برنامه ریزی چند هدفه

Generally, selecting an investment portfolio with appropriate returns that is also secure and auditable has been one of the issues raised in recent decades. For this purpose, the present research proposes an appropriate approach using ideal and anti-ideal values, ideal values, as well as maximum deviations of each objective, considering the sample in the examined market, fuzzy goals, interval fuzzy values for each asset, and their combination with satisfaction functions, fuzzy ideal planning, and weighting objectives using expert decision-makers' opinions, as well as the development of fuzzy basic weighting method. It seeks to select an investment portfolio in the digital currency market.

In this research, a new approach to selecting an investment portfolio based on uncertain data and multi-objective uncertain planning is proposed, and ultimately, the proposed approach is implemented in the digital currency market for portfolio selection.

The results of the present study show that the proposed model of investment portfolio compared to the base model not only led to higher returns but also had higher audibility and better risk control. In other words, the proposed model outperformed the base model in all the objectives under study.

Originality/Value:

 As distinguishing features of the proposed model of this research, one can mention: 1) constructing and using fuzzy distribution functions and calculating ideal values and expected ranges for all desired objectives considering the conditions of the examined market research using simple mathematical modeling, 2) utilizing the experience of financial market experts in planning model for selecting suitable investment portfolios in emerging financial markets, 3) presenting an approach to calculating portfolio risk in conditions of information scarcity in the problem environment using fuzzy theory, 4) development of the fuzzy benchmark-criterion method for weighting the objectives under study in the problem considering the expertise of financial market experts, and 5) simple modeling, considering interval fuzzy values in the model, and being usable for all individuals with different levels of investment knowledge.

The paper presents a new mathematical model for the design of a general reverse logistics network. This model minimizes system operating costs and environmental impacts through multi-objective mixed integer programming. Due to the importance of reverse logistics in waste management, the goals of this mathematical model include minimizing the costs of the logistics system and reducing the emission of environmental pollutants. In the reverse logistics system, the processes of collection, restoration, recycling, energy recovery and disposal of used products operate as an independent network. This system examines the physical flow from consumers to primary suppliers and includes the process of designing, implementing, controlling and maintaining the flow of raw materials, parts, finished products, inventories and funds. In order to solve this mathematical model, the normalized weighted sum method has been applied. The results of solving the mathematical model show that by changing the weight of the objectives, the first objective function has a decreasing trend and the second objective function has an increasing trend, which confirms the conflict between the objectives of the mathematical model. The use of this model in organizations leads to better use of resources and providing better services to customers. It also creates a better public image for companies by emphasizing environmental responsibilities.

Determining efficient solutions of the Interval Multi Objective Linear Fractional Programming (IMOLFP) model is generally an NP-hard problem. For determining the efficient solutions, an effective method has not yet been proposed. So, we need to have an appropriate method to determine the efficient solutions of the IMOLFP. For the first time, we want to introduce algorithms in which the strongly and weakly efficient solutions of the IMOLFP are obtained.

In this paper, we introduce two algorithms such that in one, strongly feasible of inequalities and in the other, weakly feasible of inequalities are considered (A system of inequalities is strongly feasible if and only if the smallest region is feasible, and a system of inequalities is weakly feasible if and only if the largest region is feasible). We transform the objective functions of the IMOLFP to real linear functions and t‎hen convert to a single objective linear model and then in each iteration of the algorithm, we add some new constraints to the feasible region. By selecting an arbitrary point of the feasible region as start point and using the proposed algorithms, we obtain the strongly and weakly efficient solutions of the IMOLFP.

 In both proposed algorithms, we obtain an efficient solution by selecting the arbitrary points, and by changing the starting point, we obtain a new point as the efficient solution.

In this research, for the first time, we have been able to obtain the strongly and weakly efficient solutions of the IMOLFP.

Because the blood is one of the most important requirements of each patient in different critical conditions, one of the concerns of the health centers is facing the blood shortage or blood corruption. Therefore blood supply chain management tries to fill the gap between suppliers and consumers, which causes failing to counter the shortage and minimizing the blood product corruption and the costs.
In this paper, this has been tried to solve mentioned problems by establishing new and appropriate blood bases, and building supporting bases and using available equipment such as moving bases and buses for receiving the blood in the East Mazandaran, in order to minimize mentioned problem to a desirable range. Thus, a mathematical programming model for this problem is designed. The first objective function is to minimize the maximum shortage of blood in hospitals during the period. The second objective function is to calculate the maximum coverage period of blood donors. The third objective function is to minimize the transportation cost among various nodes, blood inventory maintenance cost and building new regional blood centers. The proposed model has been studied using GAMS software and Pareto solution.

In this paper, a robust multi-objective model to optimally control the lead time of a complex assembly system is introduced. The system is modeled as an open queue network, whose service stations represent manufacturing or assembly operations. It is assumed that the products arrive independently according to a Poisson process. In each service station, there is either one or infinite number of servers with exponentially distributed processing time. Each service station produces uncertain but limited number of scraps which are independent from the other service stations. The transport times between the service stations are independent random variables with generalized Erlang distribution. Addressing the data uncertainty, the problem is formulated as a robust multi-objective optimal control problem that involves three types of conflicting objective functions. The types of objective functions are the total operating costs of the system per period, the average lead time and the variance of the lead time. Finally, we use the goal attainment method to solve a discretetime approximation of the original problem to obtain the optimal service rates of the problem.

This paper is a research on the ordering planning of a supply chain with one supplier, multi buyer and multi deteriorating items. Items have fixed life time and expire dates. The ordering period is taken as variable and must be computed the proposed models. The problem is studied via two different approaches the first is the joint replenishment, based on the Hwang and Chew's model [3] and with some new assumptions. In the case that the life time is less than the ordering period, the cost of deterioration is considered in the model. The second approach is cooperative planning. In this approach transportation cost, inventory and shortage costs are considered in the proposed model. The models are studied for both single and multi period cases. The purpose of the models is an investigation about the influence of expire date on ordering period's lengths. The proposed models can compute these lengths and also the amounts of orders.

Working in noisy job shops and doing hard works reduce the efficiency of workers and the resulted injuries impose cost on such an industries. Since, there are different jobs and working environment within a factory, it is possible to reduce job related injuries through a suitable job rotation program. In this paper, we propose a model for determining the job rotation program that is a bi-objective model applied to minimize daily noise exposure index as well as minimizing job severity index due to lost working days. The Global criterion “Lp- Metric” Method is proposed as the solution methodology. Finally, the model is applied in different cases and the results indicate better utilization compared to single objective models.