I/UCRC FRP: Collaborative Research: The Physical Internet for a Sustainable Logistics Future -- Advancing CELDi's Leadership Position

项目主任推荐物流与配送卓越中心(CELDI)提案#1332047、1332007、1331997、1331989本提案寻求为设在弗吉尼亚理工大学、克莱姆森大学、加州大学伯克利分校和阿肯色大学的物流与配送卓越中心(CELDI)提供资金。基础研究的资金申请是由NSF批准的申请授权的，NSF 11-570。征集邀请研究所/大学合作研究中心提交支持行业定义的基础研究的建议。物理互联网(PI)被设想为一个变革性的、开放的、全球的多式联运物流系统，它利用标准、模块化、可重复使用的集装箱、实时识别和通过共享物流设施进行路线选择。在拟议的研究中，PI侧重于两个方面的操作：(1)集装箱级流程的算法，以便在PI中建立负载并在多式联运接口处处理这些负载；(2)系统级流程的算法，以便对流经PI的负载进行路线、调度和协调。CELDI处于独特的地位，可以推动PI实现可持续的物流未来。拟议研究的更广泛影响包括极大地提高货运系统效率的潜力。成员组织的直接好处包括获得作为这项研究的一部分开发的算法和操作洞察力。其他直接好处可能是来自成员组织的数据被用来通过对其数据的洞察和潜在的业务改进来测试算法。成员组织将可以及早获得研究结果。间接的好处包括提高该协会作为一个组织的声誉和吸引力，这可能导致更好地保留和招募该协会的成员。

Program Director's Recommendation Center for Excellence in Logistics and Distribution (CELDi) Proposal #1332047, 1332007, 1331997, 1331989 This proposal seeks funding for the Center for Excellence in Logistics and Distribution (CELDi) located at Virginia Tech, Clemson University, the University of California-Berkeley, and the University of Arkansas. Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 11-570. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research. The Physical Internet (PI) is envisioned as a transformative, open, global, intermodal logistics system that utilizes standard, modular, reusable containers, real-time identification, and routing through shared logistics facilities. In the proposed research, the PIs focus on two aspects of operations: (1) algorithms for container-level processes so as to build loads in the PI and handle these loads at intermodal interfaces; and (2) algorithms for system-level processes so as to route, schedule and coordinate loads that flow through the PI. CELDi is in a unique position to advance the PI for a sustainable logistics future. The broader impacts of the proposed research include the potential to dramatically increase the efficiency of freight transportation systems. Direct benefits to member organizations include access to the algorithms and operational insights developed as part of this research. Other direct benefits are likely to result as data from member organizations are used to test the algorithms through insights into their data and potential operational improvements. Member organizations will have early access to the results of the research. Indirect benefits include enhancing the reputation and attractiveness of CELDi as an organization, which is likely to lead to better retention and recruitment of CELDi members.