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

One of the organizations’ fundamental issues is supply chain network design. Optimization of this network can lead to effective management of the whole supply chain. Network design specifies the position، capacity، number and type of network facilities، and transportation network of materials and products from the supplier to the customer and vice versa. This research proposes new solution procedure based on Multi-objective Genetic Algorithm (MOGA) and Non-dominated Sorting Genetic algorithm-II (NSGAII) to find the set of Pareto optimal solutions that empowers the decision-makers by alternative solutions. Considering that in this study the level of service is very important، so this modeling was based on satisfying all customer demands. Objectives for network optimization are minimization of total cost and maximization of capacity utilization balance for network facilities that lead to the reduction of customers’ service time (increase service levels). Nine problems were designed from small to large. In order to compare the quality of the obtained Pareto solutions of both algorithms، seven criteria (for multi-objective problems) were used in this study. The results indicated that the solutions produced by NSGAII algorithm have higher quality.

An alternative for decreasing risk for knowledge workers is effective & optimized work break.Major performance criteria in R&D based organization is successful projects. Selection of opproporiate members can be a most effect on the projects achievement. But as the selection of R&D special members lead to decrease of risk, repeatative participation of this people in the same project lead to knowledge concentration and achieve organization with serios risks. Therefore attendance to knowledge and quality project angles is important to selection of R&D teams members. In this study, we developed a model based on Non-Dominated Sorting Genetic Algorithm.

This paper presents a multi-objective mathematical model for redundancy allocation in production systems. In many of the production and assembly lines, process times, time between failures and repaired times are generally distributed. The proposed method of this paper is able to consider time dependent parameters as general distribution functions by using the hybrid approach of simulation and response surface methodology. The objectives of the mathematical model are maximizing production rate, minimizing total cost and maximizing quality. In order to solve the proposed mathematical model, non-dominated sorting genetic algorithm and multiple objective particle swarm optimization are used. Numerical results indicate the effectiveness of both algorithms for generating non-dominated solutions. Moreover, comparative results indicate the superiority of the Non-dominated sorting genetic algorithm.