Ec-Funded Projects’ Lessons Learned In Earth Friendly Freight Transportation

IF 1.1 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY
Irina Yatskiv (Jackiva), Thomas Brauner, Aleksejs Vesjolijs, V. Petrovs
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引用次数: 0

Abstract

Abstract The paper is based on the research project ePIcenter (www.ePIcenterproject.eu) supported by the EU HORIZON 2020 Programme. ePIcenter connects thirty-six partners: port authorities, logistic service providers, manufacturers, academic institutions, and technology partners. The main goal is to develop and test AI driven logistic software solutions, apply new technologies and methodologies to increase the efficiency of global supply chains and reduce their environmental impact. One of the significant aspect the project focusses on is optimisation, using AI, digitalisation, automation and innovations in freight transport and handling technologies. Finally, modelling powerful solutions to enable resilient, efficient and environment friendly supply chains. Knowledge sharing is one of powerful tool for researchers, policymakers, service providers and other stakeholders to develop a holistic and comprehensive common knowledge base. First, a theoretical framework had been developed through identification and review knowledge available from previous projects funded through the European Commission as well as other international funding projects. Second, lessons learned and success stories from previous EC-funded projects and other international research programmes were reviewed and provided in the first year of project implementation.
环保项目在环保货运方面的经验教训
本文基于欧盟HORIZON 2020计划资助的研究项目ePIcenter (www.ePIcenterproject.eu)。震中连接了36个合作伙伴:港务局、物流服务提供商、制造商、学术机构和技术合作伙伴。主要目标是开发和测试人工智能驱动的物流软件解决方案,应用新技术和方法来提高全球供应链的效率并减少其对环境的影响。该项目重点关注的一个重要方面是优化,在货运和装卸技术中使用人工智能、数字化、自动化和创新。最后,对强大的解决方案进行建模,以实现有弹性、高效和环保的供应链。知识共享是研究人员、政策制定者、服务提供者和其他利益相关者建立一个整体和全面的公共知识库的有力工具之一。首先,通过鉴定和审查以前由欧洲委员会资助的项目以及其他国际资助项目所提供的知识,形成了一个理论框架。第二,在项目执行的第一年,审查和提供了以往欧洲共同体资助项目和其他国际研究方案的经验教训和成功案例。
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来源期刊
Transport and Telecommunication Journal
Transport and Telecommunication Journal TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.00
自引率
0.00%
发文量
21
审稿时长
35 weeks
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