论文部分内容阅读
构建了不确定条件下速度时变的VRPTW问题模型(UTDVRPTW),设计了一种改进的双重进化人工蜂群算法求解该模型.在需要两点进行操作的搜索过程中,采用一点随机选取,另一点通过遍历可行解,以其中最优解确定位置的半随机式搜索策略改进插入点算子和逆转序列算子,分别在两对以及三对城市间距离之和的解空间维度上交叉搜索,并应用到局部搜索中构成双重进化过程.实验结果验证了所提出算法的有效性以及解决UTDVRPTW的可行性.
A time-varying VRPTW problem model under uncertainty (UTDVRPTW) was constructed and an improved dual evolution artificial bee colony algorithm was proposed to solve the model.Using a little random selection in the search process which needs two operations, Firstly, by traversing the feasible solution, the semi-random search strategy with the best solution among them is used to improve the insertion point operator and the reversal sequence operator, respectively, cross search in the solution space dimension of the sum of the distances between two pairs and three pairs of cities, And applied to the local search to form a dual evolutionary process.The experimental results verify the effectiveness of the proposed algorithm and the feasibility of solving UTDVRPTW.