Supply Chain Analytics最新文献_第6页

Application of artificial intelligence in reverse logistics: A bibliometric and network analysis 人工智能在逆向物流中的应用：文献计量和网络分析

Supply Chain Analytics Pub Date : 2024-07-27 DOI: 10.1016/j.sca.2024.100076

Oyshik Bhowmik , Sudipta Chowdhury , Jahid Hasan Ashik , GM Iqbal Mahmud , Md Muzahid Khan , Niamat Ullah Ibne Hossain

{"title":"Application of artificial intelligence in reverse logistics: A bibliometric and network analysis","authors":"Oyshik Bhowmik , Sudipta Chowdhury , Jahid Hasan Ashik , GM Iqbal Mahmud , Md Muzahid Khan , Niamat Ullah Ibne Hossain","doi":"10.1016/j.sca.2024.100076","DOIUrl":"10.1016/j.sca.2024.100076","url":null,"abstract":"<div><p>Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000190/pdfft?md5=675a03af106c4975660c495cacb17d46&pid=1-s2.0-S2949863524000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection 用于可解释供应商选择的多标准决策和机器学习混合方法

Supply Chain Analytics Pub Date : 2024-06-29 DOI: 10.1016/j.sca.2024.100074

Ahmad Abdulla , George Baryannis

{"title":"A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection","authors":"Ahmad Abdulla , George Baryannis","doi":"10.1016/j.sca.2024.100074","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100074","url":null,"abstract":"<div><p>Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often applied are unfamiliar to stakeholders and have limited explainability, posing a significant barrier to adopting intelligent approaches in supply chains. To address this issue, we propose a hybrid supplier selection framework that combines interpretable data-driven AI techniques with multi-criteria decision-making (MCDM) approaches: the former aims to reduce the complexity of the supplier selection problem, while the latter ensures familiarity to supply chain stakeholders by retaining MCDM at the heart of the supplier selection process. The framework is validated through two real-world case studies supporting supplier selection decisions in oil, gas, and aerospace manufacturing companies. Preliminary results from our case studies suggest that the framework can achieve comparable performance to approaches utilising only machine learning while offering the added benefits of end-to-end explainability and increased familiarity.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000177/pdfft?md5=5637ab937560ebd25525e4f54ac4c038&pid=1-s2.0-S2949863524000177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supply chain competitiveness through agility and digital technology: A bibliometric analysis 通过敏捷性和数字技术提高供应链竞争力：文献计量分析

Supply Chain Analytics Pub Date : 2024-06-28 DOI: 10.1016/j.sca.2024.100073

Emmanuel Susitha , Amila Jayarathna , H.M.R.P Herath

{"title":"Supply chain competitiveness through agility and digital technology: A bibliometric analysis","authors":"Emmanuel Susitha , Amila Jayarathna , H.M.R.P Herath","doi":"10.1016/j.sca.2024.100073","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100073","url":null,"abstract":"<div><p>Supply chain competitiveness and agility are matured areas in supply chain management. While there is an ongoing evolution in digital technology alongside supply chain competitiveness and agility, the literature appears to have limited bibliometric reviews on how digital technology impacts these aspects. This study examines supply chain competitiveness with bibliometric analysis, focusing on the critical elements of supply chain agility and the impact of rapidly advancing digital technologies. The study bridges the gap between management and technology disciplines. Employing the PRISMA methodology, 147 scholarly articles were meticulously selected and analysed, adopting a multifaceted analytical approach that combines bibliometric and descriptive analyses. This thorough literature synthesis reveals a profound and intricate connection between supply chain agility and digital technology, underscoring their joint significance in fostering competitive advantage within the dynamic business landscape. This investigation contributes to the existing body of knowledge by identifying seven distinct clusters, offering a detailed map of the current research landscape. This study charts a course for future academic inquiries into this critical area and provides valuable insights for practitioners. It underscores the importance of integrating agile supply chain practices and digital technologies to maintain and enhance competitive positioning in today’s fast-paced business environment.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000165/pdfft?md5=ca3444f3e74371f974f42476ebbb8d6f&pid=1-s2.0-S2949863524000165-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of vehicle routing problems and models in multi-echelon distribution networks 多梯队配送网络中的车辆路线问题和模型系统回顾

Supply Chain Analytics Pub Date : 2024-06-18 DOI: 10.1016/j.sca.2024.100072

Peter Nielsen , Mahekha Dahanayaka , H.Niles Perera , Amila Thibbotuwawa , Deniz Kenan Kilic

{"title":"A systematic review of vehicle routing problems and models in multi-echelon distribution networks","authors":"Peter Nielsen , Mahekha Dahanayaka , H.Niles Perera , Amila Thibbotuwawa , Deniz Kenan Kilic","doi":"10.1016/j.sca.2024.100072","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100072","url":null,"abstract":"<div><p>The vehicle routing problem (VRP) is a combinatorial optimization problem that determines optimal routes to enhance distribution efficiency. One of the most popular strategies in freight distribution is multi-echelon distribution. Multi-echelon distribution networks often apply to supply chain management, land transportation, the maritime industry, aviation, etc., and rely on VRP. This comprehensive review systematically analyses 382 papers retrieved through the Scopus database. We use a bibliometric and network analysis tool to complete a systematic literature mapping identifying key interrelationships and research clusters. The analysis depicts five main research clusters: green logistics and decision analysis, scheduling and inventory optimization, VRP for city logistics, mathematical modeling and optimization, and outbound logistics and customer service, identified based on author keywords of the systematically derived paper pool. Each cluster is provided with foundational knowledge, concepts, theories, and employed techniques. Finally, future studies are suggested to explore more comprehensive investigation in highly discussed domains like city logistics problems in e-commerce, vehicle routing problems for sustainable logistics, and technological advancement-based applications.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000153/pdfft?md5=2a68c08fb7b8eef36b3fc18102f86e43&pid=1-s2.0-S2949863524000153-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages 具有威布尔分布劣化、斜坡型需求、碳减排和短缺的综合库存管理模型

Supply Chain Analytics Pub Date : 2024-06-07 DOI: 10.1016/j.sca.2024.100069

Muthusamy Palanivel, Murugesan Venkadesh, Selvaraj Vetriselvi

{"title":"A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages","authors":"Muthusamy Palanivel, Murugesan Venkadesh, Selvaraj Vetriselvi","doi":"10.1016/j.sca.2024.100069","DOIUrl":"10.1016/j.sca.2024.100069","url":null,"abstract":"<div><p>This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100069"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000128/pdfft?md5=f4ac2487f3dd8a8eb4591780d22300f2&pid=1-s2.0-S2949863524000128-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An investigation of the interrelationship among circular supply chain management indicators in small and medium enterprises 中小企业循环供应链管理指标之间相互关系的调查

Supply Chain Analytics Pub Date : 2024-05-28 DOI: 10.1016/j.sca.2024.100068

Rangga Primadasa , Dina Tauhida , Bellachintya Reira Christata , Imam Abdul Rozaq , Salman Alfarisi , Ilyas Masudin

{"title":"An investigation of the interrelationship among circular supply chain management indicators in small and medium enterprises","authors":"Rangga Primadasa , Dina Tauhida , Bellachintya Reira Christata , Imam Abdul Rozaq , Salman Alfarisi , Ilyas Masudin","doi":"10.1016/j.sca.2024.100068","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100068","url":null,"abstract":"<div><p>Circular Supply Chain Management (CSCM) is gaining prominence among diverse stakeholders, practitioners, and scholars. However, its adoption remains limited, particularly within Small and Medium Enterprises (SMEs). This study employs Interpretative Structural Modeling (ISM), specifically tailored for SMEs, to elucidate the contextual relationships among CSCM indicators. Furthermore, it employs the Matrice d’Impacts Croisés Multiplication Appliqué à un Classement (MICMAC) analysis to categorize these indicators into driving- dependence power quadrants. Thirteen CSCM indicators are identified and classified into three sustainability dimensions: economic, environmental, and social. The ISM model comprises four levels, with employees’ exposure to hazardous materials at level one, followed by ten indicators at level two, one at level three (reuse, remanufacturing, recycling complexity), and one at level four (eco-material). MICMAC analysis reveals that none of the indicators falls into the autonomous quadrant. Employees’ exposure to hazardous materials is categorized in the dependent indicators’ quadrant, while ten indicators belong to the linkage quadrant. The independent quadrant includes two indicators: eco-material and reuse, remanufacturing, and recycling complexity. SMEs can utilize these CSCM indicators as an initial step toward circularity implementation. The recommended implementation sequence follows the ISM model hierarchy, starting with level four indicators and progressing through levels three, two, and one, acknowledging the influence of higher-level indicators on lower-level ones.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100068"},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000116/pdfft?md5=205bc54fb3ab8c13824289af7ddf3a3a&pid=1-s2.0-S2949863524000116-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bibliometric analysis of data-driven technologies in digital supply chains 数字供应链中数据驱动技术的文献计量分析

Supply Chain Analytics Pub Date : 2024-05-21 DOI: 10.1016/j.sca.2024.100067

Hamed Baziyad , Vahid Kayvanfar , Aseem Kinra

{"title":"A bibliometric analysis of data-driven technologies in digital supply chains","authors":"Hamed Baziyad , Vahid Kayvanfar , Aseem Kinra","doi":"10.1016/j.sca.2024.100067","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100067","url":null,"abstract":"<div><p>Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the core components of data-driven technologies of Industry 4.0, attracting much attention in digital supply chains and leading to a growing tide of academic publications. This study conducts a bibliometric analysis of data-driven technologies in digital supply chains. Additionally, some bibliometric methods, such as co-word analysis, are utilized to study the intellectual structure of the field and present a big picture. The co-word analysis maps data-driven technologies’ intellectual structure in digital supply chains and logistics. 3887 publications from the Web of Science (WoS) and Scopus between 2010 and 2021 were collected and analyzed. Then, a strategic diagram is employed on the co-occurrence network, indicating each theme’s current situation from two aspects of applicability and theory development. The study reveals that IoT and CPS technologies are in their infancy in digital supply chains and logistics, and additional studies are needed to fill the research gaps in this field.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"6 ","pages":"Article 100067"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000104/pdfft?md5=6ee165a6f208bcad8bd9efaee619d7bf&pid=1-s2.0-S2949863524000104-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141090221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A blockchain-based dynamic energy pricing model for supply chain resiliency using machine learning 基于区块链的动态能源定价模型，利用机器学习提高供应链弹性

Supply Chain Analytics Pub Date : 2024-04-03 DOI: 10.1016/j.sca.2024.100066

Moein Qaisari Hasan Abadi , Russell Sadeghi , Ava Hajian , Omid Shahvari , Amirehsan Ghasemi

{"title":"A blockchain-based dynamic energy pricing model for supply chain resiliency using machine learning","authors":"Moein Qaisari Hasan Abadi , Russell Sadeghi , Ava Hajian , Omid Shahvari , Amirehsan Ghasemi","doi":"10.1016/j.sca.2024.100066","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100066","url":null,"abstract":"<div><p>The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the autoregressive integrated moving average (ARIMA) algorithm has the highest accuracy compared to other algorithms.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"6 ","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000098/pdfft?md5=5299e76a1695033b1485c6213eeb968c&pid=1-s2.0-S2949863524000098-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140543451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A quantitative approach for evaluating the impact of increased supply chain visibility 评估提高供应链能见度影响的定量方法

Supply Chain Analytics Pub Date : 2024-04-03 DOI: 10.1016/j.sca.2024.100065

N. Orkun Baycik

{"title":"A quantitative approach for evaluating the impact of increased supply chain visibility","authors":"N. Orkun Baycik","doi":"10.1016/j.sca.2024.100065","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100065","url":null,"abstract":"<div><p>Communication and collaboration between supply chain partners is more important than ever. To achieve this, visibility between different supply chain tiers is essential. Recent literature has discussed the benefits of increased supply chain visibility, but more research is necessary to provide concrete evidence. The main question this article aims to answer is about what parts of a supply chain are critical for establishing and increasing visibility. Toward this end, this study uses the amount of unmet customer demand as a performance measure, and performs simulations and empirical analysis on multi-tier supply chains of various sizes. Results indicate that the customers (i.e., downstream supply chain) are the most critical components, and the managers must focus on increasing visibility with them. In addition, visibility in the downstream can be nearly as effective as full visibility in specific settings: The maximum gap between the amounts of unmet demand for the two settings is about 7%. However, the main value of full visibility becomes more apparent when significant deviations exist between forecasted and actual customer demand amounts. As the experiments demonstrate, full visibility in the entire supply chain is the most effective level of visibility.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"6 ","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000086/pdfft?md5=6c5b5f7b807172966a2eff4fa71efb53&pid=1-s2.0-S2949863524000086-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140557934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A quadratic-linear bilevel programming approach to green supply chain management 绿色供应链管理的二次线性双层程序设计方法

Supply Chain Analytics Pub Date : 2024-03-26 DOI: 10.1016/j.sca.2024.100064

Massimiliano Caramia , Giuseppe Stecca

{"title":"A quadratic-linear bilevel programming approach to green supply chain management","authors":"Massimiliano Caramia , Giuseppe Stecca","doi":"10.1016/j.sca.2024.100064","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100064","url":null,"abstract":"<div><p>Green Supply Chain Management requires coordinated decisions between the strategic and operational organization layers to address strict green goals. Furthermore, linking CO2 emissions to supply chain operations is not always easy. This study proposes a new mathematical model to minimize CO2 emissions in a three-layered supply chain. The model foresees using a financial budget to mitigate emissions contributions and optimize supply chain operations planning. The three-stage supply chain analyzed has inbound logistics and handling operations at the intermediate level. We assume that these operations contribute to emissions quadratically. The resulting bilevel programming problem is solved by transforming it into a nonlinear mixed-integer program by applying the Karush-Kuhn-Tucker conditions. We show, on different sets of synthetic data and on a case study, how our proposal produces solutions with a different flow of goods than a modified linear model version. This results in lower CO<sub>2</sub> emissions and more efficient budget expenditure.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"6 ","pages":"Article 100064"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000074/pdfft?md5=8c771b86cb61cc3677b09b7e3bca3c80&pid=1-s2.0-S2949863524000074-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0