Robust Dynamic Vehicle Routing Optimization with Time Windows

Abstract

Hard time window and randomly appeared dynamic clients are two key issues in dynamic vehicle routing problems. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served clients, which was time-consuming. Therefore, robust dynamic vehicle routing method based on local optimization strategies is proposed. Three highlights of the novel method are: (i) After constructing optimal robust virtual routes considering all clients, static vehicle routes for fixed clients are formed by removing all dynamic clients from robust virtual routes. (ii) The dynamically appeared clients append to be served according to their service demands and the vehicles’ locations. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic clients. (iii) A metric measuring the algorithms’ robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic clients appear.