Optimal reorder decision-making in the agent-based apparel supply chain

The application of agent technology in the apparel supply chain management has gained increasing interest. Agents can help automate a variety of tasks and facilitate decision-making in the supply chain. Compared with other industries, there are more uncertainties existing in the fashion industry such as market needs, fashion change and seasonality, which increases the difficulty of managing the apparel supply chain especially in the ordering process. Thus, it is necessary to increase the coordination in the apparel supply chain processes and develop optimal decision-making strategy for the apparel supply chain under the dynamic environment. In this paper, unified modeling language (UML) is applied to simulate the supply chain processes and describe the relationships between agents. This paper also applies genetic algorithm (GA) and fuzzy inference theory to the dynamic reorder strategy for the supply chain agent to make optimal decision about replenishment quantity and reorder point in order to minimize the inventory cost correspondingly.