dr. L.P. (Luuk) Veelenturf

Rotterdam School of Management (RSM)
Erasmus University Rotterdam
Member ERIM
Field: Logistics & Information Systems
Affiliated since 2007

Luuk Veelenturf is an Associate Professor at the Department of Technology and Operations Management. His research interests lie mainly in the area of real-time transport and mobility operations, with a specific focus on public transport planning, railway disruption management, city logistics, vehicle routing & pickup and delivery variants and data-driven transport operations. A large part of Luuk’s research focuses on sustainable transportation and algorithms to support logistics-related decision making. This is increasingly important, as delivery times and volumes increase. As it is impossible to wait until all information has been collected before decisions are made, smart analytics will become more important.  By improving systems and schedules, Luuk aims to make public transport a more attractive and sustainable alternative to car transportation and make city logistics more efficient, thereby reducing CO2 emissions and congestion.

  • L.P. Veelenturf (2014, november 21). Disruption Management in Passenger Railways. Erasmus University Rotterdam (128 pag.) (Rotterdam: Erasmus Research Institute of Management (PhD Serie 327)) Prom./coprom.: prof.dr. L.G. Kroon & Dr. G. Maroti.

In many delivery and service operations, the service provider and customer must agree on a day or time window for the service. The time window appointments have direct impact on the efficiency and effectiveness of the service operations, but also on the attractiveness of the service to the customer. In this research, we will study how to increase sustainability and profitability of logistics services by managing and steering the demand. To find the right balance between, sustainability, effectiveness and attractiveness of the service, we consider two types of demand management approaches: (i) selectively determining the time window assortment to be displayed to customers (e.g. dependent on their region) and (ii) providing incentives (financial and non-financial) to dynamically steer demand. One example of a non-financial incentive is the use of green labels that show which logistics choices help the service provider reducing vehicles miles and thereby emissions and pollution.  We will conduct empirical research based on both lab and field experiments and develop optimization methods/machine learning approaches to support decision making in this context.

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