车辆路径问题的轻鲁棒优化模型与算法

针对不确定旅行时间下的车辆路径问题,以总变动成本最小为优化目标,建立了一种轻鲁棒优化模型,提出了一种针对问题特征的超启发式粒子群算法.在算法中,利用基于图论中深度优先搜索的初始化策略加快算法的早期收敛速度,引入基于均衡策略的启发式规则变换方式来提高算法的寻优能力,重新设计的粒子更新公式确保生成低层构造算法的有效性.实验结果表明:所提算法能有效地求解不确定旅行时间下的车辆路径问题.

Vehicle routing problem: light-robust-optimization model and algorithm

A light-robust-optimization model is proposed for a vehicle routing problem with uncertain travel times(VRP-UT)with the objective of minimizing total variable cost.A hyper particle swarm optimization(HPSO)including problem-specific knowledge is proposed to solve the model.The proposed HPSO algorithm uses an initialized strategy based on deep first search in graph theory to accelerate the convergent speed at the early stage,and adopts two novel updating rules to generate efficient constructive algorithms,as well as employs some search algorithms based on balance strategy to enhance the optimization capability.Experimental results show that HPSO is an efficient algorithm to solve VRP-UT.