جستجوی مقالات مرتبط با کلیدواژه "non-dominated sorting genetic algorithm" در نشریات گروه "مدیریت"

Increasing environmental pollution has caused global warming and consequently, it has endangered human health and animals. Hence, green concept has received much attention in recent studies. In this research, a multi-objective mathematical for a multi-level multi-product supply chain network is presented which aims to minimize the environmental impact and total costs of supply chain and maximize the customers' satisfaction level. On the other hand, due to the unspecified demands level, demand uncertainty has been considered in the problem with different scenarios. Regarding the complexity of the proposed mathematical model and difficulties in solving the problem with exact methods in large size, a NSGA II method has been proposed. Finally, the proposed NSGAII has shown as a reliable method to find efficient Pareto frontiers in a reasonable time. Therefore, it can be employed for solving large scale problems.

Today supply chain network is recognized as the main bases in economic activity. Their significance is due to just in time delivery and the efficiency of different commodities including food, clothing, energy, computer hardware. This has stimulated researchers and experts to analyze supply chain problems. Meanwhile, uncertainty has penetrated every level of our lives and we encounter it every day. The present paper focuses on green supply chain that consider uncertainty conditions to solve a model for designing a forward green supply network (environmental)under uncertainty of future economic conditions in Iran Khodro Company. The problem of designing the aforementioned network includes hypotheses including multi-commodity, multi-layer and one-period. Due to inconsistent economic conditions, uncertainty has been differently tackled here as compared with previous literature. In this problem, several important parameters have been considered as indefinite including customers’ demands, operating expenses, the productive capacity and relocating capacity of facilities. The proposed model also considers the contamination of production section and the transportation system of the chain and tries to reduce it by suggesting an objective function and Eco-indicator 99 method. As well, production and distribution centers operate in a dual-purpose manner. Saving costs and reducing contamination due to applying transportation supplies and common infrastructures are among the benefits of this method. Considering the complexity of solving this problem and its NP-hard nature, the meta heuristic method of genetic algorithm with non-dominated sorting (NSGAII) was analyzed and finally model performance was examined with a numerical example and solving it with MATLAB and GAMS software.

In this paper, a bi-objective integrated of production distribution planning problem model in a multi echelon supply chain is presented. This model is considered in a four echelon supply chain includes suppliers, manufactures, distribution centers and customers with several types of raw material and product in multi-time period. In addition to minimize the total cost of supply chain including transportation and supply of materials costs, preparation and production of products, inventory holding of materials and products in manufacture and distribution centers, transportation and purchase of products for distributors and customers, and backorder cost, the second objective is to decrease the transfer time of products in order to increasing the service level for customers. Two Pareto-based multi-objective meta-heuristic algorithms namely non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed to solve the model. Since the solution quality of all meta-heuristic algorithms severely depended on their parameters, Taghuchi method has been utilized to tune the parameters of algorithms. Finally, computational results obtained by implementing the algorithms on several problems of different sizes demonstrate the performance of proposed methodologies.

Considering that the active companies in the field of oil, gas, petrochemical and other energies are project-based and also the increase of gas applicants who have taken policy of replacing the gas instead of other fossil fuels, have imposed certain condition on organizations and project managers in the gas company. One of the most important problem in the issue of project management is project portfolio selection which is defined one of the most important activities in many organization such as gas organization. In this study at first the effective indicators on projects are extracted by using the literature and interviews with the experts of gas industry then the mathematical robust multi objective Model is Provided by considering the uncertainty and unreliability in some parameters of model. This model is solved by using non-dominate sorting genetic algorithm for 20 degree of risk-taking decision gama (I C t / IB t ).At the end for helping in decision making the TOPSIS technique is used for providing a specific answer in pareto front .