{"title":"供应链中的定量优化模型：分类、趋势和分析","authors":"Hrishikesh Choudhary, L. N. Pattanaik","doi":"10.1007/s11831-025-10252-5","DOIUrl":null,"url":null,"abstract":"<div><p>Supply chains with diverse and conflicting objectives striving for optimal performance often land in NP (Nondeterministic Polynomial)-hard combinatorial optimization problems employing tools from classical and non-classical approaches. This paper aims to collect these studies on quantitative optimization models applied to supply chains and conduct a comprehensive review of the literature published during 2006–2023. A total of 283 research articles were collected from several relevant databases to present the taxonomy, trend and insights gained from the analysis of the data. The taxonomies presented are based on extended classification schemes such as modelling approach, objective functions, data sources, optimization tools and their hybridization, etc. Five research questions (RQs) are formed based on the required taxonomy to properly guide the review work. Statistical analysis has been carried out to comprehend any transitions observed during the review period. The review is concluded with key observations on the status of research, and future directions.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 6","pages":"3787 - 3820"},"PeriodicalIF":12.1000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative Optimization Models in Supply Chains: Taxonomy, Trends and Analysis\",\"authors\":\"Hrishikesh Choudhary, L. N. Pattanaik\",\"doi\":\"10.1007/s11831-025-10252-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Supply chains with diverse and conflicting objectives striving for optimal performance often land in NP (Nondeterministic Polynomial)-hard combinatorial optimization problems employing tools from classical and non-classical approaches. This paper aims to collect these studies on quantitative optimization models applied to supply chains and conduct a comprehensive review of the literature published during 2006–2023. A total of 283 research articles were collected from several relevant databases to present the taxonomy, trend and insights gained from the analysis of the data. The taxonomies presented are based on extended classification schemes such as modelling approach, objective functions, data sources, optimization tools and their hybridization, etc. Five research questions (RQs) are formed based on the required taxonomy to properly guide the review work. Statistical analysis has been carried out to comprehend any transitions observed during the review period. The review is concluded with key observations on the status of research, and future directions.</p></div>\",\"PeriodicalId\":55473,\"journal\":{\"name\":\"Archives of Computational Methods in Engineering\",\"volume\":\"32 6\",\"pages\":\"3787 - 3820\"},\"PeriodicalIF\":12.1000,\"publicationDate\":\"2025-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Computational Methods in Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11831-025-10252-5\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Computational Methods in Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11831-025-10252-5","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Quantitative Optimization Models in Supply Chains: Taxonomy, Trends and Analysis
Supply chains with diverse and conflicting objectives striving for optimal performance often land in NP (Nondeterministic Polynomial)-hard combinatorial optimization problems employing tools from classical and non-classical approaches. This paper aims to collect these studies on quantitative optimization models applied to supply chains and conduct a comprehensive review of the literature published during 2006–2023. A total of 283 research articles were collected from several relevant databases to present the taxonomy, trend and insights gained from the analysis of the data. The taxonomies presented are based on extended classification schemes such as modelling approach, objective functions, data sources, optimization tools and their hybridization, etc. Five research questions (RQs) are formed based on the required taxonomy to properly guide the review work. Statistical analysis has been carried out to comprehend any transitions observed during the review period. The review is concluded with key observations on the status of research, and future directions.
期刊介绍：
Archives of Computational Methods in Engineering
Aim and Scope:
Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication.
Review Format:
Reviews published in the journal offer:
A survey of current literature
Critical exposition of topics in their full complexity
By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.
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