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Article(id=1153728263273828620, tenantId=1146029695717560320, journalId=1146123222451335185, issueId=1153728261185069506, articleNumber=1671-1807(2025)01-0125-09, orderNo=null, doi=10.1007/s11356-021-16077-6, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1721836800000, receivedDateStr=2024-07-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1752999853199, onlineDateStr=2025-07-20, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752999853199, onlineIssueDateStr=2025-07-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752999853199, creator=13701087609, updateTime=1752999853199, updator=13701087609, issue=Issue{id=1153728261185069506, tenantId=1146029695717560320, journalId=1146123222451335185, year='2025', volume='25', issue='1', pageStart='1', pageEnd='361', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752999852701, creator=13701087609, updateTime=1753063089305, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1153993494768116564, tenantId=1146029695717560320, journalId=1146123222451335185, issueId=1153728261185069506, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1153993494768116565, tenantId=1146029695717560320, journalId=1146123222451335185, issueId=1153728261185069506, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=125, endPage=133, ext={EN=ArticleExt(id=1153728263772950799, articleId=1153728263273828620, tenantId=1146029695717560320, journalId=1146123222451335185, language=EN, title=Research Hotspots and Trends of Supply Chain Network Optimization:Visual Analysis of CiteSpace Based on Core Library of WoS Database, columnId=1151877660386357290, journalTitle=Science Technology and Industry, columnName=Industrial Development, runingTitle=null, highlight=null, articleAbstract=

Based on the bibliometric software CiteSpace, the knowledge graph visualization analysis of 1 537 articles on supply chain network optimization sourced from the core library of the Web of Science (WoS) database from 2001 to 2023 was conducted to explore the research overview, hot spots and trends. It is found that there are many researchers and institutions in this field, and Chinese scholars have made significant contributions. The hot topics in this field are robust optimization, facility location, sustainability, etc. The future trend is multi-objective optimization, construction of sustainable supply chains and combine new technologies such as 5G networks, block chain, and a.jpgicial intelligence to help supply chain network optimization.

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运用文献计量软件CiteSpace对2001—2023年Web of Science(WoS)核心合集数据库中收录的1 537篇供应链网络优化文献进行知识图谱可视化分析,探索其研究概况、热点及趋势。研究发现:该领域研究学者及机构众多,且中国学者贡献显著;该领域的热点主题为鲁棒优化、设施选址、可持续性等;未来趋势为多目标优化和构建可持续供应链,以及结合5G网络、区块链、人工智能等新技术助力供应链网络优化。

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陈泽洋(2000—),女,河南洛阳人,硕士研究生,研究方向为国际物流与跨境电商;

王昕(1978—),女,四川达州人,副教授,研究方向为供应链管理;

刘思含(1999—),男,贵州铜仁人,硕士研究生,研究方向为国际物流与跨境电商;

吴悠扬(2001—),女,湖北孝感人,硕士研究生,研究方向为国际物流与跨境电商;

冯瑞(1999—),男,广东清远人,硕士研究生,研究方向为国际物流与跨境电商。

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陈泽洋(2000—),女,河南洛阳人,硕士研究生,研究方向为国际物流与跨境电商;

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陈泽洋(2000—),女,河南洛阳人,硕士研究生,研究方向为国际物流与跨境电商;

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王昕(1978—),女,四川达州人,副教授,研究方向为供应链管理;

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王昕(1978—),女,四川达州人,副教授,研究方向为供应链管理;

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刘思含(1999—),男,贵州铜仁人,硕士研究生,研究方向为国际物流与跨境电商;

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刘思含(1999—),男,贵州铜仁人,硕士研究生,研究方向为国际物流与跨境电商;

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吴悠扬(2001—),女,湖北孝感人,硕士研究生,研究方向为国际物流与跨境电商;

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吴悠扬(2001—),女,湖北孝感人,硕士研究生,研究方向为国际物流与跨境电商;

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冯瑞(1999—),男,广东清远人,硕士研究生,研究方向为国际物流与跨境电商。

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冯瑞(1999—),男,广东清远人,硕士研究生,研究方向为国际物流与跨境电商。

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排名 作者 发文量/篇
1 KANNAN GOVINDAN 19
2 ARMIN JABBARZADEH 13
3 MIR SAMAN PISHVAEE 12
4 ANNA NAGURNEY 11
5 S ALI TORABI 9
6 MOHAMMAD MARUFUZZAMAN 9
7 HAMED SOLEIMANI 9
8 ALI DIABAT 9
9 BEHNAM FAHIMNIA 7
10 ZHIHAI ZHANG 6
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发文量前10的作者及其发文量

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排名 作者 发文量/篇
1 KANNAN GOVINDAN 19
2 ARMIN JABBARZADEH 13
3 MIR SAMAN PISHVAEE 12
4 ANNA NAGURNEY 11
5 S ALI TORABI 9
6 MOHAMMAD MARUFUZZAMAN 9
7 HAMED SOLEIMANI 9
8 ALI DIABAT 9
9 BEHNAM FAHIMNIA 7
10 ZHIHAI ZHANG 6
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序号 发文机构 发文量/篇
1 德黑兰大学 66
2 伊斯兰阿扎德大学 59
3 伊朗理工大学 46
4 香港理工大学 27
5 阿米尔卡比尔理工大学 27
6 南丹麦大学 21
7 皇家墨尔本理工大学 18
8 印度理工学院 17
9 马萨诸塞州大学 17
10 新加坡国立大学 15
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发文量前10的发文机构及其发文量

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序号 发文机构 发文量/篇
1 德黑兰大学 66
2 伊斯兰阿扎德大学 59
3 伊朗理工大学 46
4 香港理工大学 27
5 阿米尔卡比尔理工大学 27
6 南丹麦大学 21
7 皇家墨尔本理工大学 18
8 印度理工学院 17
9 马萨诸塞州大学 17
10 新加坡国立大学 15
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序号 国家 中心性 发文量/篇
1 美国 0.59 350
2 中国 0.27 264
3 伊朗 0.24 166
4 印度 0.43 76
5 英国 0.57 68
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发文量前5的国家及其中心性和发文量

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序号 国家 中心性 发文量/篇
1 美国 0.59 350
2 中国 0.27 264
3 伊朗 0.24 166
4 印度 0.43 76
5 英国 0.57 68
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排名 中心性 关键词 排名 中心性 关键词
1 0.21 decomposition(分解) 6 0.15 environment(环境)
2 0.20 facility location(设施选址) 7 0.14 competition(竞争)
3 0.19 chain(链) 8 0.14 challenge(挑战)
4 0.17 coordination(协同) 9 0.14 service level(服务水平)
5 0.17 distribution system design(分销系统设计) 10 0.12 information(信息)
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中心性排名前10的关键词

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1 0.21 decomposition(分解) 6 0.15 environment(环境)
2 0.20 facility location(设施选址) 7 0.14 competition(竞争)
3 0.19 chain(链) 8 0.14 challenge(挑战)
4 0.17 coordination(协同) 9 0.14 service level(服务水平)
5 0.17 distribution system design(分销系统设计) 10 0.12 information(信息)
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序号 聚类名称 聚类子簇
英文 中文 英文 中文
#0 supply chain network design 供应链网络设计 flexibility; social welfare; risk analysis; ambiguity; fuzzy programming; covid-19 pandemic 柔性;社会福利;风险分析;模糊性;模糊规划;COVID-19大流行
#1 management 管理 model; facility location; optimization; system; demand uncertainty 模型;设施选址;优化;系统;需求不确定性
#2 sustainability 可持续性 multi-objective optimization; heuristics; profit;OEE; stochastic demand 多目标优化;启发式;利润;设备综合效率;随机需求
#3 sensitivity analysis 敏感性分析 tactical planning; disruption risk; robust optimization techniques 战略规划;中断风险;鲁棒优化技术
#4 circular economy 循环经济 Grey-DEMATEL; integer programming; closed-loop supply chain network; energy efficiency; location-routing optimization Grey-DEMATEL;整数规划;闭环供应链网络;能源效率;选址-路径优化
#5 optimization 优化 simulation; supply chain design;inter-modal transportation; logistics; bullwhip effect 仿真;供应链设计;多式联运;物流;牛鞭效应
#6 robust optimization 鲁棒优化 supply chain management; uncertainty;MILP; infrastructure; network design 供应链管理;不确定性;混合整数线性规划;基础设施;网络设计
#7 supplier selection 供应商选择 mixed-integer nonlinear programming; pharmaceutical supply chain; vaccine supply chain; multi-criteria decision-making 混合整数非线性规划;药品供应链;疫苗供应链;多标准决策
#8 disruption 中断 resilience; supply network; logistics network; robust design; covid-19 弹性;供应网络;物流网络;鲁棒设计;新型冠状病毒肺炎
#9 supply chain optimization 供应链优化 life cycle assessment; water footprint; carbon footprint;fuzzy multi objective decision making; greenhouse gas 生命周期评估、水足迹、碳足迹、模糊多目标决策;温室气体
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关键词聚类结果

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序号 聚类名称 聚类子簇
英文 中文 英文 中文
#0 supply chain network design 供应链网络设计 flexibility; social welfare; risk analysis; ambiguity; fuzzy programming; covid-19 pandemic 柔性;社会福利;风险分析;模糊性;模糊规划;COVID-19大流行
#1 management 管理 model; facility location; optimization; system; demand uncertainty 模型;设施选址;优化;系统;需求不确定性
#2 sustainability 可持续性 multi-objective optimization; heuristics; profit;OEE; stochastic demand 多目标优化;启发式;利润;设备综合效率;随机需求
#3 sensitivity analysis 敏感性分析 tactical planning; disruption risk; robust optimization techniques 战略规划;中断风险;鲁棒优化技术
#4 circular economy 循环经济 Grey-DEMATEL; integer programming; closed-loop supply chain network; energy efficiency; location-routing optimization Grey-DEMATEL;整数规划;闭环供应链网络;能源效率;选址-路径优化
#5 optimization 优化 simulation; supply chain design;inter-modal transportation; logistics; bullwhip effect 仿真;供应链设计;多式联运;物流;牛鞭效应
#6 robust optimization 鲁棒优化 supply chain management; uncertainty;MILP; infrastructure; network design 供应链管理;不确定性;混合整数线性规划;基础设施;网络设计
#7 supplier selection 供应商选择 mixed-integer nonlinear programming; pharmaceutical supply chain; vaccine supply chain; multi-criteria decision-making 混合整数非线性规划;药品供应链;疫苗供应链;多标准决策
#8 disruption 中断 resilience; supply network; logistics network; robust design; covid-19 弹性;供应网络;物流网络;鲁棒设计;新型冠状病毒肺炎
#9 supply chain optimization 供应链优化 life cycle assessment; water footprint; carbon footprint;fuzzy multi objective decision making; greenhouse gas 生命周期评估、水足迹、碳足迹、模糊多目标决策;温室气体
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供应链网络优化的研究热点及趋势——基于WoS核心合集数据库的CiteSpace可视化分析
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陈泽洋 , 王昕 , 刘思含 , 吴悠扬 , 冯瑞
科技和产业 | 产业发展 2025,25(1): 125-133
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科技和产业 | 产业发展 2025, 25(1): 125-133
供应链网络优化的研究热点及趋势——基于WoS核心合集数据库的CiteSpace可视化分析
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陈泽洋, 王昕, 刘思含, 吴悠扬, 冯瑞
作者信息
  • 佛山大学经济贸易学院, 广东 佛山 528000

    • 陈泽洋(2000—),女,河南洛阳人,硕士研究生,研究方向为国际物流与跨境电商;

    王昕(1978—),女,四川达州人,副教授,研究方向为供应链管理;

    刘思含(1999—),男,贵州铜仁人,硕士研究生,研究方向为国际物流与跨境电商;

    吴悠扬(2001—),女,湖北孝感人,硕士研究生,研究方向为国际物流与跨境电商;

    冯瑞(1999—),男,广东清远人,硕士研究生,研究方向为国际物流与跨境电商。

Research Hotspots and Trends of Supply Chain Network Optimization:Visual Analysis of CiteSpace Based on Core Library of WoS Database
Zeyang CHEN, Xin WANG, Sihan LIU, Youyang WU, Rui FENG
Affiliations
  • School of Economics, Foshan University, Foshan 528000, Guangdong, China
出版时间: 2025-01-10 doi: 10.1007/s11356-021-16077-6
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摘要
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运用文献计量软件CiteSpace对2001—2023年Web of Science(WoS)核心合集数据库中收录的1 537篇供应链网络优化文献进行知识图谱可视化分析,探索其研究概况、热点及趋势。研究发现:该领域研究学者及机构众多,且中国学者贡献显著;该领域的热点主题为鲁棒优化、设施选址、可持续性等;未来趋势为多目标优化和构建可持续供应链,以及结合5G网络、区块链、人工智能等新技术助力供应链网络优化。

供应链网络优化  /  CiteSpace  /  知识图谱  /  可视化分析

Based on the bibliometric software CiteSpace, the knowledge graph visualization analysis of 1 537 articles on supply chain network optimization sourced from the core library of the Web of Science (WoS) database from 2001 to 2023 was conducted to explore the research overview, hot spots and trends. It is found that there are many researchers and institutions in this field, and Chinese scholars have made significant contributions. The hot topics in this field are robust optimization, facility location, sustainability, etc. The future trend is multi-objective optimization, construction of sustainable supply chains and combine new technologies such as 5G networks, block chain, and a.jpgicial intelligence to help supply chain network optimization.

supply chain network optimization  /  CiteSpace  /  knowledge graph  /  visual analysis
陈泽洋, 王昕, 刘思含, 吴悠扬, 冯瑞. 供应链网络优化的研究热点及趋势——基于WoS核心合集数据库的CiteSpace可视化分析. 科技和产业, 2025 , 25 (1) : 125 -133 . DOI: 10.1007/s11356-021-16077-6
Zeyang CHEN, Xin WANG, Sihan LIU, Youyang WU, Rui FENG. Research Hotspots and Trends of Supply Chain Network Optimization:Visual Analysis of CiteSpace Based on Core Library of WoS Database[J]. Science Technology and Industry, 2025 , 25 (1) : 125 -133 . DOI: 10.1007/s11356-021-16077-6
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企业的供应链是错综复杂、各环节相互交织和依赖的网络,不同企业间的联系也更加多元,在流程协调、订单管理以及供应商关系维护等方面需要投入更多精力。
供应链网络优化是指通过调整供应链上各环节的布局和能力,以提高整个供应链网络的效率,从而更好地满足市场需求。合理的供应链网络设计和优化可以使企业降低成本、提高资源利用率、加快响应速度、增强服务水平,进而提升整体竞争力。因此,供应链网络优化作为供应链运营管理中的一个重要课题,有其研究的必要性。
本文以Web of Science(WoS)核心合集数据库中供应链网络优化的相关文献为研究对象,运用CiteSpace软件进行知识图谱可视化分析,进而总结该领域的研究现状、热点及趋势。
1 数据来源与研究方法
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1.1 数据来源
本文选取WoS核心合集作为数据来源,分析国际学者在供应链网络优化方面的研究现状、热点及趋势。文献检索以“supply chain network optimization(供应链网络优化)”作为主题关键词,发表时间限制为2001年1月至2023年12月,将文献的类型限定为“论文”,期刊来源设置为“SSCI”,语种限定为“English”。通过人工去除与主题不相关的文献,并用CiteSpace软件删除重复文献,最终获得有效文献1 537篇。
1.2 研究方法
本文采用文献计量和可视化分析方法,通过CiteSpace5.7.R5软件对WoS核心合集数据库中收录的2001—2023年有关供应链网络优化的文献进行研究,探索该领域的研究概况、核心研究议题和前沿动向。CiteSpace是一款由陈超美教授研发的用以可视化分析文献的软件[1],帮助研究人员分析合作网络、主题演化等信息,从而更好地理解研究领域的发展趋势和热点。本文借助该软件进行年度发文量统计并绘制发文作者、机构、国家合作知识图谱,从而了解供应链网络优化研究概况,通过分析关键词共现与聚类知识图谱揭示研究热点,借助关键词时间线探索该研究领域趋势前沿,从而系统地评述该领域研究,为后续相关研究提供有益参考。
2 研究概况分析
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2.1 发文量分析
发文量可以直观反映一个领域的研究热度和受关注度。对2001—2023年供应链网络优化领域的1 537篇文献进行年度发文量统计,通过绘制发文量年度变化趋势图(图1),可以清晰地观察到该领域研究的发展态势。2001—2007年为供应链网络优化研究的初期阶段,关于该领域的研究文献相对较少,表明当时该领域尚未引起广泛关注。2008—2016年,供应链网络优化领域的发文量开始缓慢增长,越来越多的学者开始关注这一领域。2017—2021年供应链网络优化研究发文量逐年增加且增速较快,2021年到达顶峰(217篇)。尽管从2022年开始发文量略有回落,但整体而言,自2017年以来该领域的发文量增长速度显著加快,表明供应链网络优化领域逐渐受到更多关注,进入快速发展阶段。
2.2 发文作者分析
通过分析发文作者可以了解该领域的代表人物和核心团体,发掘作者之间的合作关系。根据检索到的供应链网络优化文献进行发文作者分析,列出发文量前10的作者及其发文量,见表1。
从表1可以看到,发文量最多的是Kannan Govinda(19篇),共有4位作者的发文量在10篇以上。由此可见供应链网络优化这一研究主题已经引起了学者们的重视,且在该领域积累了一定的研究成果。其中Kannan Govinda主要针对闭环供应链网络优化进行研究,对产品的采购、生产、配送、回收和处置进行决策,并通过基于启发式遗传算法的混合整数线性规划模型的方法求解[2]。Armin Jabbarzadeh主要关注供应链网络优化中的随机中断和鲁棒优化问题,以灾害血液供应链[3]、电力供应链[4]、药品供应链[5]等为研究对象,应用拉格朗日松弛算法、弯管分解算法等方法求解进行多目标鲁棒优化。Mir Saman Pishvaee则专注于研究供应链网络优化中的可持续性问题,通过构建多目标模糊数学规划模型,在考虑固有不确定性、成本最小化和环境影响最小化的情况下设计绿色供应链,并应用生命周期评估方法来评估和量化供应链网络配置的不同选择对环境的影响[6]。可以发现主要的几个发文作者都选择通过数学建模,运用启发式遗传算法、拉格朗日松弛算法、弯管分解算法等求解,从而对供应链网络优化问题进行研究。
将文献数据导入CiteSpace软件,时间跨度设置为2001年1月至2023年12月,时间切片为1,勾选节点类型为“Author”,运行后查看可视化结果,阈值设为3,得到发文作者合作图谱(图2)。可以看出,供应链网络优化研究学者众多,其中以Kannan Govinda与Armin Jabbarzadeh两位学者为首形成了密度较大的合作网络,表明这些作者之间学术交流密切且频繁,形成了良好的合作关系。部分学者形成了小型合作网络,表明这些作者之间也有较为密切的合作交流。然而,也存在部分合作群体仅由两人组成,甚至有个别学者独自进行研究,缺乏合作群体。这种状况无疑限制了跨领域深度研究的可能性。为了推动供应链网络优化研究的进一步发展,研究者之间应增强沟通交流,并积极构建稳固的合作关系。
2.3 发文机构分析
通过对WoS核心合集数据库中检索的供应链网络优化文献进行发文机构统计分析,发现共有494个发文机构发表过该领域论文,列出发文量前10的发文机构及其发文量,见表2。可以看出,共有6个发文机构发文量超过20篇,发文量最多的机构为德黑兰大学(66篇),其后为伊斯兰阿扎德大学(59篇)和伊朗科技大学(46篇)。中国的研究机构中,香港理工大学和清华大学分别以27篇和15篇的发文量位居前列,表明它们在推动该领域研究进步方面具有积极作用。
发文机构合作图谱能够直观地揭示不同研究机构之间的合作关系网络。将文献数据导入CiteSpace,节点类型选择“Institution”,其他设置与发文作者分析相同,运行后得到发文机构合作知识图谱(图3)。图谱呈现出较多且较为密集的节点连线,共有494个节点和438条连线,表明发文机构间已构建相对紧密的合作,有助于供应链网络优化研究跨区域科研交流、合作创新。
2.4 发文国家(地区)分析
为揭示研究领域中关注度较高的国家,探究国际科研合作与发展趋势,本文通过文献计量对发文国家进行分析。表3列出了发文量前5的国家,依次为美国、中国、伊朗、印度和英国,它们共同贡献了超过900篇相关文献,在该领域的有效文献总量中所占比例达到了60.1%,是该领域的核心研究国家。此外,由发文国家合作知识图谱(图4)可以看出,以美国、中国、伊朗等国为中心分别形成了多个合作网络,表明这些国家在该领域研究已形成良好的国际合作关系。中国发文量位居世界第2(264篇),但中心性相对较低,表明中国在相关领域中的关键性或影响力不够突出。因而中国学者需要进一步深化国际合作,提升研究水平和发文质量,以提高在该领域的影响力和地位。
3 研究热点与趋势分析
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3.1 关键词共现研究热点分析
关键词共现知识图谱可通过展示不同关键词在同一篇文献内共同出现的频次和关系,揭示研究热点和主题关联。将文献数据导入CiteSpace后,节点类型选择“Keyword”,其余设置不变,运行后得到供应链网络优化研究的关键词共现图谱(图5)。可以看出,“model(模型)”“optimization(优化)”“network design(网络设计)”等为学者们高度关注的关键词。
通过整理得到中心性排名前10的关键词,见表4。中心性较高的关键词有“decomposition(分解)”“facility location(设施选址)”“chain(链)”“coordination(协同)”“distribution system design(分销系统设计)”等,且其中心性均大于0.1,表明这些关键词是该研究领域的重点。
从研究内容来看,关键词“robust optimization(鲁棒优化)”“facility location(设施选址)”“multi-objective optimization(多目标优化)”“disruption(中断)”“inventory(库存)”“vehicle routing problem(车辆路线问题)”等表明这些问题受到学者们的广泛关注。此外,“carbon emission(碳排放)”“product recovery(产品回收)”“reverse logistics(逆向物流)”等涉及“green supply chain(绿色供应链)”“sustainability(可持续性)”的问题也是学者们讨论的重点。
从研究方法来看,关键词“stochastic programming(随机规划)”“genetic algorithm(遗传算法)”“NSGA-II(多目标遗传算法)”“simulation(仿真)”等表明这些方法是学者们研究供应链网络优化时主要应用的模型或方法,“heuristics(启发式)”“particle swarm optimization(粒子群优化)”等算法也有部分学者进行应用。
3.2 关键词聚类研究热点分析
关键词聚类是将具有相似主题或内容的关键词归为一类,同时每个聚类会根据聚类内关键词的共性和特点自动生成一个代表性标签,关键词聚类分析有助于系统地梳理和分类文献数据,揭示研究领域的热点。
在CiteSpace聚类结果中,聚类平均轮廓值(S)和聚类模块值(Q)是两个重要的评估指标。S代表聚类内样本的紧密程度,一般认为,当S大于0.5时,表示聚类结果是合理的,当S大于0.7时,则表示聚类结果是令人信服的。Q用以衡量聚类内部连接的紧密程度与聚类间连接的稀疏程度,一般认为,当Q大于0.3时,意味着划分出来的社团结构是显著的[7]。
本文采用LLR(对数似然率算法)并以关键词为标识提取聚类标签,将最小聚类变量设定为10,形成关键词聚类知识图谱(图6),并列出10个聚类的详细结果(表5)。聚类结果S为0.890 9,大于0.7,Q为0.764 5,大于0.3,表明聚类结果令人信服且网络社团结构显著。
根据图6和表5所呈现的信息,对关键词聚类结果分析如下。
聚类#0:主题为供应链网络设计。学者们主要关注供应链网络中的柔性、风险分析等问题,注重模糊规划方法的应用[8-9]。同时近年来学者们也将新冠疫情等重大公共危机事件的影响纳入供应链网络设计与优化中的重要因素[10-11]。
聚类#1:主题为管理。主要侧重供应链管理中的需求不确定性[12]、设施选址[13]等,通过建模对供应链网络优化问题进行系统研究。
聚类#2:主题为可持续性。主要关注供应链网络优化中的可持续性问题,考虑利润[14]、随机需求[15]、设备综合效率[16]等因素,利用启发式算法[17]等方法进行多目标优化。
聚类#3:主题为敏感性分析。主要从战略规划[18]、中断风险[19]、鲁棒优化[20]等角度分析供应链网络优化中的敏感性。
聚类#4:主题为循环经济。学者们运用整数规划[21]、灰色-决策实验室[22]等方法,从能源效率、选址-路径优化[23]等方面入手研究供应链网络优化如何促进循环经济的开展。
聚类#5:主题为优化。考虑供应链网络设计中多式联运等物流方式应用[24]、牛鞭效应影响、库存管理[25]等方面,同时纳入仿真方法[26-27]探究供应链网络优化问题。
聚类#6:主题为鲁棒优化。主要关注供应链管理中的不确定性、基础设施建设等在鲁棒优化中的影响[28-29],注重应用混合整数线性规划等方法以深入研究供应链网络中的鲁棒优化问题[30]。
聚类#7:主题为供应商选择。侧重从多标准决策、订单分配等因素探究供应链网络优化中的供应商选择问题[31]。同时自新冠疫情爆发后,疫苗供应链[32]、药品供应链[33]等的供应商选择和网络优化问题也受到了学术界的关注。
聚类#8:主题为中断。供应链弹性成为学者们研究的重点之一[34-35],此外鲁棒设计也是研究供应链中断问题的侧重点[36]。自新冠肺炎疫情对全球供应链产生较大冲击后,中断应对成为学者们研究供应链网络优化的关键点[37-38]。
聚类#9:主题为供应链优化。主要侧重从水足迹[39]、碳足迹[40]、温室气体排放[41]等角度探究供应链网络优化中的供应链绿色化问题。此外,生命周期评估也被纳入供应链网络优化的研究方法,用以评估和量化不同供应链网络配置选择对环境的影响[42]。
3.3 研究趋势分析
通过关键词时间线图可以清晰地看到不同时间点上关键词的热度和演变,揭示出研究领域的发展趋势、热点转移以及主题兴衰,对未来可能的研究方向进行展望。借助CiteSpace绘制出供应链网络优化关键词时间线图,如图7所示。
根据关键词时间线图,可将供应链网络优化研究划分为以下3个阶段。
(1)研究起步阶段(2001—2010年)。随着学术界对供应链领域研究的深入,供应链网络优化问题开始引起学者们的关注。关键词“facility location(设施选址)”“distribution system design(分销网络设计)”“uncertainty(不确定性)“inventory management(库存管理)“collaboration(协同)”等表明这些是学者们研究的主要主题。关键词“simulation(仿真)”“genetic algorithm(遗传算法)”“MILP(混合整数线性规划)”等表明这些方法在该阶段已被应用于研究供应链网络优化问题。
(2)研究深化阶段(2011—2016年)。随着供应链网络优化领域研究的不断深入,研究热点更为丰富。首先,学者们多针对“supplier selection(供应商选择)”“agility(敏捷性)”等问题对供应链网络优化展开讨论。其次,关键词“risk(风险)”“disruption(中断)”“reliability(可靠性)”“robust optimization(鲁棒优化)”等表明供应链可靠性、中断应对和鲁棒优化等问题也开始受到学者们的关注。最后,“loop supply chain(循环供应链)”“carbon emission(碳排放)”等关键词表明部分学者开始关注供应链绿色化问题。
(3)加速发展阶段(2017—2023年)。学者们主要关注供应链网络优化中的“multi-objective optimization(多目标优化)”问题和如何构建“sustainable supply chain(可持续供应链)”以促进“circular economy(循环经济)”的形成。此外自新冠疫情爆发后,“humanitarian supply chain(人道主义供应链)”“pharmaceutical supply chain(药品供应链)”“vaccine supply chain(疫苗供应链)”等的网络优化问题也引起学术界探讨。同时,随着科学技术的不断发展,“5G network(5G网络)”“drone delivery(无人驾驶飞机交付)”“block chain(区块链)”“a.jpgicial intelligence(人工智能)”等新技术也被纳入供应链网络优化的研究因素。
4 研究结论
收起
(1)整体而言,供应链网络优化研究领域自2017年开始引起学者们的广泛关注。该领域研究学者较多,且已有部分学者形成了较为密集的合作网络。中国的发文量位居世界第2,但中心性相对较低,未来中国学者需进一步深化国际合作,提升该领域研究水平和发文质量。
(2)对于供应链网络优化,学术界主要针对鲁棒优化、设施选址、供应链中断、库存优化、可持续性等问题,通过数学建模,运用遗传算法、启发式算法、粒子群优化算法等求解研究,部分学者采用了仿真方法进行研究。
(3)研究趋势方面,供应链网络优化研究热点演进趋势大致分为3个阶段。未来趋势主要关注供应链网络多目标优化和构建可持续供应链,同时如何结合5G网络、区块链、人工智能等新技术助力供应链网络优化也是研究重点之一。
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doi: 10.1007/s11356-021-16077-6
  • 接收时间：2024-07-25
  • 首发时间：2025-07-20
  • 出版时间：2025-01-10
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