multi objective

The present study aims to optimize a green closed-loop supply chain (GCLSC) network while minimizing carbon emissions and maximizing product shipments. The proposed model incorporates unbalanced factors such as capacity level, input current limit to each distribution center, and facility environmental level. We considered emission control levels for locating distribution centers as well as the reduction in CO2 emissions at all levels of the supply chain. Moreover, all types of expenditures in a closed-loop supply chain including manufacturing, distribution, recovery, assembly, and disassembly in the model are considered. Consideration of these assumptions closes this study to reality and makes this study an innovative one. Moreover, to account for demand uncertainty, a robust optimization method, the Bertsimas and Sim optimization approach, is used. The Epsilon Constraint Method and non-dominated sorting genetic algorithm II (NSGA-II) were employed to solve multi-objective functions with unknown demand, and the genetic algorithm is used to solve large-scale problems. The results indicate that the proposed approach achieves the objectives of reducing costs, minimizing environmental impact. Moreover, the NSGA-II algorithm outperforms other solution methods in terms of the number and diversity of solutions on the Pareto front. Specifically, the Pareto boundary obtained by NSGA-II contains a larger number of solutions compared to the different types of epsilon constraint methods. Additionally, the diversity of solutions on the Pareto front is higher in the NSGA-II algorithm, indicating a more well-spread and diverse set of solutions. These findings highlight the superiority of NSGA-II as a powerful and effective algorithm for multi-objective optimization problems in green closed-loop supply chain networks.

Given the importance of supply chain and environmental issues, this paper presents a new mathematical model for a green closed-loop supply chain (GCLSC) network with the objectives of maximizing profits, maximizing the number of jobs created, and maximizing reliability. Due to the uncertainty on some parameters such as demand and transportation costs, the new method of robust fuzzy programming model was utilized. Multi-objective Grey Wolf Optimizer (MOGWO) and Non-dominated Sorting Genetic Algorithm II (NSGA II) were used to tackle the problems for larger sizes. A number of instances of the problem in larger sizes were solved. The results from comparing the algorithms considering some criteria including means of objective functions, spacing index, distance index from ideal point, maximum amplitude index, Pareto response number index and computational time showed the fast convergence and high efficiency of MOGWO algorithm for this problem. Finally, the implementation of the model for a real case study in Iranian engine oil industry, showed the efficiency of the obtained solutions for this network.

Given that disasters are unavoidable, and many people are suffering from them each year, we should manage the emergencies and plan for them well to reduce mortality and financial losses. One of the measures that organizations must take after the disaster is the assessment of the conditions and needs of the people. We consider some characteristics for sites and roads and two teams for assessment as well as the uncertain assessment time to modeling. A multi-objective model is proposed in this study. The first objective function maximizes the gain from the assessment of areas and roads. The second and third objective functions maximize total coverage at damaged areas and roads. We use the LP-metric technique to solve small size problems in the GAMS software and the Grasshopper Optimization Algorithm (GOA) as a Meta-heuristic algorithm to solve a case study.  Numerical results are presented to prove the credibility and efficiency of our model.

The humidification-dehumidification system is one of the desalination technologies that can utilize non-fossil thermal sources and requires insignificant input energy. This system is usually suitable for rural areas and places far from the main sources of energy. The purpose of this study is to obtain the most suitable working conditions and dimensions of this system. In this research, thermodynamic modeling was first performed for a simple type of the system (water-heated); then, the effect of parameters on the system performance was investigated. Modeling was conducted through a numerical simulation; furthermore, the assumption of the saturation of exhaust air from the humidifier was also considered in the mentioned code. Afterward, a comparison was made between two different forms of the system, and the proper form was chosen for the rest of the research. Moreover, through heat transfer equations, the dimensions of the two main parts of the system, i.e., humidifier and dehumidifier, were calculated. Besides, multi-objective optimization was carried out for two objective functions, i.e., gained output ratio (GOR) and the system volume, to reduce the space occupied by the system and reach the desired efficiency simultaneously. The optimization was performed using a simulation program, and results were obtained for different weights in order to optimize each objective function. For instance, 379 liters of freshwater can be produced in a day with a total volume of 48 liters for the humidifier and the dehumidifier in the optimized system.

The optimization of investment portfolios is the most important topic in financial decision making, and many relevant models can be found in the literature.  According to importance of portfolio optimization in this paper, deals with novel solution approaches to solve new developed portfolio optimization model. Contrary to previous work, the uncertainty of future returns of a given portfolio is modeled using LR-FUZZY numbers while the function of its return are evaluated using possibility theory. We used a novel Lp-metric method to solve the model. The efficacy of the proposed model is tested on criterion problems of portfolio optimization  on LINGO provides a framework to optimize objectives when creating the loan portfoliso, in a search for a dynamic markets decision. In addition to, the performance of the proposed efficiently encoded multi-objective portfolio optimization solver is assessed in comparison with two well-known MOEAs, namely NSGAII and ICA. To the best of our knowledge, there is no research that considered NSGAΠ, ICA fuzzy simultaneously. Due to improve the performance of algorithm, the performance of this approach more study is probed by using a dataset of assets from the Iran’s stock market for three years historical data and PRE method. The results are analyzed through novel performance parameters RPD method. Thus, the potential of our comparison led to improve different portfolios in different generations.

In the last decade, theory of constraint application in project management lead to make a new approach for project scheduling and control as a critical chain. In this paper, a multi-objective optimization model for multi-project scheduling on critical chain is investigated. The objectives include time, cost and quality. In order to solve the problem, a Simulated Annealing algorithm is developed and then analyzed to investigate the effect of each objectives. The number of activities in each project is not considered the same. Time, cost and quality value are calculated by solving the proposed algorithm and then the total utility amount is obtained. Sensitivity analysis is performed based on various amount of each objective weights. Then the effect of objectives weight variation is investigated on utility function value. In addition the results show that the proposed algorithm are able to solve problem optimally in large scale.
Keywords

In the facility location problem usually reducing total transferring cost and time are common objectives. In the p-hub covering problem it is attempted to locate hubs and allocate customers to established hubs while allocated nodes to hubs are inside of related hubs covering radius. In this paper, we attempt to consider capability of established hubs to achieve a more reliable network. Also, the proposed model try to construct a network with more covering reliability by determining operating covering radius inside of nominal radius. Then, a sensitivity analysis is performed to analyze effect of parameters in the model. The proposed multi objective model is solved by ε-constraint algorithm for small size instances. For large scale instances a non-dominated sorting genetic algorithm (NSGA-II) is presented to obtain Pareto solutions and its performance is compared with results of ε constraint algorithm. The model and solution algorithm were analyzed by more numerical examples such as Turkish network dataset. The sensitivity analysis confirms that the network extracted by the proposed model is more efficient than classic networks.