multi objective mathematical model

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.

The timely production of orders and their delivery to the customer is a competitive advantage that makes production systems and becomes customer satisfaction. Balancing the load of orders on work stations, reducing the time of the production period and minimizing the cost of human skills and access to machines can be components in the cases of balance of production lines. In this article, by addressing the above components of the mathematical model, several objectives for checking the online load balance are presented. Solving the mathematical model is done by introducing the combined meta-heuristic algorithm of multi-objective genetics and ant milk, this solution method selects the fittest parents based on the fitness of the initial population by using the feature of selecting the initial population and having the search memory to find quality answers at the right time. produces. The results obtained compared to the previous online load balancing methods show that the production time and load balance on the workstations have been improved.

Nowadays, business owners and entrepreneurs are constantly looking for ways to increase the flexibility of their employees to meet the goals of their organization to respond the diverse needs of their customers. Optimizing service delivery in production systems and providing appropriate work patterns can ultimately simplify service delivery and increase employee satisfaction. While job rotation is a good way to improve an organization's agility in responding to a variety of demands, addressing this issue without the right attitude will lead to issues such as running costs as well as ergonomics and employee motivation. Hence, in this study, a multi-objective mathematical model of employee scheduling with the goal of minimizing scheduling costs and personnel allocation, enhancing employee satisfaction, and job ergonomics. The Epsilon constraint method is used to solve this multi-objective optimization problem. The results of this study showed that using appropriate multi-objective models can provide optimal results in terms of job rotation tailored to employees' working conditions. Scheduling can also ultimately reduce costs and increase motivation, considering job rotation.

The aim of this study was to design a new fuzzy expert system for project portfolio risk management in ongoing projects of a first mobile holding.

To do this; To identify the risks from the Dematel method developed in Excel, to evaluate and determine the most important risks from the fuzzy FMEA methods in Excel and the AHP method in Expert Choice software; And to select the optimal risk response, a proposed mathematical model is used and solved in Gamz software.

The research findings showed that there are 23 risks in the studied projects, 8 of which were of high importance and impact. Also, risk prioritization and determination of their impact level was performed using the integrated AHP-FMEA method. Findings obtained from solving and implementing the mathematical model in Gamz software also showed that the proposed model of efficiency and capability for selecting optimal risk response strategies is established.

Originality/Value:

The general findings indicate that the proposed new fuzzy expert system also has the necessary capability and efficiency to manage the portfolio risk in the study holding.

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.

The toxic and dangerous waste material from electrical and electronic equipment is a potential threat for environment and the future of human. The users and governments forced manufactures to take a responsibility for recieving and recovering of their products. Recovering of these products has economical benefit and social benefits. Therefore a management system is essential for recovering the electronic products to maximize the social and economical benefits and minimize the environmental impact. In this paper a multi objective mathematical model is proposed based on sustainable development for selecting the proper options to recover the electronic products .Multi objectives genetic algorithm is used to solve the model. The model validation is conducted through a case study with data from industry .

This research aims at developing original components of Decision Support System (DSS) in order to support the evaluation and selection of suppliers in multi products having limited capacity. To achieve this purpose, researchers have proposed three different models based on which suppliers allow selecting their own desired type of discount. Firstly based on the performed studies, some criteria have been suggested for evaluating the suppliers’ capability. These criteria will be selected by the buyer with respect to the purchase condition and type of the industry, and their importance level will be determined. Then based on multi-criteria decision models, evaluation of all the suppliers will be done. In this study fuzzy numbers are applied so as to translate qualitative criteria into quantitative one. Suppliers have been ranked according to the scores they have gained and the selection share of each from the whole quantity has been determined based on the multi-objective mathematical model. The existing model has exerted practical ways such as taking loans with determined interest as well as certain variables in order to compensate the shortage of budget the buyer encounters with. Outputs, which are, the selective suppliers and orders assigned to each of them are also to be considered by using Genetic Algorithm in MATLAB software for three parts in Emersan Company accordingly.