Study On Vehicle Scheduling System For Logistics

This paper has proposed a vehicle scheduling system for logistics companies whose service focuses on vehicle transportation. This system includes two key parts: one is the Vehicle Routing optimization layer, which calculates out the best route to the transportation destination for each vehicle by applying Ant Colony Algorithm, and the layer also calculates the shortest time to the destination; the other is fuzzy-neuron network decision-making layer, which combines the unique conditions of each vehicle (including the shortest distance to the destination, the time to reach the destination, the total running distance for today and so on) to calculates the value of Total Satisfied Function( short for TSF) for each vehicle. TSF concludes three sub-functions: Client Satisfied Function, Staff Satisfied Function and Costs Minimized Function, the paper builds mathematic model for each sub-function and realizes them with three modified fuzzy-neuron networks. The value of TSF equals to the sum of each sub-function multiplying its weight. The vehicle which has the highest value of TSF is the best vehicle the system chooses finally. Then system...