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Collaborative Research: Real-Time Stochastic Matching Models for Freight Electronic Marketplace

合作研究：货运电子市场的实时随机匹配模型

基本信息

批准号：
1826320
负责人：
Stephen Boyles
金额：
$ 33.33万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826320&HistoricalAwards=false
关键词：
Collaborative Research Real Time Stochastic

项目摘要

This grant will support research investigating how a real-time marketplace might be constructed to match freight shipments with available carriers. In such a system, freight shippers make a request to the system manager, and an automated service will match shipping jobs to carriers who are likely to accept them. The benefits of such a system include more efficient use of freight capacity (fewer empty miles) and reduced shipping prices, resulting in economic benefits, reduced fuel consumption and emissions, and reduced congestion. These benefits will directly contribute to economic prosperity in the nation. This award supports the basic research needed to design and implement such a system, particularly focusing on the matching mechanism between shippers and carriers, and methods for quickly estimating the additional cost carriers will incur should they agree to a match. More specifically, the research work involves developing new algorithms in these areas, and testing and validating these algorithms with real-world freight data. In addition to benefits in the trucking industry, broader economy, and reduced urban congestion, the education and outreach activities will provide benefits to diverse K-12 and university student populations.The research approach further extends traditional algorithms and heuristics for stochastic multi-armed bandit problems and the vehicle routing problem. A freight match problem is characterized by the arrival of stochastic demands that are queued for service and the need to estimate the costs a match would impose on a carrier, which serves as reserve prices for carriers for particular requests. The model represents this by extending the traditional multi-armed bandit problem to allow the set of "arms" to vary over time, as carriers enter and leave the system. Different from a traditional vehicle routing problem, efficiently and robustly estimating changes in routing costs from incorporating freight jobs is more important in the freight matching problem than deriving detailed routing plans at a fine-grained level. The research team will investigate new algorithms in both of these areas, implementing them in a simulation framework and testing them with real-world data, to test the hypothesis that the queueing bandit approach can outperform auctions and alternative market mechanisms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这笔拨款将支持研究如何构建实时市场以将货运与可用承运人相匹配。在这样的系统中，货物托运人向系统管理员提出请求，自动化服务将把运输作业与可能接受它们的承运人匹配起来。这种系统的好处包括更有效地利用货运能力（更少的空英里）和降低运输价格，从而产生经济效益，减少燃料消耗和排放，并减少拥堵。这些好处将直接促进国家的经济繁荣。该奖项支持设计和实施这样一个系统所需的基础研究，特别是关注托运人和承运人之间的匹配机制，以及快速估计承运人同意匹配时将产生的额外成本的方法。更具体地说，研究工作涉及在这些领域开发新的算法，并使用真实世界的货运数据测试和验证这些算法。除了在卡车运输业的好处，更广泛的经济，减少城市拥堵，教育和推广活动将提供不同的K-12和大学生population.The研究方法的好处进一步扩展了传统的算法和随机多武装土匪问题和车辆路径问题的算法。货运匹配问题的特点是随机需求的到来，排队等待服务，并需要估计成本的匹配将强加给承运人，这是作为特定请求的承运人的保留价格。该模型通过扩展传统的多武装土匪问题来表示这一点，以允许随着时间的推移，随着运营商进入和离开系统的“武器”的集合而变化。与传统的车辆路径问题不同，在货运匹配问题中，有效和鲁棒地估计包含货运作业的路径成本的变化比在细粒度级别上导出详细的路径计划更重要。该研究团队将研究这两个领域的新算法，在模拟框架中实现它们，并使用真实世界的数据进行测试，以验证假设，即拍卖强盗方法可以优于拍卖和替代市场机制。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。