model for the design of the closed-loop supply chain network, taking into account the optimal pricing of products, the return rate and demand, along with taking into account the wastage of materials in the system, is strongly needed. At first, the supply chain of the paper-cardboard industry was designed and modeled with mixed integer programming, then due to the high volume of calculations and data of the problem, we can't receive exact solution approaches, the innovative approach of searching for harmony was used for the solution. . The problem is a single-objective model that minimizes system costs by considering environmental considerations. The present research shows that the price increase has a positive effect on the product return rate and reducing the level of product corruption. Finally, to validate the model, the numerical solution of a closed loop network has been done in this industry.

Environmental pollution has become one of the most important human concerns. Today, the high volume of industrial waste created by military products, environmental pressures and lack of resources have promoted the manufacturing industries in this field to move towards the implementation of a closed loop supply chain. The main goal of this article is to design a mathematical model to empower the environmental capabilities of the closed loop supply chain. This research is an applied research in terms of purpose and a quantitative research in terms of data and information collection and analysis method. Therefore, a fuzzy mathematical programming model has been designed. The presented model is a 4-objective model, the first objective of which is to minimize emissions, the second objective is to minimize environmental waste, the third objective is to minimize cost, and the fourth objective is to minimize the risk of raw material supply. After designing the model, validation of the model has been done by solving it in small dimensions and then using four algorithms NSGAII, MOPSO, MOACO, MOSA to solve the model in medium and large dimensions and its results have been compared. Based on the results of sensitivity analysis of the model, the response of the model to different parameters has been investigated. The results show that the simultaneous consideration of environmental, economic and risk dimensions in the parameters leads to the improvement of the performance of the closed loop supply chain in terms of empowering the environmental capabilities and profitability.

In the realm of green supply chain management, the pivotal responsibilities of companies involve supplier selection and order allocation. These tasks were treated as independent challenges in prior studies. This issue holds significant importance within the context of green supply chain management, demanding the simultaneous consideration of both quantitative and qualitative criteria. These criteria, at times conflicting, necessitate an in-depth analysis of trade-offs. There is a pressing need for a comprehensive framework capable of concurrently addressing both aspects. In addition to accommodating green criteria, such a framework should enable the determination of order quantities allocated to suppliers.

The utilized approach in the present study included Fuzzy DNP, Fuzzy TOPSIS, and a Bi-objective optimization model. In the first stage, the criteria weights, encompassing both traditional and green criteria, were determined using the fuzzy DNP. Subsequently, the suppliers' scores were computed using the fuzzy TOPSIS. In the second stage, a non-linear bi-objective model was formulated, aiming to minimize the total cost and maximize the purchased value. The first objective function comprised the purchase price, suppliers' variable costs, ordering and setup costs, as well as buyer's and suppliers' inventory holding costs. Additionally, the second objective function ensured a higher allocation of orders to suppliers who had achieved a superior score in the supplier evaluation process. The model's constraints encompassed the suppliers' capacity, buyer's demand, and constraints associated with the suppliers' discounts. Acknowledging the bi-objective nature of the mathematical model, the Global Criterion method was employed to transform the bi-objective problem into a single-objective one. According to this method, the relative deviations of the objectives from their corresponding single-objective optimal values were minimized. Furthermore, due to the non-linearity of the mathematical model, a harmony search algorithm was developed for the problem, and a repair algorithm was proposed to handle constraints in the problem.

The previous studies and expert opinions were utilized to identify the criteria, encompassing both traditional and green criteria. The results of fuzzy DANP indicated that the supplier's credit holds the utmost importance, followed by quality, green purchasing, green design, green transportation, green production, on-time delivery, and distance. Furthermore, all criteria exhibit an effect on the supplier's credit. Subsequently, six suppliers underwent evaluation based on the criteria using fuzzy TOPSIS, and their final scores were employed as the suppliers' weights in the second objective function of the mathematical model. To validate the proposed approach, a case study with real data in the field of the medical equipment supply chain was examined, and the optimal solution to the problem was presented. Subsequently, by adjusting the weights of the objective functions in the single-objective problem, the Pareto solutions were identified. The proposed approach facilitates the optimal allocation to the suppliers by considering the green criteria.

The results showed that a decision maker can choose the best solution according to the Pareto solutions in such a way that there is a correct trade-off between the supply chain costs and purchased value.

As economic enterprises that seek to earn a profit, banks collect and accept financial resources from depositors in the form of financial intermediaries. They allocate the resources to economic activities (investors) by providing various facilities. Therefore, banks play a very sensitive and important role in the economic system of a country and have a significant effect in regulating the economic relationships in society. Banks need to pay attention to the accurate allocation of facilities and resources. Accordingly, in today's financial landscape, effective asset and debt management has become a crucial component for the success of banks and other financial institutions. It is now essential to optimize the provision and allocation of financial resources in the banking system to reduce the costs of collecting financial resources and increase income via the distribution of financial resources in the form of various facilities. Introducing a multi-objective mathematical model and considering the limitations of the problem, this study seeks to present a mathematical model for equipping and optimal allocation of financial resources.
After reviewing the literature and previous studies conducted in the field of the research topic, using the opinions of banking experts, the researchers defined the objectives of the study as maximizing the income from the allocation of resources and minimizing the costs from the collection of resources. The first objective was defined to maximize the income from the payment of facilities (allocation of financial resources) and the second objective was defined to minimize the costs of the collection of financial resources (equipment of financial resources). The objectives were determined according to the rules and regulations of the Iran Central Bank as well as the financial regulations governing the business of the Iranian banking system. After defining and coding the model, the problem was solved using meta-heuristic algorithms. Finally, the effectiveness of the algorithms in providing the desired answer was assessed. Due to the NP-HARD nature of the problem, three meta-heuristic algorithms NSGAII, MOPSO, and CUCKOO were chosen to solve the problem. Due to the high efficiency of the MATLAB program, this software was used to perform the coding and calculations.
The results of the research indicated that the credit and operational risks of the bank will be reduced in case the banking system's resources are equipped and allocated scientifically. The achieved results from the evaluation of the algorithms confirmed the superiority of the mass particle swarm algorithm (MOPSO) over the other two algorithms. Long calculation time was found to be one of the disadvantages of genetic algorithms. Also, in the evaluation of various indices, the cuckoo algorithm did not demonstrate any significant advantage over the other two algorithms under scrutiny.
The results showed that thanks to its optimal allocation of financial resources, the model presented by this study can significantly increase the profitability of banks.

With the importance of lending in the banking industry, it is very important to use the indicators affecting credit to decide on lending. The purpose of the present study is to identify and prioritize the effective features in customer accreditation using the viewpoints of bank experts in Kerman and to compare them with existing indicators in models extracted from Meta-Heuristic and Artificial Intelligence methods. The aim is to find out whether there is a match between the human views that arise from knowledge and experience and the views of artificial intelligence that look at the problem as black-box modeling. Required data were collected by questionnaire method and Quantum Binary particle swarm optimization algorithm and analyzed by Delphi. The results show that the selected indices have 80% overlap between the two methods. Due to the results of research and high accuracy of artificial intelligence techniques, it is suggested that in order to give credit to customers in banks and financial and credit institutions, to consider a higher weight for these indicators.

Due to the increase in the volume of data and information in recent years, the issue of choosing the most appropriate feature for decision making has become very important. Classic attribute selection methods cannot work well on big data. Because feature selection is a complex problem, it seems appropriate to use meta-heuristic algorithms to solve this problem. In this paper, a new meta-heuristic algorithm inspired by nomadic migration to solve the feature selection problem is presented. This algorithm is named in honor of the Qashqai tribe. In this hybrid algorithm, the proportional function was designed based on the feature selection algorithm and based on minimizing the number of features and the amount of data error using neural network results. Then the Qashqai meta-heuristic algorithm was implemented on this fitness function and the results were compared with the well-known meta-heuristic algorithms of genetics and particle swarm. The results of the hypothesis test showed that the Qashqai optimization algorithm to solve the feature selection problem by the genetic algorithm and particle swarm is not defeated and in terms of convergence to the optimal solution works well.

The main solution for companies to simultaneously achieve economic and environmental goals is to implement a closed loop supply chain. The main goal of this research is to design a mathematical model to empower the environmental capabilities and profitability of the closed loop supply chain in military industries. The presented model is a 4-objective model, the first objective of which is to minimize emissions, the second objective is to minimize environmental waste, the third objective is to minimize cost, and the fourth objective is to minimize the risk of raw material supply. After designing the model, validation of the model has been done by solving it in small dimensions and then using four algorithms NSGAII, MOPSO, MOACO, MOSA to solve the model in medium and large dimensions and its results have been compared. Based on the results, the parameters of the model have been adjusted and the response of the model to different parameters has been investigated. The results show that simultaneous consideration of environmental and economic dimensions in the parameters leads to the improvement of the performance of the closed loop supply chain in terms of empowering the environmental capabilities and profitability.

When it comes to providing aid to the victims of natural and unnatural disasters, the main goal of a relief chain is to provide the items needed by the victims such as water and food, medicine, shelter and other necessities to reduce the number of deaths caused by reduce the occurrence of disasters as much as possible; therefore, designing, developing and implementing a relief chain can play an important role in finding a suitable answer. The most obvious differences in dealing with the relief supply chain are the unpredictability of demand in terms of time, place, type, scale and volume. Other reasons such chains are the sudden occurrence of a large amount of demand and a very short opportunity to provide a large amount of goods, lack of resources including goods, relief forces, appropriate technology, transportation capacity, the need to provide timely and sufficient supplies after the accident, and the risks in the relief environment. In the present research, a mathematical model for the location-inventory problem for planning response to casualties is presented; also, due to the NP-hard nature of the problem considered, meta-heuristic algorithms were used to solve it.

In today's world economy, companies must focus all their activities and capabilities on customer satisfaction because customers are the only source of return on investment. Besides, customer satisfaction in supplying and distributing short-lived commodities especially food, due to their special and perishable properties, has doubled the importance of the issue. When a vehicle carries the demand of a number of customers in one shipment, due to the long travel time and the frequent opening of the refrigerator door, the quality of the remaining products in the vehicle decreases and, as a result, the satisfaction of customers reduces. The main purpose of this article is to maximize customer satisfaction in the food supply chain network. The study integrates the decisions of the different parts of a food supply chain under uncertainty. The first part includes food suppliers. Because the studied supply chain is multi-commodity, one supplier is not able to supply all the food. Therefore, it can supply part of the customer needs according to its conditions and expertise. The second part includes the heterogeneous transport fleet, which serves as a VRP problem. Thus, a vehicle can receive food from a supplier and deliver it to customers located in different geographical locations. In addition, the preparation time of vehicles is also considered as a constraint. The transport fleet consists of several refrigerated vehicles with different carrying capacities and speeds. Since one vehicle is not able to carry all the orders, each product, according to the required temperature and storage conditions, must be transported by vehicles specific to that product. Also, due to weather and traffic conditions, the vehicle travel time is not definite. So, in this study, the uncertainty of vehicle travel time (triangular fuzzy) is taken into account too. The third part of this supply chain includes end users, whose geographical location and the amount of demand of each is definite and specific. There is a time window like (x,y) for each user. If the orders are delivered to the customer before time x, it will cause earliness. If it is delivered after time y, it will cause tardiness. The objectives of this study are minimizing the sum of tardiness and earliness of deliveries to customers and maximizing the quality of products delivered to them. A mathematical model of the problem is presented, and the augmented ε constraint method is used to solve the model. It has been shown that the exact solution method cannot solve large-scale problems within a reasonable time. Therefore, meta-heuristic algorithms should be used. This research has presented the MOTTH meta-heuristic algorithm, which is a new development of the genetic algorithm and is inspired by the long-standing human desire to travel throughout history. In this algorithm, the best solutions of the current generation replace the worst solutions of R generations, and, thus, the algorithm’s premature convergence is prevented and more solutions are searched in the solution area. As the mathematical model was solved through the augmented ε constraint method, the relationship between the two objective functions was explained. It was also shown that an increase in the quality of the products delivered to customers leads to a rise in the sum of the tardiness and earliness of deliveries to customers. Therefore, considering the importance of each of the objective functions, every company should establish a balance between the two objective functions.For validation, the results of the NSGA-II and MOTTH algorithms were compared with those of the exact solution of the augmented  constraint method. The results of the algorithms were also compared. It was shown that the MOTTH meta-heuristic algorithm performs better. For the mathematical modeling of this research and for solving the model, the resources available in the literature and the GAMS and MATLAB software programs were used respectively. According to the results of this study, a practical suggestion for frozen food supply chain managers is to use the MOTTH algorithm. This algorithm offers better solutions than the NSGA-II algorithm, the sum of tardiness and earliness of deliveries to customers is less, and the quality of the products delivered to customers remains higher. Moreover, the use of frozen food trucks partitioned with separate doors and equipped with cooling systems is another practical suggestion of this research; if the door of a partition is opened and the products are emptied, the other products in the other partitions will not receive a heat shock and their quality does not decline.

Finding a subset of features is an issue that has been widely used in a variety of fields such as machine learning and statistical pattern recognition. Since increasing the number of features increases the computational cost of a system, it seems necessary to develop and implement systems with minimum features and acceptable efficiency.

Considering objective, it's developmental research and in terms of two Meta-heuristic algorithms, namely genetic algorithm (GA) and multi-objective non-dominated sorting genetic algorithm (NSGA II). The multi-objective method compared to the single-objective method has reduced the number of features to 50% in all instances; it doesn't make much difference in classification accuracy. The proposed method is applied on six datasets of credit data, and the results were analyzed using two common classifiers namely, support vector machine (SVM) and K-nearest neighbors (KNN). Comparing two classifiers applied on datasets, K- nearest neighbors (KNN) compared to the support vector machine (SVM) has shown relatively better performance in increasing the classification accuracy and reducing the number of attributes.

Genetic algorithm and multi objective non-dominated sorting genetic algorithm have a good performance in increasing the accuracy of classification and reducing the number of attributes in feature selection problem of multi-class data. The results also indicate an increase in classification accuracy, simultaneously with a significant decrease in the number of features in both KNN and SVM methods.

According to the results, the proposed approach has a high efficiency in features selection problem.

Today, platforms success cause platform business model changes to a successful business model in world level. But thing causes platform valuable is facilitate interactions that in this regard analysis and study of customers and servants’ expectations as customers of platforms is more important. The goal of research of customers’ expectations model design (customers and servants) is using meta-heuristic algorithm and factor analysis in order to platform development and grows. So, this research is a development and applied as view of purpose and has had a descriptive-survey research as identity and method that is a total action of inductive research. Research has two statistical societies include experts, universities masters and researchers in this area and also platform Iranian customers and servants that has used of two methods of judgmental sampling (snowball technique) and simple random. At first, for finding customers and servants expectations used of semi-structure interview and then was done automatic clustering with using meta-heuristic algorithms and the result shows that customers were include customers conclude two clusters with full of expectations and based on interaction and indifferent customers in all expectations dimensions. Its servants also include two clusters of customers with full of expectations and based on trust and indifferent servants in all expectation’s dimensions.

Hub location-routing problem is a practical subject in the last decades. This study considers a many-to-many hub location-routing problem where the best locations of hubs and tours for each hub are determined with simultaneous pickup and delivery. First, an optimization model is proposed to minimize the total sum of fixed costs of locating hubs, the costs of handling, traveling, assigning, and transportation costs. To find practical solutions, the hubs have constrained capacity, in which single allocations can service every node to the hubs. What is more, the balancing requisites are imposed on the network by allocating the appropriate number of demand nodes to the hubs. Then the problem is solved using GAMS software for small-size instances of the problem. Due to the NP-hard nature of the problem, the proposed optimization model is solved by the Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). For the problem instances, the comparative results indicate that GA has a better performance compared to ICA, and incorporating capacity and balancing considerations can influence the reduction of costs of the investigated network.

In this paper a stable multi-objective model of location,inventory,supply chain routing in conditions of uncertainty with a passive defense approach is presented.In the studied model,parameters such as demand.facility establishment cost and inventory maintenance cost are considered as indefinite and in the form of triangular fuzzy numbers.Also, in order to increase the resilience of the supply chain,the characteristics and capabilities of passive defense in the supply chain such as"ready flow rate","security of backup routes"and "possibility of deployment of resources and equipment" and the principle of dispersion for location were considered.First, a fuzzy multi-objective mathematical model was designed and then a stable supply chain model with a passive defense approach, using methods such as LP metric, a unique single-objective model, was generated and solved in Gomez software environment for small problems. The presented mathematics was validated. After checking the validity of the model, multivariate multi-objective particle mass algorithms based on Pareto archive and NSGA-II were used to solve the model. Sample problems in three groups of small, medium and large were solved by two algorithms MOPSO and NSGA-II and the results of the two algorithms were compared based on quality, scatter,uniformity and execution time.The results of this section showed that the multi-objective particle mass algorithm in all cases has a higher ability to produce higher quality solutions and explore and extract the scalable area of the answer than the NSGA-II algorithm.Also, the comparison of the execution time of the algorithms indicates that the multi-objective particle mass algorithm has a higher solution time

In the real world, businesses with hybrid flow-shop manufacturing environment have faced the constraints in machinery, human resource constraints and increasing salary and have tried to use their human resource better. Given the limitations of these resources, customer delivery requirements have made the job rejection essential in order to meet distinct customer requirements. Therefore, this paper considers the dual resource constrained hybrid flow-shop scheduling problem with job rejection in order to minimize the total net cost (the sum of the sum of costs obtained from rejected jobs and total penalty cost) which has high practical and operational relevance in many industries. The present study is developed a new mixed integer linear programming model for the problem. In addition, a new improved sooty tern optimization algorithm (STOA) is proposed to solve large-size problems as well as a new decoding method due to the NP-hardness of the problem. In order to evaluate the proposed optimization algorithm, 5 well-known algorithms in the literature including (Immunoglobulin-based artificial immune system (IAIS), Genetic algorithm (GA), Novel discrete artificial bee colony algorithm (DABC), Fruit fly optimization (IFFO), Effective modified migrating birds optimization (EMBO)) are adapted with the proposed problem and finally the performance of the proposed optimization algorithm is investigated against the adapted algorithms. All results and evaluations show the good performance of the new improved sooty tern optimization algorithm.

Multi-criteria decision making methods have been of the most popular and practical areas in recent years. Among these methods, the AHP method has been considered by many researchers due to its unique features and has been used in numerous studies. This method facilitates decision-making for the decision-maker by allowing pairwise comparison between alternatives using language expressions. But this feature causes inconsistency in the decision matrix. One of the sources of inconsistency is the use of a predetermined scale to convert linguistic variables into quantitative values due to the different intellectual background of experts and their information about the problem. Therefore, the purpose of this study is to provide a customized scale for converting linguistic variables using the granularization of linguistic variables, which is in line with expert opinions and reduces the inconsistency. The unique feature of this framework is that the distribution of cut-off points is not known in advance and is determined according to expert opinions. To optimize the proposed model, a particle swarm optimization metaheuristic algorithm is used, which is modified to adapt to the specific characteristics of the problem. The results show the good performance of the proposed framework in reducing incompatibility.

The purpose of this paper is to design a multi-objective mathematical model in order to optimize the four-echelon supply chain of perishable goods using a hybrid algorithm with regard to procurement time, cost and customer satisfaction. Perishable four-echelon food supply chains are considered as different supply chains due to continuous and significant changes in the quality of food products throughout the chain until the end of consumption. In this research, a mathematical model for the location-routing facility in a four-echelon supply chain for perishable products with a simultaneous optimization approach of total supply chain costs, order delivery time, emissions and customer satisfaction is presented. To assess the validity of the research, the mathematical model in Behshahr food industry has been studied and the research problem is presented in the form of a multi-objective nonlinear programming model of mixed integer and to solve it, a hybrid of two refrigeration and red deer algorithms has been used. The results of the proposed algorithm are solved in a case study and the results of the algorithm performance are reviewed based on standard indicators and finally the computational results indicate the efficiency of the algorithm for a wide range of problems of different sizes.

This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times. The objective is to minimize total completion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several different parts. At first, the parts are manufactured in a flow shop stage with some different machines and then they are assembled into a final product on a single machine. This paper presents three meta-heuristic algorithms, namely Parallel Variable Neighborhood Search (PVNS), Artificial Immune Algorithm (AIA) and Simulated Annealing (SA), for solving under studied problem. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of presented algorithms. Also, Numerical experiments are used to evaluate the performance of the proposed algorithms. The results show that the PVNS algorithm performs better than the other algorithms.

Hazardous materials which are materials due to their chemical and physical properties impose significant risk to the safety of people and the environment. It's more complex routing transport of such material than normal materials. The Combination of the two subjects as the problem of locating and routing, it has created unified system to locating- routing problems. These problems determined optimal number and location of facilities at the same time and also set the optimal number of vehicles and their routes. The purpose of this study was design a network for the transportation of hazardous materials and includes supply levels, distribution (hub) and customers. Hence, it presented a mathematical model in order to minimizing costs and risk simultaneously. Hazardous materials sent from supplier to the hubs and deliveries to customers from there via routing by road transportation. It should be mentioned that in proposal model the hubs been locating In order to validate the model, prepared code GAMS In software and for the exact solution, sample problems with various dimensions were produced in the form of smart and random. For this purpose, was written an algorithm design in Matlab software. According to the problem was NP-Hard, presented a hybrid algorithm based on simulated annealing and genetic algorithms to solve large-scale. At the end of research the proposed algorithm were compared with the results of exact solution.