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

这笔拨款将支持研究如何建立一个实时市场，将货运与可用的承运人相匹配。在这样的系统中，货主向系统管理员提出请求，自动服务将把运输工作与可能接受这些工作的承运商匹配起来。这样一个系统的好处包括更有效地利用货运能力（更少的空载里程）和降低航运价格，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，并使用实际货运数据测试和验证这些算法。除了对卡车运输业、更广泛的经济和减少城市拥堵有好处之外，教育和推广活动还将为不同的K-12和大学生群体带来好处。该研究方法进一步扩展了传统算法和启发式算法在随机多臂强盗问题和车辆路径问题中的应用。货运匹配问题的特征是排队等待服务的随机需求的到来，以及需要估计匹配将对承运人施加的成本，这是承运人对特定请求的保留价格。该模型通过扩展传统的多武装强盗问题来表示这一点，允许“武装”的集合随着时间的推移而变化，因为载体进入和离开系统。与传统的车辆路线问题不同，在货物匹配问题中，有效和稳健地估计由于纳入货运作业而导致的路线成本变化比在细粒度水平上得出详细的路线计划更重要。研究小组将研究这两个领域的新算法，在模拟框架中实现它们，并用现实世界的数据对它们进行测试，以检验排队强盗方法优于拍卖和其他市场机制的假设。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.