Robust Dynamic Multi-Objective Vehicle Routing Optimization Method.
For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, and the total distance of routes were normally considered as the optimization objectives. Except for the above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, a corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. 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 customers, which was time-consuming. Therefore, a robust dynamic multi-objective vehicle routing method with two-phase is proposed . Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii) The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (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 customers appear.
Yi-Nan
YN
Guo
Jian
J
Cheng
Sha
S
Luo
Dunwei
D
Gong
Yu
Y
Xue
D000465:Algorithms; D001334:Automobile Driving; D002244:Carbon / Q000032:analysis; D004787:Environmental Pollution / Q000032:analysis / Q000517:prevention & control; D006801:Humans; D000069550:Machine Learning; D014186:Transportation / Q000379:methods*; D001335:Vehicle Emissions / Q000032:analysis / Q000517:prevention & control
D001335:Vehicle Emissions; D002244:Carbon