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dr. ir. Said Dabia

Associate Professor, School of Business and Economics, Operations Analytics

Personal information

Said Dabia is Associate Professor of Transport Optimization at the School of Business and Economics, department of Operations Analytics. He joined the department of Operations Analytics as an Assistant Professor in 2014. In 2012, He received his PhD in Operations Research from the School of Industrial Engineering at Eindhoven University of Technology (TU/e). He has a Master degree in Industrial Engineering and Bachelor degree in Mathematics and Physics. Prior to joining VU Amsterdam, He was postdoctoral researcher at TU/e from 2012 to 2014.

Research

His research focuses on modeling and solving real-life problems arising in transportation. In particular, He is interested in developing exact methods for variant transportation problems inspired from practice. He also has several years of experience as a consultant and algorithms expert. His research results have been successfully implemented and tested in several projects with industry, and published in leading academic journals: Transportation Science, European Journal of Operational Research, Computers and Operations Research and the International Journal of Production Economics.

Teaching

I am the course coordinator of the following subjects:

Business Processes
Logistics Analysis

Grants

Project: Sensing Logistics (SensLog), Amount: €600.000 (2018-2021)

Innovative sensors enable capturing large amounts of data about freight flows and are more and more applied in practice. To reap the benefits of sensory data, the project will particularly focus on three topics: to develop models and decision support tools for sensor placement and sensor repositioning strategies, generate predictive models for logistics operations; to improve logistics performance; and finally to develop suitable offline and online algorithms to reschedule/re-route logistics operations.

Project: Sensors for Sustainable Logistics, Amount: €100.000 (2021-2022)

Innovative sensors enable capturing large amounts of data about freight flows and are more and more applied in practice. The project particularly focuses on:

Use sensory data to better estimate temperature changes during transport operations
Incorporate insights from sensory data in deterministic models and algorithms for integrated sustainable decision-making on consolidating products into trucks, designing distribution routes and on operating the cooling equipment for fresh products.
Exploring sensory data to define data-driven stochastic/robust models to support dynamic decision making
Decide on the vehicle fleet mixt for sustainable and cost-efficient transport plans
Evaluate the benefits of the data-driven stochastic/robust decision models using real-life data

Ancillary activities