EAGER: Optimization with Data Acquisition in Transportation Engineering

这个探索性研究的早期概念补助金(AGER)项目将寻求获得基本知识，以确定应该购买的数据量，以便对优化模型的系数进行统计估计。这将通过检查用于校准优化模型的数据成本和利用这些模型获得的解的质量之间的权衡来实现。这种权衡将主要针对运输中的相关问题进行探讨，例如任务的车辆分配、车辆路线、车队规模和网络流模型。这一结果应该有助于世界各地的运输机构和公司分配资源，改进他们的专业实践。除了交通问题，这项工作可能会产生在其他应用、行业和学科中有价值的方法，包括工程、运筹学、经济学和管理学。传统的优化方法不考虑获取输入数据的成本，这在工程和运筹学的许多应用中可能非常重要，包括交通运输。现在和将来，当交通数据的所有权从公有转变为私有时，考虑数据获取成本的必要性甚至更大。其目标是开发方法来优化为校准一些特定的交通优化模型而获得的数据量，然后探索这些方法和指南是否以及如何可以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。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，以探索如何开发一个通用的决策框架，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"?