考虑节点中断风险的弹性供应链设计方法

doi:10.11949/0438-1157.20231094

摘要/Abstract

摘要：

供应链全球化发展为企业带来广阔前景，但庞大的供应链体系也造成供应链中断风险激增，为企业带来了巨大潜在风险。然而，由于供应链中断风险难以预知，针对中断的弹性供应链优化设计方法还远不成熟。在此背景下，建立了一种新颖的弹性供应链设计优化方法，旨在增强供应链抵御中断的能力。提出并定义了节点中断风险评价指标（NDII），以反映节点中断对供应链的影响程度。定义风险系数（RW）以表征供应链风险程度，避免了对风险场景及其概率分布的预测，同时体现企业对供应链弹性的重视程度。基于鲁棒优化（RO）方法，建立了对高风险节点惩罚的NDII-RO优化方法，并应用于广东省生物燃料供应链优化设计。优化结果表明该方法可实现抵御中断风险的弹性供应链设计。

关键词: 供应链中断, 节点中断风险评价指标, 供应链弹性, 优化, 优化设计, 生物燃料

Abstract:

The global development of supply chains has brought broad prospects to enterprises, but huge supply chain systems are faced with increasing risk of disruption, bringing huge potential risks to enterprises. Nevertheless, due to the unpredictability of these disruptions, the design of resilient supply chains to effectively mitigate disruptions remains an underdeveloped field. Against this background, this paper establishes a novel resilient supply chain design optimization method aimed at enhancing the supply chain's ability to withstand disruptions. Node disruption impact index (NDII) is introduced and defined to reflect the severity of node disruptions. Risk weight (RW) is defined to represent the degree of supply chain risk, thus avoiding predictions regarding risk scenarios and their probability distributions. RW also highlights the importance enterprises place on supply chain resilience. Based on robust optimization (RO) techniques, we establish an NDII-RO optimization framework that penalizes high-risk nodes. This method is applied to the optimization of a biofuel supply chain in Guangdong province. The optimization results demonstrate that this approach is capable of achieving a resilient supply chain design that can withstand disruptions.

Key words: supply chain disruptions, node disruption impact index, supply chain resilience, optimization, optimal design, biofuel

中图分类号:

F 274

余洋, 罗祎青, 魏荣辉, 张文慧, 袁希钢. 考虑节点中断风险的弹性供应链设计方法[J]. 化工学报, 2024, 75(1): 338-353.

Yang YU, Yiqing LUO, Ronghui WEI, Wenhui ZHANG, Xigang YUAN. A resilient supply chain design method considering node disruption risk[J]. CIESC Journal, 2024, 75(1): 338-353.

图/表 16

图1 生物燃料供应链超结构

Fig.1 Biofuel supply chain superstructure

图2 闲置秸秆原料及生物燃料需求的地理分布

Fig.2 Geographical distribution of unused straw resources and biofuel demand

图3 潜在前处理中心和生物精炼厂地理位置分布

Fig.3 Geographical distribution of potential pre-processing centers and bio-refineries

图4 各秸秆收集中心NDII值

Fig.4 NDII values for straw collection centers

表1 三种优化方法结果的对比

Table 1 Comparison of results from three optimization methods

优化结果	DO	RO	NDII-RO
总成本/10⁶ CNY	720.83	728.84	806.88
备用仓储/10⁴ t	0	0	19.71

图5 RW=0.05条件下三种优化方法的供应链决策

Fig.5 Supply chain decisions with three optimization methods when RW=0.05

表2 中断场景下三种方法的表现

Table 2 Performance of three methods under disruptions

优化方法	茂名		湛江
优化方法	需求缺口/10⁴ gal	总成本/10⁶ CNY	需求缺口/10⁴ gal	总成本/10⁶ CNY
DO	618.39	896.90	681.08	924.95
RO	618.39	896.98	681.08	926.50
NDII-RO	130.58	836.49	193.27	867.32

图6 中断发生时三种决策的额外成本

Fig.6 Additional costs of three decisions under disruptions

图7 湛江生物质原料供应中断下不同RW取值对两种优化结果的影响

Fig.7 Influence of different RW values on the outcomes of two optimization methods when Zhanjiang disrupted

表A1 各地级市生物质供应量上限

Table A1 Upper limit of biomass supply in cities of Guangdong province

地级市	s/10⁴ t	地级市	s/10⁴ t
湛江	49.828	河源	11.995
茂名	47.295	汕头	10.518
肇庆	28.931	云浮	10.426
广州	26.695	潮州	10.268
惠州	23.003	阳江	9.962
梅州	22.640	汕尾	9.701
清远	22.422	中山	4.199
韶关	17.357	东莞	3.542
揭阳	17.007	珠海	1.407
江门	14.841	深圳	1.371
佛山	12.235

表A2 各地级市生物燃料需求量

Table A2 Biofuel demand in cities of Guangdong province

地级市	b/10⁴ gal	地级市	b/10⁴ gal
广州	1872.754	中山	236.560
深圳	2034.138	江门	238.889
珠海	257.494	阳江	100.554
汕头	194.351	湛江	236.146
佛山	806.398	茂名	245.312
韶关	103.079	肇庆	175.786
河源	84.509	清远	133.163
梅州	86.766	潮州	82.577
惠州	330.171	揭阳	150.276
汕尾	85.442	云浮	75.553
东莞	720.084

表B1 前处理中心建厂成本

Table B1 Capital cost of pro-processing centers

l	$C l P P$ /10⁴ t	$c l P P$ /CNY
1	15	55125
2	25	91875
3	35	128625
4	45	165375
5	55	202125

表B1 前处理中心建厂成本

Table B1 Capital cost of pro-processing centers

l	$C l P P$ /10⁴ t	$c l P P$ /CNY
1	15	55125
2	25	91875
3	35	128625
4	45	165375
5	55	202125

表B2 生物精炼厂建厂成本

Table B2 Capital cost of biorefineries

l	$p l B R$ /10⁴ gal	$c l B R$ /10⁴ CNY
1	1400	1734.6
2	2000	2478
３	2600	3221.4
４	3200	3964.8
５	3800	4708.2

表B2 生物精炼厂建厂成本

Table B2 Capital cost of biorefineries

l	$p l B R$ /10⁴ gal	$c l B R$ /10⁴ CNY
1	1400	1734.6
2	2000	2478
３	2600	3221.4
４	3200	3964.8
５	3800	4708.2

表C1 广东省各地级市经纬度

Table C1 Latitude and longitude of cities in Guangdong province

地级市	经度/(°)	纬度/(°)	地级市	经度/(°)	纬度/(°)
广州	113.2810	23.1252	梅州	116.1180	24.2991
韶关	113.5920	24.8013	汕尾	115.3640	22.7745
深圳	114.0860	22.5470	河源	114.6980	23.7463
珠海	113.5540	22.2250	阳江	111.9750	21.8592
汕头	116.7080	23.3710	清远	113.0510	23.6850
佛山	113.1230	23.0288	东莞	113.7460	23.0462
江门	113.0950	22.5904	中山	113.3820	22.5211
湛江	110.3650	21.2749	潮州	116.6320	23.6617
茂名	110.9190	21.6598	揭阳	116.3560	23.5438
肇庆	112.4730	23.0515	云浮	112.0440	22.9298
惠州	114.4130	23.0794

表C2 物流相关参数

Table C2 Logistics-related parameters

参数	固体(秸秆)	液体(生物乙醇)
C_m	35 CNY/t	0.14 CNY/gal
V	25 t/辆	8000 gal/辆
C_Ｄ	8.4 CNY/(辆·km)	9.1 CNY/(辆·km)
C_T	203 CNY/(辆·h)	224 CNY/(辆·h)
v	40 km/h

表D1 其他参数

Table D1 Other parameters

γ	δ	φ	c^backup	s^backup	p^prod	c^pro
275 gal/t	30%	30%	400 CNY/t	53.346×10⁴ t	4.9 CNY/gal	35 CNY/gal

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