Coordinated Optimization of Heterogeneous Fleet Configuration and Multimodal Routing for Industrial Clusters

doi:10.16182/j.issn1004731x.joss.25-1148

Abstract

Abstract:

A two-level optimization model integrating heterogeneous electric vehicle fleet configuration with multimodal path coordination is built to address the challenges in industrial cluster freight transportation involving electric heterogeneous fleet scheduling, such as diverse tasks and stringent constraints on energy consumption and delivery timeliness. Considering the performance coupling characteristics of heterogeneous electric fleets in terms of load-energy consumption-endurance, based on the coordination of multimodal paths and fleet configuration coding, the upper level employs the GWO to optimize task splitting and fleet configuration. The lower level adopts a multi-neighborhood SA algorithm to achieve path coordination scheduling. Simulation results show that the coordinated optimization of heterogeneous fleets and multimodal paths reduces the comprehensive transportation cost by 26.4%, the travel distance by 7%, and the number of configured vehicles by 16.7%, with significantly improved fleet timeliness and scheduling reliability. A generalizable technical path is provided for closed-loop freight transportation of industrial clusters.

Key words: heterogeneous fleet configuration, multimodal path, coordinated optimization, multi-factor constraints, fleet energy efficiency

CLC Number:

TP391.9

Xiao Renxin, Xu Yifan, Chen Guisheng, Pan Nan. Coordinated Optimization of Heterogeneous Fleet Configuration and Multimodal Routing for Industrial Clusters[J]. Journal of System Simulation, 2026, 38(10): 2997-3014.

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Table 2

Vehicle type and technical parameter setting

车辆类型	编号	$ε j$ /(kW·h/km)	$E j m a x$ /(kW·h)	$c j h$ /元	$c j m$ /元	$c j p$ /万元	$c j f$ /(元/km)	额定载重/kg	车辆总质量/kg
电动轻卡	1~3	0.29	80	180	22	16.2	0.23	1 000	1 800~6 000
	4~6	0.33	108			20.8	0.26	2 000
	7~10	0.39	140			26.3	0.31	3 000
电动中卡	11~12	0.8	255	220	37	41.5	0.64	5 000	6 000~14 000
	13~14	0.90	298			47.0	0.72	6 000
	15~16	1.00	340			53.2	0.80	7 000
	17~20	1.10	425			61.0	0.88	9 500
电动重卡	21~22	1.40	510	260	60	70.6	1.12	14 000	>14 000
	23~24	1.50	553			74.8	1.20	16 000
	25~26	1.60	595			81.3	1.28	19 000
	27~28	1.70	680			84.6	1.36	22 000
	29~30	1.80	723			89.8	1.44	22 000

Table 2

Table 3

Table 4

Demand breakdown plans for different scenarios

场景	原客户编号 $i$	原始需求量/kg	拆分次数	虚拟位置索引	子需求量/kg
I、III	1	12 000	2	$θ 11$ , $θ 12$	6 000, 6 000
	2	14 000	2	$θ 21$ , $θ 22$	7 000, 7 000
	3	15 000	4	$θ 31$ , $θ 32$ , $θ 33$ , $θ 34$	3 750, 3 750, 3 750, 3 750
	4	27 000	4	$θ 41$ , $θ 42$ , $θ 43$ , $θ 44$	6 750, 6 750, 6 750, 6 750
	5	25 000	3	$θ 51$ , $θ 52$ , $θ 53$	8 334, 8 333, 8 333
	11	9 500	2	$θ 111$ , $θ 112$	4 750, 4 750
II、IV	4	27 000	2	$θ 41$ , $θ 42$	13 500,13 500
II、IV	5	25 000	2	$θ 51$ , $θ 52$	12 500,12 500

Table 4

Table 5

Fig. 6

Fig. 7

Table 6

Fig. 8

Table 7

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	供应点编号	x	y	日供应量/kg
原料供应端	1	125	610	30 000
	2	480	785	38 000
	3	720	290	39 000
	4	310	450	32 000
	5	890	150	30 000
	6	670	830	31 000
	7	200	370	39 500
	8	540	670	30 500
成品需求端	1	123	789	12 000
	2	874	234	14 000
	3	456	654	15 000
	4	231	876	27 000
	5	789	345	25 000
	6	345	123	8 550
	7	567	798	8 000
	8	912	401	7 000
	9	678	658	6 000
	10	134	924	5 000
	11	802	312	9 500
	12	298	789	7 500
	13	556	214	8 500
	14	715	562	6 500
	15	489	381	7 200
	16	264	640	900
	17	832	478	1 500
	18	901	839	1 800
	19	378	295	2 200
	20	147	610	2 700
	21	623	742	1 000
	22	510	309	1 200
	23	394	531	2 000
	24	775	183	2 500
	25	286	865	3 000
	26	431	702	500
	27	697	453	400
	28	852	260	750
	29	555	511	850
	30	370	698	800

场景	总行驶里程/km	成品配送量/t	原料回运量/t	单位负载效率/(kg/km)	配置车辆数
I	5 167.1	188.85	—	36.5	24
II	3 712.6	188.85	—	50.9	16
III	5 954.1	188.85	168.98	60.1	24
IV	4 807.1	188.85	183.85	74.4	20

场景	总能耗/ (kW·h)	单位能耗/ (kW·h/t)	能量强度/ (kW·h/km)	SOE 标准差
I	5 683.8	30.1	1.10	14.3
II	4 766.7	25.2	1.28	13.7
III	6 549.4	18.3	1.10	16.1
IV	5 372.3	14.4	1.12	13.3

场景	净成本/万元	车辆成本/万元	距离成本/元	维护成本/元	时间窗成本/元	单位成本配送效能/(kg/万元)
I	1 191.9	1 464.0	4 547.1	5 140.0	7 254.5	158.4
II	788.7	1 061.1	3 813.4	4 624.0	5 786.6	239.4
III	1 190.3	1 464.0	5 239.5	6 168.0	7 018.3	158.7
IV	877.3	1 152.1	4 297.8	5 432.0	–555.4	215.3