meta-heuristic algorithms

Refurbishing an oil rig is one of the best alternatives to renting this capital-intensive equipment since its rental cost amounts to tens of thousands of dollars per day. This research focuses on the scheduling of activities and inventory planning in the supply chain of the Sina 1 drilling rig reconstruction project. Since no prior record of such an endeavor exists in the country, project scheduling is conducted under conditions of activity uncertainty. In addition, uncertainty in the timing of activities improves project owners' understanding of their schedule and creates a broader view of the project and future activities. In this model, contractor and supplier costs are considered simultaneously, and uncertain activity scheduling and order planning are carried out in a way that minimizes the overall cost of the chain.

Given the large number of variables and constraints in the mathematical model of supply chain activity scheduling, the problem under study is classified as NP-hard. For this reason, meta-heuristic methods are used to solve such problems, which provide near-optimal answers in less time compared to exact methods. In this study, the electromagnetic algorithm has been used to solve this problem. This algorithm has been applied to a real project (the foundation section of the Sina 1 drilling rig).

The mathematical model proposed in this research has been coded using the electromagnetic algorithm within the MATLAB software environment. The input parameters include general parameters and control parameters of the electromagnetic meta-heuristic algorithm. General parameters pertain to the specifications of the contractor, suppliers, activities, resources, and consumables. To validate the efficiency and effectiveness of the designed electromagnetic algorithm, three case problems were selected. The first problem consists of five activities; the second problem involves the same five activities but with two suppliers, while the third problem features a larger-scale scenario. First, the exact solution for each problem was obtained using AIMMS software, and then the solutions and computational times of the electromagnetic algorithm were compared with those of AIMMS. Notably, as problem size increases, the solution time in AIMMS grows significantly (exponentially). The proposed electromagnetic algorithm demonstrates acceptable performance in terms of computational time. The meta-heuristic electromagnetic algorithm solves the sample problem in 115 seconds, whereas AIMMS requires approximately 747 seconds to find the exact solution. Additionally, the relative deviation of the electromagnetic meta-heuristic algorithm from AIMMS is approximately one percent. Finally, a sensitivity analysis was conducted to examine the impact of different payment methods on the project's supply chain costs.

This research presents the modeling and solution approach for a two-level project supply chain, encompassing both the contractor and suppliers, with the objective of minimizing project and inventory costs for both entities.By comparing the results obtained from the exact method and the electromagnetic algorithm, it was found that the solution time using the electromagnetic algorithm is significantly less than the solution time in the exact method. In addition, the relative difference in the quality of the results is limited to approximately one percent. These cases clearly indicate the effectiveness and efficiency of the proposed algorithm.The results indicate that the proposed electromagnetic algorithm is a highly effective approach for this problem and converges toward an optimal solution.

The purpose of this research was to design a distribution channel selection system in the supply chain of the oil industry using the combination of adaptive neural-fuzzy network and meta-heuristic algorithms (Case study: National Oil Products Distribution Company of the dual regions of West Azerbaijan). This research was based on the purpose of the research, developmental-applicative and based on the descriptive-modeling research method. The statistical population of this research was 185 axis sales, Sekodar and fuel supply stations in the two regions of North and South of West Azarbaijan province. In this research, questionnaires and documents were used to collect data. In order to analyze the data, confirmatory factor analysis, adaptive neural-fuzzy network in basic mode and adaptive neural-fuzzy network combined with genetic meta-heuristic algorithms and particle swarm have been used. In this research, first, the combined distribution channel selection system was designed, and then using the system designed based on the least error, the traditional distribution channel and the branding plan of the fuel stations were evaluated based on the input scores. The results show that the best system for selecting the distribution channel was the adaptive neural-fuzzy network combined with the particle swarm algorithm. By comparing the performance of the branding plan and the traditional method, it was determined that the branding plan has a better performance and is a suitable distribution channel for the National Oil Products Distribution Company of the dual regions of West Azerbaijan.

Time and cost are significant factors in every project. By reducing the resources allocated to the project, project costs are reduced, while the reduction of available resources means the inability to simultaneously implement activities or activities in the shortest possible time, which in turn increases the duration of the project. This is although, in all projects, the completion of the projects in the earliest time is considered one of the important parameters of the project. Considering the highly practical application of the examined problem, project scheduling by investing multi-skill resources with the possibility of changing the skill level in fuzzy conditions can be considered a positive step towards creating project scheduling problems.                         
Design/methodology/approach: In this paper, the proposed mathematical model of meta-heuristic genetic algorithms to solve the proposed model is discussed and explained in detail. Several skills are needed to perform each activity. The goal is to optimally determine resource availability and find the best schedule by minimizing investment in resources.              
Considering the activities' need for different skills as well as the expertise of the project members in different skills, it seems obvious that each activity can be done with several different situations in terms of human resources allocation, which might be only for one activity, reaching more than 10 modes. As a result, compared to MRCPSP, this issue has a much higher complexity. The Resource Investment Problem (RIP) is a variant of RCPSP where renewable resource constraints are considered decision variables. In many projects, managers, in addition to making decisions about the time of implementation of activities, should determine the number of resources allocated to activities in each period of the implementation of activities according to the status of the project, which means ignoring the constant pattern of resource consumption for activities during their implementation.                          
Practical implications: By comparing the algorithms with the indicators of maximum extension, distance from the ideal solution, distance, and several Pareto solutions, it was found that the multi-objective genetic algorithm performs far better than the multi-objective Cuckoo algorithm regarding the criteria, distance from the ideal solution, and the largest expansion. However, in terms of the number of Pareto solutions, the algorithm is not superior to the other algorithms. Therefore, it can be concluded that the multi-objective genetic algorithm has relatively a better performance than the multi-objective Cuckoo algorithm.           
Social implications: In this research, each activity can be performed with several different situations in terms of human resource allocation, which may reach more than 10 situations just for one activity. As a result, compared to MRCPSP, this issue has a higher level of complexity. Literature review indicates that being multi-skilled increases the productivity, quality, and consistency of work and gives managers more flexibility in work allocation.              
Originality/value: One of the most important branches of project scheduling knowledge is the problem of project scheduling with limited resources. This new concept has led to the development of one of the most general modes of scheduling problems under the title of multi-mode project scheduling with limited resources, which solves many real problems and can be modeled for application.

Supply chain network design is one of the most important issues in supply chain management. Therefore, the purpose of this study is to design a closed loop supply chain network in conditions of uncertainty and solve it using meta-heuristic algorithms.

This research is of applied kind. In this research, field and library study methods were used to collect information. Considering the selection of the tire industry as the under study industry for designing the proposed supply chain network, in this study, using field research, the required data from Kian Tire, Iran Tire, Dena Tire, Barez Tire, Kavir Tire, Yazd Tire, Artaville Tire and Pars Tire were collected and used to solve the model.

The modeling of this chain is done based on environmental considerations, minimizing the total cost of the chain and maximizing the response to customer demand. Finally, this model is solved using NRGA and NSGAII meta-heuristic algorithms and the obtained results are compared with each other. Also for credit evaluation suggested model using limitation epsilon method has been implemented in the GAMS software too.

In this research, the design of closed-loop supply chain network in conditions of uncertainty and its solution using meta-heuristic algorithms is considered and based on the research results, executive proposals were presented.

In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear and NP-hard problems have dramatically increased, while new algorithms have constantly being introduced. In this research, with the aim of ranking meta-heuristic algorithms, using group decision making techniques (different from other research in this field), 5 algorithms including: GA, PSO, ABC, SFLA and ICA by 15 standard test functions, and considering 2 attribute: "mean of answers" and "run time", have been compared. Then they are ranked by 3 group decision making methods including: "Cook and Seiford", "Condorcet" and "Dodgson". In addition, as in ranking by "Condorcet" and "Dodgson" methods, sometimes some options posit the same rank, therefore, in this study; we presented a proposal to overcome the limitation. Then the algorithms with these proposed methods were ranked. Finally, the overall ranking is done using an allocation model our results show that the overall ranking is as follows, respectively: PSO, ICA, GA, ABC and SFLA.

The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.

This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry.

To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities.

Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy.Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable.

This study was one of the first in-house studies to compare the results of meta-heuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers.

In this research, a multi-objective mathematical model is proposed for the airline multi-skilled crew scheduling problem. The multi-skilled crew can be assigned to flights and airplanes according to their skills. The objective functions of the proposed model are: (1) Maximizing the number of leave days planned according to the days announced by the flight crew, and (2) Minimizing the penalty costs associated with violation of minimum and maximum working hours. Several test problems have been designed based on the data acquired by the airline studied in this research. Due to the NP-hard essence of the model, we have employed two meta-heuristics, namely the multi-objective differential evolution (MODE) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). These algorithms are calibrated using the Taguchi method. The algorithms have been compared based on several multi-objective performance measures. Each algorithm has been more successful in terms of some metrics. The comparisons between algorithms and sensitivity analysis show that the proposed model and algorithms can produce appropriate schedules for the airline crew scheduling problem.

In recent years, global concerns about environmental and social issues have made consumers, government organizations, companies and universities more active, and their focus has increasingly been on the design of the supply chain network as the most important part of the supply chain. The main objective of this paper is to present a supply chain modeling model for Hamadan Glass Manufacturing Company considering the dimensions of sustainability.

In this paper, a Fuzzy Multi-objective Mixed Integral Programming is presented to design a closed loop supply chain under uncertainty conditions in order to minimize environmental impacts and maximize social impacts and economic benefits. In this model, both the constraints and the parameters of the problem are fuzzy, which is determined by the Jimenez method, and the algorithms of NSGA-II and MOPSO have been used to solve the model.

The proposed model was solved with two multi-objective genetic algorithms and multi-objective particle swarm optimization, and the necessary comparisons were made between the results. Finally, Pareto's solutions were determined. According to the results, the two algorithms differ in the time criterion that the NSGA-II is superior to MOPSO. Also, there are two different algorithms in the MID standard that MOPSO excels over NSGA-II and does not have any significant superiority over the remaining criteria.

Based on the results of the research, simultaneous consideration of economic, environmental and social dimensions and uncertainty in some parameters such as demand and returns lead to improved supply chain performance in terms of profitability and customer satisfaction.

Vehicle routing is one of the significant issues in the field of Logistics management and despite many endeavors for integrating this issue with other fields of logistics, most of its aspects require research and development. The selection of interdependent factors in vehicle routing is one of these approaches and it can have a considerable impact on price increase, income decrease, lost sale, dissatisfaction of customers and increase in returned goods. This issue is the rewritten classic model of vehicle routing by considering the approach of selecting a customer as one of the significant approaches in the distribution management. The main idea of this article is to consider the fact that the number of requests for each region is a fixed amount and if goods were distributed among several sellers in that region, all of the requests would be divided among these sellers, which is not desirable for the sellers and results in considerable distribution expenses. Above all, the satisfaction of ultimate consumers depends on broad distribution and if distributed among more ones, there would be greater satisfaction. Based on this idea, in the present research, a new formulation from this issue in the form of a two-purpose mathematical model has been presented by putting together the classic vehicle routing and the approach of selective distribution. In this research, firstly, a review of the literature of this subject has been carried out, and then the issue has been raised. Following that, based on the limiting assumptions describing the conditions of the issue, modeling of vehicle routing with the approach of selecting interdependent factors has been considered and mathematical modeling has been presented. This model with a set of sample data has been dealt with and its reliability has been assessed.

The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting businesses, entrepreneurs and companies to pay. Accordingly, the purpose of this study was to predict the growth of small and medium enterprises with the combination of neural network and meta-heuristic algorithms. The purpose of this research was applied and based on the method of doing descriptive-modeling work. Statistical population of this research was all small and medium enterprises of Zanjan province. Statistical sample size According to the growth of companies, 158 companies has been designated. In order to collect data in this study, interviews, questionnaires and documents of companies have been used. Validity and reliability of the questionnaire were verified and and using Cronbach's alpha coefficient. In order to analyze the research data using confirmatory factor analysis methods, the neural network of multilayer perceptron, neural network combined with genetic algorithm and neural network combined with particle swarm algorithm have been used. The results show that all three methods are able to predict the growth of the company. Among these three methods, the best predictive method for growth of the company is the neural network combined with the particle swarm algorithm with the least error rate compared to the other two methods.

Investment decision, have always has been one of the most important issues. Investors are trying to achieve the highest efficiency and the least risk by selecting the best companies from Among a wide variety of companies considering to various financial indicators. Accordingly, today, there are many ways to analyze the data from this company. One of the ways is clustering that classification of the companies. However, the present study aimed to identify and distinguish successful from unsuccessful companies in Tehran Stock Exchange has been done using K-means clustering. Then this problem is solved using meta-heuristic algorithms. The results indicate that meta-heuristic algorithms compared with conventional methods, more efficient and have led to a global optimum. Also these results of Altman’s bankruptcy model were confirmed results of meta-heuristic algorithms.

The purpose of this paper is to provide a model to solve the problem of discrete resource constraints project scheduling with multi objectives of reliability, risk, time and cost of the project, taking into account the multiple crashable modes and mode-identity capabilities.

Studying the literature on the subject, a mathematical programming model for the problem is presented. Due to the NP-hardness of discrete project scheduling problems, the NSGA-II, NSGA-III and MODA, meta-heuristic algorithms are developed within different dimensions to solve the problem. After presenting the results, the comparison of these algorithms has been done using a number of multi-objective performance measures.

Using multiple crashable modes concept and mode-identity in the subset of activities, and, consequently, choosing the best mode for executing activities in each subset and also determining the number of suitable units to reduce the time span, will lead to much better results in terms of project objectives.  As a result, the reliability of the project will be maximized and the risk, the time and the cost of project completion will be minimized.

While most previous studies have mainly focused on the time and costs of the project objectives, considering the reliability and risk of the project can help projects to yield better results. In addition, the features such as multiple crashable modes and mode-identity will lead to the real world situations and also better solutions can be found.

There are several different factors in industrial processes, which may have an impact on final product specifications. In this paper, the nonlinear multi-objective mathematical modeling for the 5 qualitative characteristics of polyethylene terephthalate (PET) as one of the most widely used products in petrochemical industry is done by 9 Key parameters affecting the production process. For this purpose, Response Surface Methodology that is the combination of experiments design and statistical tests is applied along with Artificial Neural Network and Metaheuristic algorithms.

There are several different factors in industrial processes، each of which may have an impact on final product specifications. In this paper، to optimize the parameters of continuous production process and restrictions the choice of these parameters and influence factors are discussed، and conceptual model with its steps، using a combination approach of Response Surface Methodology، Artificial Neural Network and Meta heuristic algorithms is presented. as an example، modeled polyethylene terephthalate (PET) process with the 5 quality characteristics as response variables and 9 influence factors. The proposed approach makes it possible that when caused a change in the production plan which leads to a decrease or an increase of production، be able Meanwhile observe the standard of response variables (characteristics of product quality)، to determine amounts variables (the product of the adjustment of process requirements) can be.