Real-time Planning and Optimization under Uncertainty in Logistics and Transportation

物流和运输相关业务通常占项目总成本的很大一部分，并与主要污染排放有关。私营部门和公共部门都呼吁提高效率，减少交通运输，以缓解交通拥堵和污染排放。虽然数学优化是为这些领域提供有效规划解决方案的最成功工具之一，但传统的模型是基于对问题参数的预测。从不同来源获取和存储数据的信息技术能力的不断提高，为运筹学社区提供了一个机会，可以在实践中提高生产计划解决方案的性能。

Logistics and transportation related operations typically account for large parts of total project costs and are linked to major pollution emission. Both private and public sectors call for more efficiency and reduction of transportation to relieve traffic and pollution emission. While mathematical optimization has been among the most successful tools to provide efficient planning solutions for these domains, models have been traditionally based on forecasts of the problems’ parameters. The ongoing increase of information technology capacity to capture and store data from different sources offer an opportunity to the Operations Research community to improve the performance of produced planning solutions in practice.