Research On The Inventory Routing Problem With Uncertainty Demand Based On A Modified Ant Colony Optimization Algorithm

As the science and technology continually developing and the globalization of the ecnomics, the competition between enterprises focus on the supply chain. Modern logistics also trun to the collaboration and cooperation among different enterprises on the supply chain. In recent years, a inventory management strategy is widely used which is called vendor manage inventory(VMI), it can effectively overcome the bull-effect, and the key point of VMI is how to harmonize the transportation and the inventory which are two trade-off links in logistics, and this is called the inventory routing problem(IRP).This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads. And then uses a modified ant colony optimization (ACO) to find a solution for the model. The modified ACO departs from the conventional ant colony optimization algorithm which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stock out costs on nodes. At last uses the Solomon’s benchmark problems to test the feasibility and validity of the model and algorithm.The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem.