Mobile Robot Routing with Energy Consumption Optimization.

We consider scheduling mobile robots which are used to carry materials for intelligent manufacturing. Battery-powered mobile robots transport materials from warehouse to a set of demand points, and their power varies during a trip as the weight of loads varies whenever unloading some materials at a demand point. In most of literature about vehicle routing problems, researchers only consider minimizing travel distance, without explicitly considering the effect of different loads on the energy consumption, which is critical for many real world applications. To address this problem, we develop an innovative mixed integer linear programming model for scheduling robots with energy consumption optimization. Numerical results show that our model can significantly reduce energy consumption with slightly increased traveling distance, comparing with the traditional routing models which just minimize the total travel distance. The model can also accurately predict the energy consumption of trips in serving demand points, hence increasing the reliability of scheduling and reducing the times of charging. The results demonstrate the importance of considering energy consumption in some scheduling problems where the weight variation of loads cannot be ignored. This optimization model can also be used in other areas, such as city logistics where vehicles are used to deliver goods to customers at different locations in a city.