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

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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.