EAGER: Optimization with Data Acquisition in Transportation Engineering

这个早期概念的探索性研究资助（EAGER）项目将寻求获得基础知识，以确定应该购买的数据量，用于统计估计优化模型的系数。这将通过检查用于校准优化模型的数据成本与使用这些模型获得的解决方案的质量之间的权衡来完成。这种权衡将主要探讨与运输有关的问题，如车辆分配任务，车辆路线，车队规模和网络流模型。研究结果将有助于世界各地的运输机构和公司分配资源并改善其专业实践。除了运输问题，这项工作可能会产生的方法，将是有价值的其他应用，行业和学科，包括工程，运筹学，经济学和management.Traditional优化方法不考虑成本获取输入数据，这可能是非常重要的工程和运筹学的许多应用，包括运输。考虑数据获取成本的必要性在现在和将来甚至更大，因为用于运输的数据的所有权正在从私人拥有转向私人拥有。我们的目标是开发方法来优化校准一些特定的运输优化模型所获得的数据量，然后探索这些方法和准则是否以及如何推广到其他领域的其他优化问题。数据的质量和成本可能决定应该获得多少数据。除了涉及真实世界数据的广泛实验外，该项目还将研究数据集的结构及其基本分布，以探索如何开发通用决策框架，以确定“我们应该购买多少数据来校准优化模型”？

This EArly-concept Grant for Exploratory Research (EAGER) project will seek to acquire fundamental knowledge in determining the amount of data that should be purchased for statistically estimating the coefficients of optimization models. This will be done by examining the tradeoffs between the cost of data used for calibrating optimization models and the quality of solutions obtained with those models. Such tradeoffs will be explored mainly for problems relevant in transportation, such as vehicle assignment to tasks, vehicle routing, fleet sizing, and network flow models. The results should help transportation agencies and companies across the world in allocating resources and improving their professional practice. Beyond transportation problems, this work may yield methods that would be valuable in other applications, industries and disciplines, including engineering, operations research, economics and management.Traditional optimization approaches do not account for the cost of acquiring input data, which can be very important in many applications of engineering and operations research, including transportation. The need for considering the cost of data acquisition is even greater now and in the future when the ownership of the data for transportation is moving from publically owned to privately owned. The goals are to develop methods for optimizing the amounts of data acquired for calibrating some specific transportation optimization models and then explore whether and how such methods and guidelines may be generalized to other optimization problems in other fields. The quality and cost of data may determine how much should be obtained. In addition to extensive experiments involving real-world data, the project will examine the structures of datasets and their underlying distributions, to explore how a generic decision making framework may be developed for determining "how much data we should purchase in order to calibrate an optimization model"?