Research on an Algorithm for Solving Time-Dependent Green Vehicle Routing Problem
The Ant Colony Optimization(ACO)algorithm is an optimization algorithm that simulates the behavior of ants identifying food paths.It can solve the Non-deterministic Polynomial(NP)-hard combination problem of geometric distributions in a dynamically changing environment without any external guidance or control.To prevent the ACO algorithm from falling easily into the local optimum and to mitigate the difficulty in balancing the depth and breadth of search when solving NP-hard problems,a Green Intelligent Evolutionary Ant Colony Optimization(G-IEACO)algorithm is proposed.By introducing four types of domain operators,the state transition rules and pheromone update methods of the ACO algorithm are improved,thus enhancing the optimization performance and preventing premature convergence.Additionally,a congestion avoidance strategy is adopted to balance between time and environmental costs.Results of numerical analysis show that the G-IEACO algorithm outperforms the Genetic Algorithm(GA)in terms of the Total driving Time(TT)and vehicle carbon emission(TCO2)of the fleet.Specifically,it reduces the TT and TCO2 by 13.32％and 13.64％on average,respectively,in test cases of R2 and RC2 involving 100 clients,thus implying that it can effectively promote the realization of green and low-carbon goals.
Ant Colony Optimization(ACO)algorithmoperation operatorstate transitionpheromone updatecongestion avoidance strategy
万方数据
维普
