农村公交协同车辆路径问题的研究  

Research of collaborative vehicle routing problem for bus in countries

作　　者：蔡延光[1] 汤雅连[1] 朱君[1] 

机构地区：[1]广东工业大学自动化学院,广州510006

出　　处：《计算机应用研究》2015年第6期1657-1662,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61074147;61074185);广东省自然科学基金资助项目(S2011010005059;8351009001000002);广东省教育部产学研结合项目(2012B091000171;2011B090400460);广东省科技计划资助项目(2012B050600028;2010B090301042)

摘　　要：针对考虑农村人口出行频次的季节偏好性、早晚高峰期班次多、乘客乘车的最长忍受时间、司机连续驾驶时间限制、车辆可以停在其他车场、车场与车场之间的车辆可以共享等因素的农村公交的协同车辆路径问题,建立车辆租赁模式的单车型开放式协同车辆路径问题的数学模型。结合节约算法、扫描算法和遗传算法,构造混合蚁群算法对实例进行仿真。首先通过扫描算法对站点进行分组,然后应用节约算法对单个旅行商问题(traveling salesman problem,TSP)求解得到可行解,最后应用混合蚁群算法对可行解进一步优化。结果表明该算法在收敛速度和寻优能力两方面都优于遗传算法。Considering these facts, such as travel frequency seasons＇ preference of rural population, much more shifts in rush hours, enduring the longest time of getting the bus, continuous driving time limit of drivers, vehicles can be parked in other sta- tions, vehicles can be shared between different depots etc. , this paper established homogeneous vehicle open collaborative vehi- cle routing problem with leasing pattern model. Combining with saving algorithm, scanning algorithm and genetic algorithm, it constructed hybrid ant colony optimization. First of all, by scanning algorithm for grouping site, then applied saving algorithm for single TSP and got feasible solution, at last, applied the hybrid ant colony optimization to further optimized solution. The simula- tion results show that the algorithm is superior to genetic algorithm in convergence speeds and optimization ability.

关 键 词：协同车辆路径问题 季节偏好性 节约算法 蚁群优化 扫描算法 遗传算法 

分 类 号：TP301.5[自动化与计算机技术—计算机系统结构]