考虑个体驾驶速度偏差的车辆调度模型

张娜; 杨琦; 胡飞虎; 蒋馨玉; 刘永雄

doi:10.19818/j.cnki.1671-1637.2020.05.015

考虑个体驾驶速度偏差的车辆调度模型

doi: 10.19818/j.cnki.1671-1637.2020.05.015

张娜^{1, 2, 3,},
杨琦^4,,
胡飞虎^5, ,,
蒋馨玉⁵,
刘永雄⁶

1.
长安大学运输工程学院, 陕西西安 710064
2.
长安大学人文学院, 陕西西安 710064
3.
得克萨斯大学奥斯汀分校穆迪传媒学院, 得克萨斯奥斯汀 TX78705
4.
长安大学经济与管理学院, 陕西西安 710064
5.
西安交通大学电气工程学院, 陕西西安 710049
6.
长安大学信息工程学院, 陕西西安 710064

基金项目:

国家自然科学基金项目 71732006

国家自然科学基金项目 61174154

陕西省社会科学基金项目 2019S032

中央高校基本科研业务费专项资金项目 310833160212

四川省科技成果转移转化示范重大项目 21ZHSF0055

详细信息

作者简介:
张娜(1981-), 女, 陕西宝鸡人, 长安大学副教授, 长安大学工学博士研究生, 从事交通运输规划与管理研究

杨琦(1963-), 男, 陕西白水人, 长安大学教授, 工学博士

通讯作者:
胡飞虎(1972-), 男, 陕西咸阳人, 西安交通大学副教授, 管理学博士

中图分类号: U492.2
计量
- 文章访问数: 1260
- HTML全文浏览量: 308
- PDF下载量: 297
- 被引次数: 0
出版历程
- 收稿日期: 2020-04-25
- 刊出日期: 2020-10-25

Vehicle scheduling model considering individual driving speed deviation

ZHANG Na^{1, 2, 3
,},
YANG Qi^4
,,
HU Fei-hu^{5
, ,},
JIANG Xin-yu⁵,
LIU Yong-xiong⁶

1.
College of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
2.
School of Humanities, Chang'an University, Xi'an 710064, Shaanxi, China
3.
Moody College of Communication, The University of Texas at Austin, Austin TX78705, Texas, USA
4.
School of Economics and Management, Chang'an University, Xi'an 710064, Shaanxi, China
5.
School of Electric Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
6.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Natural Science Foundation of China 71732006

National Natural Science Foundation of China 61174154

Social Science Foundation of Shaanxi Province 2019S032

Fundamental Research Funds for the Central Universities 310833160212

Sichuan Major Project of Science and Technology Achievements Transter Demonstration 21ZHSF0055

More Information

Author Bio:
ZHANG Na(1981-), female, associate professor, doctoral student, zhangna@chd.edu.cn

YANG Qi(1963-), male, professor, PhD, yangqi@chd.edu.cn

HU Fei-hu(1972-), male, associate professor, PhD, hufeihu@mail.xjtu.edu.cn

Article Text (Baidu Translation)

摘要

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。
- 车辆调度 /
- 调度方案 /
- 遗传算法 /
- 驾驶速度偏差 /
- 驾驶人分配
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.
- vehicle scheduling /
- scheduling scheme /
- genetic algorithm /
- driving speed deviation /
- driver assignment

HTML全文

图 1 算法流程

Figure 1. Algorithm flow

下载: 全尺寸图片幻灯片

表 1 相关变量与符号

Table 1. Related variables and symbols

变量/符号	说明
M	物资集合, m∈M
D	仓库点集合, d∈D
R	需求点集合, r∈R
V	车辆集合, v∈V
P	驾驶人集合, p∈P
n $_{d}^{m}$	仓库点d中m类物资的存储量
n $_{r}^{m}$	需求点r对m类物资的需求量
l $_{d}^{r}$	仓库点d与需求点r之间的距离
d $_{p}^{z}$	驾驶人p的初始仓库点
d_v	车辆v的初始仓库点
c_v	车辆v的载货量
s_v	车辆v的平均速度
h_v	车辆v的满载运输成本
e_v	车辆v的空载运输成本
Φ	所有车辆的路径调度方案集合
ϕ_v	车辆v的调度方案, ϕ_v⊆Φ
Θ	所有车辆的驾驶人分配方案集合
θ_v	被分配给车辆v的驾驶人, θ_v∈P
s_{θ_v}	驾驶人θ_v的驾驶速度偏差, 为θ_v驾驶车辆v时车速与车辆v平均速度差异的百分比
t $_{ϕ_{v}}^{v}$	车辆v完成调度方案ϕ_v的总时间
C	整体运输成本

下载: 导出CSV

表 2 各仓库点到各需求点的距离

Table 2. Distances from each depot to each demand place

距离/km	d₁	d₂	d₃
r₁	505	1 014	939
r₂	523	553	957
r₃	447	250	610
r₄	682	173	372
r₅	838	823	463
r₆	352	677	317
r₇	377	886	711

下载: 导出CSV

表 3 各仓库点中各类物资储备量

Table 3. Amounts of various materials in each depot

储备量/t	m₁	m₂	m₃
d₁	114	103	110
d₂	102	94	78
d₃	98	89	94

下载: 导出CSV

表 4 各需求点对各类物资的需求量

Table 4. Demands for all kinds of materials demand

需求量/t	m₁	m₂	m₃
r₁	27	23	30
r₂	25	26	28
r₃	17	19	15
r₄	35	37	33
r₅	34	38	32
r₆	18	23	17
r₇	25	20	21

下载: 导出CSV

表 5 车辆参数

Table 5. Vehicle parameters

参数	c_v/t	s_v/(km·h^-1)	h_v /(元·km^-1)	e_v /(元·km^-1)
v₁-v₅	3	65	0.8	0.8
v₆-v₁₀	5	62	1.0	1.0
v₁₁-v₁₅	8	60	1.2	1.2

下载: 导出CSV

表 6 驾驶人初始仓库点

Table 6. Starting depots of drivers

仓库点

p₁

p₂

p₃

p₄

p₅

p₆

p₇

p₈

p₉

p₁₀

p₁₁

p₁₂

p₁₃

p₁₄

p₁₅

_{p}^{z}

d₁

d₂

d₃

d₁

d₂

d₃

d₁

d₂

d₃

下载: 导出CSV

表 7 以最大单辆车辆完成调度方案的总时间最短为目标的最优调度方案

Table 7. Optimal scheduling schemes for shortest completion time on largest single-vehicle

v	ϕ_v	θ $_{v}^{(1)}$	t $_{ϕ_{v}}^{v}$ /h
v₁	(d₁m₁r₃)→(d₂m₃r₅)→(d₁m₃r₂)→(d₁m₃r₅)→(d₃m₂r₄)→(d₂m₂r₅)	p₁	102.01
v₂	(d₁m₁r₂)→(d₁m₁r₅)→(d₁m₁r₇)→(d₁m₁r₄)→(d₂m₃r₆)→(d₃m₂r₅)→(d₃m₁r₄)→(d₂m₂r₄)→(d₂m₂r₄)	p₇	109.84
v₃	(d₂m₁r₇)→(d₃m₃r₆)→(d₁m₁r₇)→(d₃m₂r₇)→(d₃m₁r₇)	p₂	92.29
v₄	(d₂m₂r₃)→(d₃m₃r₄)→(d₂m₃r₂)→(d₃m₁r₁)→(d₂m₂r₅)→(d₃m₃r₆)	p₃	108.82
v₅	(d₃m₂r₆)→(d₁m₃r₁)→(d₂m₁r₂)→(d₁m₂r₁)→(d₂m₂r₄)→(d₁m₁r₂)	p₅	103.52
v₆	(d₃m₃r₇)→(d₃m₁r₅)→(d₃m₃r₄)→(d₃m₃r₂)→(d₃m₁r₆)→(d₃m₂r₇)	p₄	106.76
v₇	(d₁m₃r₄)→(d₃m₃r₆)→(d₃m₁r₃)→(d₂m₂r₂)→(d₁m₃r₅)→(d₁m₃r₁)→(d₁m₃r₇)	p₆	105.75
v₈	(d₂m₃r₅)→(d₂m₂r₅)→(d₁m₁r₅)→(d₃m₃r₃)→(d₂m₃r₃)→(d₂m₁r₄)→(d₃m₁r₃)	p₈	112.79
v₉	(d₃m₃r₇)→(d₁m₂r₄)→(d₃m₂r₅)→(d₃m₂r₄)→(d₃m₂r₅)→(d₃m₂r₂)	p₁₀	90.05
v₁₀	(d₁m₁r₁)→(d₂m₁r₂)→(d₃m₁r₆)→(d₃m₃r₃)→(d₁m₂r₃)→(d₁m₂r₇)→(d₃m₂r₁)	p₁₂	104.77
v₁₁	(d₁m₁r₁)→(d₁m₃r₆)→(d₁m₂r₂)→(d₂m₂r₄)→(d₁m₃r₂)→(d₂m₂r₄)→(d₃m₁r₅)→(d₁m₂r₅)→(d₃m₁r₄)	p₁₁	112.54
v₁₂	(d₂m₁r₁)→(d₁m₁r₇)→(d₃m₁r₅)→(d₂m₃r₂)→(d₂m₁r₆)→(d₃m₃r₁)→(d₂m₃r₄)	p₁₄	111.45
v₁₃	(d₂m₁r₂)→(d₁m₁r₄)→(d₁m₂r₁)→(d₁m₃r₇)→(d₃m₃r₄)→(d₃m₃r₅)→(d₂m₁r₄)	p₁₃	92.18
v₁₄	(d₃m₃r₄)→(d₂m₂r₂)→(d₁m₃r₁)→(d₁m₂r₃)→(d₁m₃r₂)→(d₂m₁r₅)→(d₃m₂r₆)→(d₂m₁r₃)→(d₂m₂r₇)	p₁₅	112.92
v₁₅	(d₃m₂r₅)→(d₁m₂r₁)→(d₁m₃r₅)→(d₃m₁r₇)→(d₁m₂r₆)→(d₂m₂r₆)→(d₁m₃r₁)	p₉	109.64
C/元	80 904.40	max(t $_{ϕ_{v}}^{v}$ )/h	112.92

下载: 导出CSV

表 8 以整体运输时间最短为目标、以整体运输成本最低为目标及多目标下的最优驾驶人分配方案

Table 8. Optimal driver allocation schemes for least overall transportation time, least overall transportation cost and multi-objective

分配方案

θ_v₁

θ_v₂

θ_v₃

θ_v₄

θ_v₅

θ_v₆

θ_v₇

θ_v₈

θ_v₉

θ_v₁₀

θ_v₁₁

θ_v₁₂

θ_v₁₃

θ_v₁₄

θ_v₁₅

Θ⁽¹⁾

p₁

p₇

p₂

p₃

p₅

p₄

p₆

p₈

p₁₀

p₁₂

p₁₁

p₁₃

p₁₄

p₁₅

p₉

Θ⁽²⁾

p₁

p₇

p₃

p₂

p₅

p₄

p₁₂

p₈

p₁₀

p₁₁

p₆

p₁₃

p₁₄

p₁₅

p₉

Θ⁽³⁾

p₁

p₆

p₃

p₂

p₅

p₄

p₁₁

p₁₃

p₁₀

p₇

p₁₁

p₈

p₁₄

p₁₅

p₉

下载: 导出CSV

表 9 以整体运输时间最短为目标、以整体运输成本最低为目标及多目标下的最优调度结果

Table 9. Optimal scheduling results for least overall transportation time, least overall transportation costs and multi-objective

分配方案	目标函数值	max(t $_{ϕ_{v}}^{v}$ )/h	C/元
Θ⁽¹⁾	112.92	112.92	80 904.40
Θ⁽²⁾	77 465.40	115.21	77 465.40
Θ⁽³⁾	4 064.17	113.63	79 124.50

下载: 导出CSV

表 10 驾驶人随机分配方案

Table 10. Driver random allocation schemes

分配方案

θ_v₁

θ_v₂

θ_v₃

θ_v₄

θ_v₅

θ_v₆

θ_v₇

θ_v₈

θ_v₉

θ_v₁₀

θ_v₁₁

θ_v₁₂

θ_v₁₃

θ_v₁₄

θ_v₁₅

_{v}^{(4)}

p₁₂

p₁₁

p₂

p₈

p₄

p₉

p₇

p₁₃

p₁₅

p₁

p₆

p₃

p₁₄

p₁₀

p₅

_{v}^{(5)}

p₁₁

p₁

p₁₃

p₃

p₁₅

p₄

p₁₂

p₂

p₉

p₇

p₆

p₁₄

p₈

p₅

p₁₀

_{v}^{(6)}

p₁₂

p₇

p₁₃

p₈

p₄

p₉

p₁

p₁₄

p₁₅

p₁₁

p₆

p₂

p₃

p₁₀

p₅

下载: 导出CSV

表 11 无驾驶速度偏差及驾驶人随机分配方案的以最大单辆车辆完成调度方案的总时间最短为目标的调度结果

Table 11. Scheduling results for no driving speed deviation and random driver allocation schemes with shortest completion time on largest single-vehicle

分配方案	max(t $_{ϕ_{v}}^{v}$ )/h	C/元	时间差比/%	成本差比/%
无驾驶速度偏差	116.89	83 105.60	3.50	2.72
Θ⁽⁴⁾	118.27	84 440.20	4.73	4.37
Θ⁽⁵⁾	116.03	80 546.70	2.75	-0.44
Θ⁽⁶⁾	117.73	83 446.90	4.26	3.14

下载: 导出CSV

表 12 无驾驶速度偏差及驾驶人随机分配方案的以整体运输成本最低为目标的调度结果

Table 12. Scheduling results for no driving speed deviation and random driver allocation schemes with least overall transportation cost

分配方案	max(t $_{ϕ_{v}}^{v}$ )/h	C/元	时间差比/%	成本差比/%
无驾驶速度偏差	124.75	79 760.40	8.83	2.96
Θ⁽⁴⁾	120.53	79 821.50	4.62	3.04
Θ⁽⁵⁾	120.71	78 560.50	4.77	1.41
Θ⁽⁶⁾	126.22	79 016.70	9.56	2.00

下载: 导出CSV

表 13 无驾驶速度偏差及驾驶人随机分配方案的多目标调度结果

Table 13. Scheduling results for no driving speed deviation and random driver allocation schemes with multi-objective

分配方案	目标函数值	max(t $_{ϕ_{v}}^{v}$ )/h	C/元	目标函数值差比/%
无驾驶速度偏差	4 110.23	121.35	79 899.30	1.13
Θ⁽⁴⁾	4 153.93	119.25	80 812.80	2.21
Θ⁽⁵⁾	4 103.72	118.72	79 818.70	0.97
Θ⁽⁶⁾	4 176.64	120.43	81 244.60	2.77

下载: 导出CSV

参考文献(35)

[1]	BRAEKERS K, RAMAEKERS K, VAN N I. The vehicle routing problem: state of the art classification and review[J]. Computers and Industrial Engineering, 2016, 99: 300-313. doi: 10.1016/j.cie.2015.12.007
[2]	王万良, 黄海鹏, 赵燕伟, 等. 基于车辆共享的软时间窗动态需求车辆路径问题[J]. 计算机集成制造系统, 2011, 17(5): 1056-1063. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201105020.htm WANG Wan-liang, HUANG Hai-peng, ZHAO Yan-wei, et al. Dynamic customer demand VRP with soft time windows based on vehicle sharing[J]. Computer Integrated Manufacturing Systems, 2011, 17(5): 1056-1063. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201105020.htm
[3]	BERGHIDA M, BOUKRA A. EBBO: an enhanced biogeography-based optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows[J]. International Journal of Advanced Maufacturing Technology, 2015, 77(9/10/11/12): 1711-1725.
[4]	ADELZADEH M, ASl V M, KOOSHA M. A mathematical model and a solving procedure for multi-depot vehicle routing problem with fuzzy time window and heterogeneous vehicle[J]. International Journal of Advanced Maufacturing Technology, 2014, 75(5/6/7/8): 793-802.
[5]	DA SILVA JÚNIOR O S, LEAL J E, REIMANN M. A multiple ant colony system with random variable neighborhood descent for the dynamic vehicle routing problem with time windows[J]. Soft Computing, 2020, DOI: 10.1007/s00500-020-05350-4.
[6]	MADANKUMAR S, RAJENDRAN C. A mixed integer linear programming model for the vehicle routing problem with simultaneous delivery and pickup by heterogeneous vehicles, and constrained by time windows[J]. Sadhana—Academy Proceedings in Engineering Sciences, 2019, 44(2): UNSP39-1-39.
[7]	LIU Ran, JIANG Zhi-bin. A constraint relaxation-based algorithm for the load-dependent vehicle routing problem with time windows[J]. Flexible Services and Manufacturing Journal, 2019, 31(2): 331-353. doi: 10.1007/s10696-018-9323-0
[8]	KIRCI P. An optimization algorithm for a capacitated vehicle routing problem with time windows[J]. Sadhana—Academy Proceedings in Engineering Sciences, 2016, 41(5): 519-529.
[9]	WEN L, EGLESE R. Minimum cost VRP with time-dependent speed data and congestion charge[J]. Computers and Operations Research, 2015, 56: 41-50. doi: 10.1016/j.cor.2014.10.007
[10]	胡飞虎, 马贝龙, 杨丽, 等. 基于改进遗传算法的应急物资配送车辆调度优化问题研究[J]. 计算机应用研究, 2014, 31(10): 2928-2932. doi: 10.3969/j.issn.1001-3695.2014.10.010 HU Fei-hu, MA Bei-long, YANG Li, et al. Research on vehicle scheduling optimization in emergency material distribution based on improved genetic algorithm[J]. Application Research of Computers, 2014, 31(10): 2928-2932. (in Chinese). doi: 10.3969/j.issn.1001-3695.2014.10.010
[11]	陈钢铁, 帅斌. 基于模糊网络和时间依赖的应急车辆调度优化研究[J]. 中国安全科学学报, 2011, 21(8): 171-176. doi: 10.3969/j.issn.1003-3033.2011.08.027 CHEN Gang-tie, SHUAI Bin. Research on emergency vehicle scheduling optimization based on fuzzy network and time dependence[J]. China Safety Science Journal, 2011, 21(8): 171-176. (in Chinese). doi: 10.3969/j.issn.1003-3033.2011.08.027
[12]	SRINIVAS S S, GAJANAND M S. Vehicle routing problem and driver behaviour: a review and framework for analysis[J]. Transport Reviews, 2017, 37(5): 590-611. doi: 10.1080/01441647.2016.1273276
[13]	SCHNEIDER M. The vehicle-routing problem with time windows and driver-specific times[J]. European Journal of Operational Research, 2016, 250(1): 101-119. doi: 10.1016/j.ejor.2015.09.015
[14]	LIGHTNERLAWS C, AGRAWAL V, LIGHTNER C, et al. An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem[J]. International Journal of Intelligent Computing and Cybernetics, 2016, 9(1): 2-22. doi: 10.1108/IJICC-06-2015-0018
[15]	AGRAWAL V, LIGHTNER C, LIGHTNERLAWS C, et al. A bi-criteria evolutionary algorithm for a constrained multi-depot vehicle routing problem[J]. Soft Computing, 2017, 21(17): 5159-5178. doi: 10.1007/s00500-016-2112-3
[16]	MANCINI S. The hybrid vehicle routing problem[J]. Transportation Research Part C: Emerging Technologies, 2017, 78: 1-12. doi: 10.1016/j.trc.2017.02.004
[17]	KOULAEIAN M, SEIDGAR H, KIANI M, et al. A multi-depot simultaneous pickup and delivery problem with balanced allocation of routes to drivers[J]. International Journal of Industrial Engineering: Theory Applications and Practice, 2015, 22(2): 223-242.
[18]	DREXL M, RIECK J, SIGL T, et al. Simultaneous vehicle and crew routing and scheduling for partial- and full-load long-distance road transport[J]. Business Research, 2013, 6(2): 242-264. doi: 10.1007/BF03342751
[19]	KOK A L, MEYER C M, KOPFER H, et al. A dynamic programming heuristic for the vehicle routing problem with time windows and European Community social legislation[J]. Transportation Science, 2010, 44(4): 442-454. doi: 10.1287/trsc.1100.0331
[20]	YOUSEFIKHOSHBAKHT M, DIDEHVAR F, RAHMATI F. An effective rank based ant system algorithm for solving the balanced vehicle routing problem[J]. International Journal of Industrial Engineering—Theory Application and Practice, 2016, 23(1): 13-25.
[21]	杨群, 赵亚男, 张国伍, 等. 基于模糊规则的货运车调度方法[J]. 系统工程理论与实践, 2002(5): 125-127. doi: 10.3321/j.issn:1000-6788.2002.05.023 YANG Qun, ZHAO Ya-nan, ZHANG Guo-Wu, et al. Freight car scheduling method based on fuzzy rules[J]. System Engineering Theory and Practice, 2002(5): 125-127. (in Chinese). doi: 10.3321/j.issn:1000-6788.2002.05.023
[22]	GEE S B, AROKIASAMI W A, JIANG J, et al. Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands[J]. Soft Computing, 2016, 20(9): 3443-3453. doi: 10.1007/s00500-015-1830-2
[23]	曹剑东. 模糊条件下市区集送货的计算机辅助调度[D]. 北京: 清华大学, 2008. CAO Jian-dong. Computer aided dispatch for city pickup and delivery under fuzzy condition[D]. Beijing: Tsinghua University, 2008. (in Chinese).
[24]	许茂增, 余国印, 周翔, 等. 综合成本最小的低碳车辆调度问题及算法[J]. 计算机集成制造系统, 2015, 21(7): 1906-1914. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201507028.htm XU Mao-zeng, YU Guo-yin, ZHOU Xiang, et al. Low-carbon vehicle scheduling problem and algorithm with minimum-comprehendsive-cost[J]. Computer Integrated Manufacturing System, 2015, 21(7): 1906-1914. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201507028.htm
[25]	GOEL A. The minimum duration truck driver scheduling problem[J]. EURO Journal on Transportation and Logistics, 2012, 1(4): 285-306. doi: 10.1007/s13676-012-0014-9
[26]	TAVAKKOLI-MOGHADDAM R, ALINAGHIAN M, SALAMAT-BAKHSH A, et al. A hybrid meta-heuristic algorithm for the vehicle routing problem with stochastic travel times considering the driver's satisfaction[J]. Journal of Industrial Engineering International, 2012, 8(4): 1-6.
[27]	GHASEMI NOUGHAB M, SADEGHI A, MOHAMMADZADEH MOGHADDAM A, et al. Fatigue risk management: assessing and ranking the factors affecting the degree of fatigue and sleepiness of heavy-vehicle drivers using TOPSIS and statistical analyses[J]. Iranian Journal of Science and Technology—Transactions of Civil Engineering, 2020, 44(4): 1345-1357. doi: 10.1007/s40996-019-00320-9
[28]	徐福博. 基于IC卡数据的公交运行速度差异分析[J]. 汽车实用技术, 2018(16): 94-95. https://www.cnki.com.cn/Article/CJFDTOTAL-SXQC201816034.htm XU Fu-bo. Analysis of bus speed difference based on IC card data[J]. Automobile Applied Technology, 2018(16): 94-95. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SXQC201816034.htm
[29]	李长城, 刘小明, 荣建, 等. 不同能见度条件下高速公路车辆速度特性研究[J]. 交通运输系统工程与信息, 2014, 14(6): 213-218. doi: 10.3969/j.issn.1009-6744.2014.06.035 LI Chang-cheng, LIU Xiao-ming, RONG Jian, et al. Characteristics of vehicle speed for expressway under different visibility condition[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 213-218. (in Chinese). doi: 10.3969/j.issn.1009-6744.2014.06.035
[30]	AKIN D, SISIOPKU V P, SKABARDONIS A. Impacts of weather on traffic flow characteristics of urban freeways in Istanbul[C]∥Elsevier. 6th International Symposium on Highway Capacity and Quality of Service. Amsterdam: Elsevier, 2011: 89-99.
[31]	陈皓, 张磊. 基于大数据的出租车与普通小汽车行驶速度差异研究[J]. 公路, 2017(2): 147-152. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201702029.htm CHEN Hao, ZHANG Lei. Research on the difference of taxi and car speed based on big data[J]. Highway, 2017(2): 147-152. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201702029.htm
[32]	谢涛, 陈火旺, 康立山. 多目标优化的演化算法[J]. 计算机学报, 2003, 23(8): 997-1003. doi: 10.3321/j.issn:0254-4164.2003.08.015 XIE Tao, CHEN Huo-wang, KANG Li-shan. Evolutionary algorithms of multi-objective optimization problems[J]. Chinese Journal of Computers, 2003, 23(8): 997-1003. (in Chinese). doi: 10.3321/j.issn:0254-4164.2003.08.015
[33]	张栋亭. 多目标优化与模糊决策在混流装配排序中的应用[D]. 武汉: 华中科技大学, 2008. ZHANG Dong-ting. Application of multi-objective optimization and fuzzy decision-making for mixed-model assembly sequencing[D]. Wuhan: Huazhong University of Science and Technology, 2008. (in Chinese).
[34]	《中国公路学报》编辑部. 中国交通工程学术研究综述: 2016[J]. 中国公路学报, 2016, 29(6): 1-161. doi: 10.3969/j.issn.1001-7372.2016.06.001 Editorial Department of China Journal of Highway and Transport. Review on China's traffic engineering research progress: 2016[J]. China Journal of Highway and Transport, 2016, 29(6): 1-161. (in Chinese). doi: 10.3969/j.issn.1001-7372.2016.06.001
[35]	TREIBER M, HELBING D. Macroscopic simulation of widely scattered synchronized traffic states[J]. Journal of Physics A—Mathematical and General, 1999, 32(1): 17-23. doi: 10.1088/0305-4470/32/1/003

施引文献

资源附件(0)

访问统计

点击查看大图

图(1) / 表(13)

计量

文章访问数: 1260
HTML全文浏览量: 308
PDF下载量: 297
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

考虑个体驾驶速度偏差的车辆调度模型

doi: 10.19818/j.cnki.1671-1637.2020.05.015

作者简介:
张娜(1981-), 女, 陕西宝鸡人, 长安大学副教授, 长安大学工学博士研究生, 从事交通运输规划与管理研究

杨琦(1963-), 男, 陕西白水人, 长安大学教授, 工学博士

通讯作者:
胡飞虎(1972-), 男, 陕西咸阳人, 西安交通大学副教授, 管理学博士

计量

Vehicle scheduling model considering individual driving speed deviation

Author Bio:
ZHANG Na(1981-), female, associate professor, doctoral student, zhangna@chd.edu.cn

YANG Qi(1963-), male, professor, PhD, yangqi@chd.edu.cn

HU Fei-hu(1972-), male, associate professor, PhD, hufeihu@mail.xjtu.edu.cn

计量

目录

留言板

考虑个体驾驶速度偏差的车辆调度模型

doi: 10.19818/j.cnki.1671-1637.2020.05.015

作者简介: 张娜(1981-), 女, 陕西宝鸡人, 长安大学副教授, 长安大学工学博士研究生, 从事交通运输规划与管理研究 杨琦(1963-), 男, 陕西白水人, 长安大学教授, 工学博士

通讯作者: 胡飞虎(1972-), 男, 陕西咸阳人, 西安交通大学副教授, 管理学博士

计量

出版历程

Vehicle scheduling model considering individual driving speed deviation

Author Bio: ZHANG Na(1981-), female, associate professor, doctoral student, zhangna@chd.edu.cn YANG Qi(1963-), male, professor, PhD, yangqi@chd.edu.cn HU Fei-hu(1972-), male, associate professor, PhD, hufeihu@mail.xjtu.edu.cn

计量

出版历程

目录

作者简介:
张娜(1981-), 女, 陕西宝鸡人, 长安大学副教授, 长安大学工学博士研究生, 从事交通运输规划与管理研究

杨琦(1963-), 男, 陕西白水人, 长安大学教授, 工学博士

通讯作者:
胡飞虎(1972-), 男, 陕西咸阳人, 西安交通大学副教授, 管理学博士

Author Bio:
ZHANG Na(1981-), female, associate professor, doctoral student, zhangna@chd.edu.cn

YANG Qi(1963-), male, professor, PhD, yangqi@chd.edu.cn

HU Fei-hu(1972-), male, associate professor, PhD, hufeihu@mail.xjtu.edu.cn