Research on constraint-based genetic algorithm approach for mobile supply chain dynamic decisions

With mobile technologies like RFID and GPS, dynamic and real-time decision making can be achieved in a so-called "Mobile Supply Chain". The traditional supply chain model is based on static information. The Inventory Optimizing Model, the Vehicle Routing Problem (VRP) model, and the Inventory-Routing Problem (IRP) model are all determined as the static and immobile programming of a Supply Chain. In this paper, a Real-time Inventory-Routing Integrated (RIRI) model is proposed to analyze the dynamic Location and Time information in a mobile supply chain. First, the static inventory ordering policy with stochastic demand is made in a 1: n distribution supply chain. In succession the RIRI model regards the on-the-way inventory as a virtual Distribution Center (DC) with the help of real-time location information. As a result, the destination and quantity of on-the-way inventory can be optimized dynamically in order to decrease the cost of the SC and keep service level for customers. We also propose a constraint-based genetic algorithm approach to resolve the NP problem and to satisfy complex constraints. Finally, simulation based on the data of China's FAW Group Corporation is calculated to prove the validity and efficiency of the RIRI model. An integrated framework is set up for dynamic mobile supply chain optimization.