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dr. ir. P. (Paul) Buijs

dr. ir. P. Buijs
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Towards virtual ports in a Physical Internet

Academic partners: Delft University of Technology
Main industry partners: Port of Rotterdam; Groningen Seaports
Duration: Jan 2016 – Dec 2020
(Co-)funding: 85% by NWO through the program “Vitale logistiek”; 15% by the industry partners
My role: Manager external relations and post-doc researcher
Short description: The project revolves around the role of ports in the Physical Internet initiative. Using sending e-mails through the digital web as analogy, the Physical Internet (PI) is based on universally interconnected, open networks of logistic services, in which physical goods are moved around, stored, delivered and used. For port and hinterland logistics, PI can be seen as an expansion of the current synchro-modal approach. In PI, decisions about the route, choice of transport mode and transshipment location of sea containers and their contents are made for individual containers and in real-time. In addition to research conceptualizing the role of ports in PI, the project also involves developing decision-supporting systems for logistic operations in ports and their hinterland. This is the first Dutch research project studying PI, and is widely considered to be the world’s first project to focus primarily on the role of ports within PI networks.

SLIFF: Sustainable Logistics in Fresh Foods

Academic partners: Rotterdam School of Management
Main industry partner: Albert Heijn
Duration: Feb 2015 – Jan 2019
(Co-)funding: 85% by NWO through the program “Duurzame logistiek”; 15% by the industry partner
My role: Project manager, post-doc researcher, daily supervisor of PhD candidate Roel Post
Short description: The central objective of SLIFF is to enhance the sustainability of fresh food logistics by improving logistics resource utilization while reducing distribution lead times. The project addresses two prevalent challenges in this context. Firstly, the distribution network planning and local DC planning have been largely addressed separately in science and practice. However, inventory reduction policies have resulted in a just-in-time flow of products with little or no storage buffers throughout the distribution network. This requires a synchronization of network level and local DC logistics planning. SLIFF aims to propose (distributed) decision-structures and develop optimization models to facilitate such synchronization. Secondly, SLIFF aims for joint optimization of transportation, inventory, and material handling resources. These resources are generally owned by many independent parties. Therefore, this requires research not only in the domain of optimization, but also in the domains of organizational design and IT interoperability.

Design of LNG Networks

Academic partners: TU Eindhoven
Main industry partners: Gasunie, Groningen Seaports, ENGIE, Energy Valley, Husa logistics, Feederlines and many others.
Duration: Sept 2013 – Dec 2016
(Co-)funding: 50% by Dinalog, 25% by academic partners and 25% by industry partners.
My role: Manager external relations, post-doc researcher, daily supervisor of PhD candidate Jose Lopez
Short description: The underlying premise of the project is that, for liquefied natural or bio-gas (LNG) to become a truly sustainable fuel for transportation, the distribution network through which LNG is made available to its end-users should be designed and operated sustainably. One of the main objectives is to derive possible designs for a multi-modal LNG distribution network. Moreover, the aim is to gain insights in the network requirements in different phases in the roll-out of LNG as transport fuel and to study the impact of infrastructural development alternatives by means of simulation models. Lastly, decision support tools are designed for efficient transportation of LNG and replenishment of LNG facilities.

LNG PILOTS

Main partners: Energy Valley (lead partner), Hogeschool van Arnhem en Nijmegen, Rotec Engineering, BTO Management, Energie Agentur NRW, Rolande LNG, PitPoint, POC Transport, CG Holthausen, Low8 and many others.
Duration: Jan 2016 – Jun 2019
(Co-)funding: 50% by INTERREG V A Deutschland-Nederland, 50% by partners
My role: Post-doc researcher
Short description: The introduction of LNG as a fuel for transportation and energy source for off-grid industry results in several new research and development questions. This project addresses the development of the small-scale LNG infrastructure in Dutch-German border region. It aims to identify locations for LNG fuel and bunker stations, while taking into consideration the design of the cross-border distribution network for supplying those facilities. Also technological advancements, such as robotized refueling and lightweight LNG storage tanks, and innovation for LNG production from renewable sources  play an important role in this project.

ADVANCE: Advanced predictive-analysis-based decision-support engine for logistics

Academic partners: SZTAKI, Hungary (project leader); Aston University, United Kingdom; TTS, Italy
Main industry partner: Palletways ltd., United Kingdom
Duration: Oct 2010 – Sept 2013
(Co-)funding: EU FP7
My role: PhD candidate
Short description: Transportation and distribution networks typically accumulate huge amounts of new data (transportation orders, customer details, loading lists, GPS tracking of vehicles, etc.), generated every minute of every day by thousands of shipments traveling on hundreds of trailers for many customers scattered across large geographical areas, each with multiple different service requirements. The project ADVANCE supports collaborating logistics service providers in improving their information collection and processing infrastructure, enabling strategic planning coupled with instant decision making. Patterns and dependencies existing in the vast data sets of these collaborative transportation networks can only be meaningfully processed by intelligent data-mining approaches closely linked to decision making procedures. The research consortium developed, tested, and documented innovative solutions for analysis of very large data sets in real time.

Laatst gewijzigd:06 oktober 2016 12:34
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