基于模块化仿真的共享汽车联合调度优化

蒋阳升; 李衍; 李皓; 胡路; 唐优华

doi:10.3969/j.issn.0258-2724.20210083

基于模块化仿真的共享汽车联合调度优化

doi: 10.3969/j.issn.0258-2724.20210083

蒋阳升^{1, 2,},
李衍^{1, 2},
李皓^{1, 2,},
胡路^{1, 2},
唐优华^{1, 2, ,}

1.
西南交通大学交通运输与物流学院，四川成都 610031
2.
西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 610031

基金项目: 国家自然科学基金（71901183）；四川省应用基础研究（JDSKCXZX202001）；成都市科技项目（2019-YF05-02657-SN）

详细信息

作者简介:
蒋阳升（1976—），男，教授，博士，研究方向为公共交通与共享交通等，E-mail：jiangyangsheng@swjtu.cn

通讯作者:
唐优华（1977—），男，教授级高级工程师，博士，研究方向为智能交通，E-mail：tyhctt@swjtu.cn

中图分类号: U491.2
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- 被引次数: 12
出版历程
- 收稿日期: 2021-02-02
- 修回日期: 2021-08-18
- 网络出版日期: 2022-10-15
- 刊出日期: 2021-09-13

Optimization for Joint Relocation of Carsharing Based on Modular Simulation

JIANG Yangsheng^{1, 2
,},
LI Yan^{1, 2},
LI Hao^{1, 2
,},
HU Lu^{1, 2},
TANG Youhua^{1, 2
, ,}

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
2.
National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要:
运营商在调度车辆时单独采用员工或顾客调度策略均难以有效解决共享汽车分布不均衡导致的盈利难问题. 为此，在传统时空网络基础上，考虑道路拥堵和用车需求随时间变化对运营的影响，基于C# 语言和O²DES （object-oriented discrete event simulation）离散事件仿真框架，建立由模块化站点和路段模型组成、可高效率运行的共享汽车仿真系统；在此基础上，提出一个以运营商日均净收益最大化为目标，联合决策车辆库存量阈值和行程定价的仿真优化模型，并为解决随机环境下的全局优化问题，设计了EGA-OCBA （elitist genetic algorithm with optimal computing budget allocation）算法；最后，以成都市的5个共享汽车站点为例，验证了仿真优化模型的有效性. 仿真优化结果表明：在相同车队规模下，与采用固定价格的顾客调度策略相比，联合策略可使日均净收益提升10.37%～162.30%；与单独的员工调度策略相比，联合策略可使日均净收益提升15.34%.
- 共享汽车 /
- 离散事件仿真 /
- 动态定价 /
- 阈值触发调度 /
- 精英遗传算法（EGA） /
- 最优计算量分配（OCBA）
Abstract:
It is difficult for operators to effectively solve the profitable difficulty caused by the imbalanced distribution of shared vehicles when considering staff-based and customer-based relocation alone. Thus, based on the traditional space-time network, the impact of time-varying road congestion and trip demands on the operation is considered. Based on C# language and O²DES (object-oriented discrete event simulation) framework, an efficient carsharing system model composed of modular station and road segment models is built. Moreover, a simulation-optimization model that jointly determines vehicle inventory thresholds and trip pricing is proposed to maximize the daily net revenue of operators. In order to solve the global optimization problem in a random environment, an elitist genetic algorithm (EGA) with optimal computing budget allocation (OCBA) is designed. Finally, a case study in Chengdu with five sites is conducted to demonstrate the efficiency of the proposed simulation-optimization model. The results show that with the same fleet size, the optimal design can increase the average daily net revenue by 10.37%−162.30% compared with customer-based relocation (fixed pricing); the optimized scheme can increase the profit by 15.34% compared with separate staff-based relocation.
- carsharing /
- discrete-event simulation /
- dynamic pricing /
- threshold-triggering relocation /
- elitist genetic algorithm (EGA) /
- optimal computing budget allocation (OCBA)

HTML全文

图 1 时间与事件驱动机制

Figure 1. Time-driven and event-driven mechanisms

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图 2 共享汽车网络示意

Figure 2. Schematic of carsharing network

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图 3 模块化的路段事件

Figure 3. Event-diagram for a modularized path

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图 4 模块化的站点事件

Figure 4. Event-diagram for a modularized station

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图 5 EGA-OCBA算法流程

Figure 5. Flowchart of the EGA-OCBA algorithm

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图 6 路网案例

Figure 6. Road network for a case

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图 7 EGA-OCBA算法迭代过程

Figure 7. Iterative process of the EGA-OCBA algorithm

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表 1 路径模块事件

Table 1. Events of path module

类型	符号	说明
输入	$\alpha _{ij,n}^{( { {\text{p} }1} )}$	车辆 h 进入路段， $\forall h \notin {H_{ij,n}}\left( \tau \right)$
内部	$\beta _{ij,n}^{( {{\text{p}}1} )}$	更新车辆的剩余路程和时间戳
	$\beta _{ij,n}^{( { {\text{p} }2} )}$	车辆 h 加入到路段车辆集合
	$\beta _{ij,n}^{( {{\text{p}}3} )}$	根据路径状态计算平均速度
	$\beta _{ij,n}^{( {{\text{p}}4} )}$	更新共享汽车的预计离开时刻
	$\beta _{ij,n}^{( {{\text{p}}5} )}$	共享汽车 h “尝试”离开路段
输出	$\gamma _{ij,n}^{( {{\text{p}}1} )}$	车辆 h 离开路段， $\forall h \in {H_{ij,n}}( \tau )$

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表 2 站点模块事件

Table 2. Events of station module

类型	符号	说明
输入	$\alpha _i^{( {{\text{p}}1} )}$	顾客到达车站
	$\alpha _i^{( {{\text{p2}}} )}$	下达调度指令，向车站 i 调车
	$\alpha _i^{( {{\text{p3}}} )}$	表示车辆 h 归还到站点 i
内部	$\beta _i^{( {{\text{p1}}} )}$	判断车辆库存是否足够
	$\beta _i^{( {{\text{p2}}} )}$	更新车辆集合，车辆选择去向
	$\beta _i^{( {{\text{p3}}} )}$	判断是否执行调度指令
	$\beta _i^{( {{\text{p4}}} )}$	更新车辆和调度员数量
输出	$\gamma _i^{( {{\text{p1}}} )}$	表示车辆 h 离开站点 i， $\forall h \in {H_i\left( \tau \right)}$

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表 3 车站初始状态

Table 3. Initial state of stations

站点	车辆数/辆	调度员数/人
1	6	2
2	4	2
3	5	2
4	5	2
5	5	2

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表 4 动态定价方案

Table 4. Dynamic pricing scheme 元

时段	${A_{12}}$	${A_{13}}$	${A_{14}}$	${A_{15}}$	${A_{21}}$	${A_{23}}$	${A_{24}}$	${A_{25}}$	${A_{31}}$	${A_{32}}$
06：00—09：00	37.0	22.0	40.0	24.0	48.0	40.0	34.0	28.0	16.0	54.0
09：00—12：00	39.0	56.0	12.0	12.0	51.0	23.0	31.0	25.0	13.0	19.0
12：00—15：00	50.0	38.0	41.0	19.0	43.0	58.0	13.0	51.0	27.0	57.0
15：00—18：00	37.0	34.0	20.0	35.0	17.0	20.0	47.0	43.0	38.0	13.0
18：00—21：00	36.0	36.0	42.0	14.0	55.0	50.0	54.0	15.0	23.0	53.0
21：00—24：00	33.0	54.0	28.0	33.0	44.0	32.0	43.0	43.0	44.0	10.0

时段	${A_{34}}$	${A_{35}}$	${A_{42}}$	${A_{42}}$	${A_{43}}$	${A_{45}}$	${A_{51}}$	${A_{52}}$	${A_{53}}$	${A_{54}}$
06：00—09：00	46.0	31.0	33.0	37.0	56.0	37.0	54.0	10.0	10.0	34.0
09：00—12：00	48.0	58.0	10.0	25.0	35.0	12.0	27.0	59.0	36.0	34.0
12：00—15：00	48.0	32.0	27.0	14.0	34.0	48.0	55.0	32.0	12.0	22.0
15：00—18：00	25.0	48.0	37.0	29.0	28.0	27.0	30.0	16.0	20.0	19.0
18：00—21：00	53.0	30.0	21.0	58.0	24.0	22.0	12.0	18.0	57.0	42.0
21：00—24：00	45.0	23.0	39.0	40.0	50.0	54.0	54.0	42.0	51.0	39.0

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表 5 阈值触发调度方案

Table 5. Threshold-triggering relocation scheme 辆

时段	$S_{ 1}^{ {\text{up} } }\left( \tau \right)$	$S_{ 1}^{ {\text{low} } }\left( \tau \right)$	$S_{ 2}^{ {\text{up} } }\left( \tau \right)$	$S_{ 2}^{ {\text{low} } }\left( \tau \right)$	$S_{ 3}^{ {\text{up} } }\left( \tau \right)$	$S_{ 3}^{ {\text{low} } }\left( \tau \right)$	$S_{ 4}^{ {\text{up} } }\left( \tau \right)$	$S_{ 4}^{ {\text{low} } }\left( \tau \right)$	$S_{ 5}^{ {\text{up} } }\left( \tau \right)$	$S_{ 5}^{ {\text{low} } }\left( \tau \right)$
06：00—09：00	19	18	17	5	17	6	15	12	9	6
09：00—12：00	23	13	11	7	12	5	24	4	17	2
12：00—15：00	13	11	20	19	4	2	21	20	21	2
15：00—18：00	15	5	15	6	19	1	7	5	22	0
18：00—21：00	9	6	12	6	13	2	24	4	23	7
21：00—24：00	12	6	17	16	7	0	22	1	23	15

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表 6 策略净收益对比

Table 6. Net income comparison among strategies 元

策略	收益	策略	收益
联合调度策略	10374.68	动态定价无调度	8979.37
定价 15 元有调度	3955.25	定价 15 元无调度	3756.46
定价 20 元有调度	5888.43	定价 20 元无调度	5466.05
定价 25 元有调度	7055.95	定价 25 元无调度	6880.96
定价 30 元有调度	7863.41	定价 30 元无调度	7193.02
定价 35 元有调度	9234.57	定价 35 元无调度	8043.68
定价 40 元有调度	9826.50	定价 40 元无调度	8106.93
定价 45 元有调度	9504.22	定价 45 元无调度	7698.97
定价 50 元有调度	7973.05	定价 50 元无调度	7203.43

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