Research Of Mdvrp On Mmas

As information technology is widely used in modern logistics. Intelligent logistics scheduling also become a researching hot spot. Vehicle routing problem (Vehicle Routing Problem) is a very important link in .intelligent logistics scheduling.This problem effectively resolved can improve logistics scheduling scientific level lower transport cost, increase economic effectively.Logistics industry is fusion transport, warehousing, freight forwarding industry and information industries compound services industry is national economy important component, in promoting industrial restructuring, change economic development way and enhance national economy competitiveness play important role. In Logistics booming process transport throughout logistics occupy important position, total cost represented logistics total costs 35% -50% of accounted commodity prices 4% -10%. Transport on logistics total cost savings has pivotal role.As people's living standards, users of enterprise logistics and distribution service quality put forward higher requirements, business logistics and distribution environment is increasingly complex, facing the logistics task more arduous, many companies have thus established multiple distribution centers. Multi Distribution Center (ie more supply market) Vehicle Routing Problem (Multiple-Depot Vehicle Routing Problem, MDVRP), become intelligent logistics scheduling of a research hot spots. The so-called multi-distribution center and distribution, is in response to a broader geographical range of customer service needs, distribution of vehicles from multiple distribution centers starting to complete the transportation task, to improve vehicle utilization, reduce the total transport distance, saving transport costs and faster to meet customer needs purposes. This multi-Depot Vehicle Routing Problem in the main from the following aspects:1. Multi Depot Vehicle Scheduling (MDVRP) mathematical modelDistribution center on the multi-vehicle scheduling problem was modeled above, the analysis of the corresponding constraints, multi-distribution center constructed a mathematical model of vehicle scheduling. In this mathematical model, taking into account the resources available in some time not sufficient to satisfy all customer demands and how to deal with different customer needs, customer evaluation of fuzzy model is introduced to make the model more realistic business needs. 2. Ant colony algorithm and improved maximum - the smallest ant colony algorithm (MMAS) Distribution centers around the multi-vehicle routing problem, ant colony algorithm is introduced, the basic principle of ant colony algorithms have been described, describes the basic process of the basic ant colony algorithm, and an improved maximum - the smallest ant colony algorithm.3. Simulation results show that the biggest improvement - the smallest ant colony algorithm for the validity of MDVRPBy Matlab simulation, the improved MMAS algorithm is applied to Marius benchmarks described in the literature MDVRP instance, can be drawn to improve the MMAS has better convergence speed and better results.