Designing fuzzy-genetic learner model based on multi-agent systems in supply chain management
Supply chain requirements and challenges in recent years have made managers to explore new methods in dealing with supply chain management (SCM) problems. Methods with high flexibility which can adapt plans to real conditions help one to make a decision at the right time. In the SCM, distribution and allocation problems are of enormous significance and due to their applications in the cross-functional and final parts of SCM problems, they are in a particular position among the SCM problems. In this paper, by proposing an architecture based up on multi-agent system (MAS), a model is developed to tackle such problems as the nature of supply chain distributions, dynamic distributions systems (DS), uncertain parameters in DS, management of diverse objectives in DS, need for flexibility in DS and other factors considered as challenges and designing requirements in an agile model which can be all found in the SCM. MAS was used in this article owing to their special attributes and features. In MAS, each agent follows up a duty in a self-contained way and is able to adapt it to the environmental changes, after all helping the system to stay alive.
supply chain management (scm) multi-agent system (mas) fuzzy inference (fi) genetic algorithm (ga) self organized maps (som)