| 考虑个体驾驶速度偏差的车辆调度模型 |
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| 引用本文： | 张娜,杨琦,胡飞虎,蒋馨玉,刘永雄.考虑个体驾驶速度偏差的车辆调度模型[J].交通运输工程学报,2020,20(5):187-197. |
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| 作者姓名： | 张娜 杨琦 胡飞虎 蒋馨玉 刘永雄 |
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| 作者单位： | 1.长安大学 运输工程学院, 陕西 西安 7100642.长安大学 人文学院, 陕西 西安 7100643.得克萨斯大学奥斯汀分校 穆迪传媒学院, 得克萨斯 奥斯汀 TX787054.长安大学 经济与管理学院, 陕西 西安 7100645.西安交通大学 电气工程学院, 陕西 西安 7100496.长安大学 信息工程学院, 陕西 西安 710064 |
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| 基金项目： | 国家自然科学基金;陕西省社会科学基金;中央高校基本科研业务费专项;四川省科技成果转移转化示范重大项目 |
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| 摘 要： | 考虑驾驶速度偏差, 建立了多驾驶人、多种车型、多种物资、多仓库点和多需求点的物资车辆调度模型, 分别以整体运输时间最短、整体运输成本最低以及综合整体运输时间与成本最小为目标, 研究了个体驾驶速度偏差对上述目标的影响; 将驾驶人参数加入到遗传算法的基因编码中, 建立了驾驶人唯一性约束、初始地点约束以及物资供需数量约束, 保证每个基因个体中驾驶人分配方案可行, 且物资运输不超供需总量; 采用遗传算法求解了随机分配驾驶人条件下有驾驶速度偏差与无驾驶速度偏差时各目标的车辆调度方案。计算结果表明: 优化调度方案满足模型中的所有约束条件; 3种目标下的最优方案中, 驾驶人的分配方案不同, 说明目标函数受驾驶人驾驶速度偏差影响; 有驾驶速度偏差情况下的各目标调度结果均优于相应无驾驶速度偏差的调度结果, 3种目标函数差比分别为3.50%、2.96%和1.13%, 说明驾驶速度偏差对求解质量有一定影响; 驾驶人随机分配时的各目标调度结果均劣于相应最优结果, 3种目标函数差比分别为3.91%、2.47%和1.98%, 说明驾驶速度偏差会影响调度效率, 优化驾驶人分配方案能降低整体运输时间与成本。由此可见, 根据特定的调度目标对驾驶人进行合理分配, 可以得到更符合调度目标、更贴近实际、更经济省时的车辆调度方案。
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| 关 键 词： | 车辆调度 调度方案 遗传算法 驾驶速度偏差 驾驶人分配 |
| 收稿时间： | 2020-04-25 |
Vehicle scheduling model considering individual driving speed deviation |
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| ZHANG Na,YANG Qi,HU Fei-hu,JIANG Xin-yu,LIU Yong-xiong.Vehicle scheduling model considering individual driving speed deviation[J].Journal of Traffic and Transportation Engineering,2020,20(5):187-197. |
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| Authors: | ZHANG Na YANG Qi HU Fei-hu JIANG Xin-yu LIU Yong-xiong |
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| Abstract: | In view of driving speed deviation, a vehicle scheduling model was established for multi-drivers, multi-vehicles, multi-materials, multi-depots, and multi-demands targeting at the shortest overall transportation time, the lowest overall transportation cost and the least multi-objective overall transportation time and cost, respectively. The effects of individual driving speed deviation on the above targets were studied. The driver parameters were input into the gene coding of the genetic algorithm. The constraints of driver uniqueness, initial location, and the supply and demand quantities of materials were established to ensure that the distribution scheme of drivers in each gene was feasible and the material transportation did not exceed the total supply and demand. A genetic algorithm was applied to solve the vehicle scheduling schemes for each target with and without driving speed deviation under the condition of randomly assigned drivers. Calculation result shows that the optimized scheduling schemes satisfy all the constraints in the model. For the three optimal schemes, the driver assignments are different, indicating that the target function is affected by the driving speed deviation. The dispatching results of each target with driving speed deviation are superior to those without driving speed deviation. The difference ratios of the three objective functions are 3.5%, 2.96% and 1.13%, respectively, which shows that the driving speed deviation has a certain influence on the solving quality. The target scheduling results of the driver's random assignment are inferior to the corresponding optimal results. The different ratios of the three objective functions are 3.91%, 2.47% and 1.98%, respectively, showing that the dispatching efficiency is affected by the driving speed deviation, and optimizing the driver allocation scheme can reduce the overall transport time and cost. Analysis result shows that the vehicle scheduling scheme, which is more in line with the scheduling target, closer to reality, and more economical and time-saving, can be obtained by allocating drivers reasonably according to the specific dispatching target. |
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