nsga-ii algorithm

One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the organ transplant supply chain and other supply chains is the possibility of corruption of related products. Therefore, the time and also the location of organ transplant centers are of special importance. On the other hand, due to the rapid growth of the demand for organ transplantation and the lack of resources, the patient's waiting time to complete the transplantation process plays a vital role in the organ transplantation system.

This study presents a robust bi-objective mathematical model for the location problem of allocating organ transplant centers under uncertainty, which includes the total costs of the organ transplant system as well as the average patient waiting time for organ transplantation, which follows a G/G/m queuing system.

To solve this model, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been used. Finally, the applicability of this model and the efficiency of the mentioned algorithm compared to the defined indicators have been shown through numerical experiments.

Originality/Value:

 Since each organ can spend a certain amount of time outside the body and there is a possibility of corruption or a decrease in the quality of the transplant, the time between the organ leaving the body and the completion of the transplant operation plays an essential role in the transplant system.

Nowadays, increasing the quality level in production systems and reducing costs are two of the signicant goals of manufacturers. More manufacturers pay for more qualitative raw materials, more skilled labor, and more advanced and accurate machines the more waste is reduced. Increasing quality levels and decreasing costs become more complex when some parameters are under uncertainty. One of the methods to encounter uncertainties is robust optimization, where uncertainty probability distribution is unknown. As a consequence, the robust scenario-based approach, which is presented by Mulvey, is applied. In this paper, we present a biobjective scenario-based supply chain model. In this model, three echelons including suppliers, manufacturers, and customers are considered. Also, we consider uncertainty in backorder, demand, and cost values. The rst objective function aims to minimize supply chain costs including production, raw material purchasing, production inventory holding, raw material inventory holding, transportation, and backorder. The second objective function aims to minimize the total amount of raw material wastes in the production line and supplier batch. The proposed model has been dened as a multi-product, multi-period, multiple suppliers, multiple customers, and multiple transportation modes mixed-integer linear programming model. Also, in this model, workforce e- ciency, storage and transportation capacities, and inventory planning are considered. The model parameters are considered randomly distributed. The Epsilon constraint method, NSGA-II, and SPEA2 algorithms are applied to solving the proposed model. Also, the Taguchi method is applied to tune the parameters of the algorithms. Then, a comparison between the quality of results and the CPU time of these methods is provided. This comparison indicates that the use of evolutionary algorithms provides close results with the exact method in a shorter CPU time. Afterward, the Mean Ideal Distance (MID) and Analytic Hierarchy Process (AHP) methods are respectively employed to evaluate Pareto fronts performance and make a decision about selecting the best cost and quality level policy.

 Optimizing the negative balance of the financial portfolio of branches by observing the limits defined in the banking system of Iran.

 In recent years, several models have been proposed for the investment portfolio. In banks, Fundraising operations are carried out in parallel with investments. Attracting deposits and repaying loans are the main pillars of investment and form the basis of the resource and expenditure portfolio in the bank. In this research, a multi-objective planning model is designed to maximize returns and minimize risk.

 The approach of the problem is such that by taking administrative and personnel costs and interest rates on deposits and facilities and exchange rates of the domestic market can offer a variety of portfolios. The branches select the appropriate portfolio as the goal and work plan according to their requirements.

 Due to the nature of the problem, which is hard nonlinear, the model is solved using NSGA-II evolutionary algorithm. The output of solving the problem is a set of optimal solutions on the Pareto frontier. Each of the portfolios is a strategic choice for the decision-maker, according to the level of return and risk.

Unsustainable development and rapid urbanization have led to changes in the hydrological characteristics of watersheds, and the risk of flooding has been increased consequently. One of the solutions used for quantitative and qualitative control of urban runoff is green infrastructure and low impact development (LID) based approaches that have attracted the attention of many researchers. In this study, SWMM was used to simulate the rainfall-runoff process in region 1, district 11, Tehran. Six scenarios, including different combinations of several LID types such as Green Roof, Rain Barrel, Bioretention Cell, Porous Pavement, Vegetated Swale, and Dry Pond were developed. Then the SUSTAIN model was utilized to assess each scenario's performance. Optimal solutions were then obtained using non-dominated sorting genetic algorithm-II (NSGA-II), and a cost-effectiveness Pareto frontier curve was performed for all scenarios. Results showed that the selected solutions of scenarios one to six reduced the runoff volume by 53%, 4%, 66%, 72%, 31%, 34%, respectively. Scenario 4, with a combination of rain barrels, porous pavements, and vegetated swales with a runoff volume reduction of 72% and an implementation cost of $ 12.2 million, showed the best performance, comparing the other scenarios' corresponding optimal solutions. Scenario 6 also came in next with a 34% effectiveness and a cost of $ 7.1 million. The combined use of SUSTAIN and SWMM, in addition to the technical evaluation, helped to attain optimized, cost-effective solutions for developed scenarios as well. The results of this study can also help relevant organizations and decision-makers to design, evaluate performance, and implement costs of different LID scenarios.

in this Paper, Optimal placement and sizing of DGs is considered as an engineering optimization problem. Due to integration and influence of DGs in distributed networks, optimal location and sizing of them are done based on objective functions such as: voltage profile improvement, power loss reduction, reliability improvement, restraining the increase of power plant, etc. These goals considering the NSGA-II algorithm with cold standby strategy will be investigated and simulated on IEEE 33-bus standard test system. In order to implement the concept of Cold Standby theory, a group of DGs are considered which are off in normal condition but in fault condition, these DGs turn on and help to reconstruction the network. In order to measuring reliability, used the expected energy not supply (EENS) index. Optimization is done with two goals: reduction of EENS and the cost, including investment cost and power loss cost. Moreover, voltage profile improvement has been applied as a function penalty. Simulation has been done in MATLAB software and results show the effectiveness and capability of proposed algorithm.