Title: The Data Platform of the Future - Large Base Registries of the Netherlands
The Dutch cadastre has taken the lead by developing the geospatial data platform of the future and releasing it as PDOK (Public Services on the Map; www.pdok.nl). This platform offers a semi-automated transformation from geospatial data (such as WFS, GML) to Linked Data (RDF), and on top of that APIs, including a SPARQL endpoint, view, test & documentation environment. This platform is open for Dutch governments and has been used to make the first two official key registers of the Dutch governments as Linked Open Data & APIs, the first registers as so-called 5-star open data, the desired format for all open data. The platform is continuously improving itself offering new functionalities in upcoming releases including a quality dashboard, a feedback function and datasets to be continuously added. So far it is the biggest implementation of Linked Data in the Netherlands. At Kadaster/PDOK we offer more than 100 datasets (from different government organisations) as geospatial services. This talk will address the innovative new data platform for transforming and publishing these datasets as linked data. The datasets offered include several base registries such as the Building and Addresses data of the Netherlands (1.2 billion triples), with daily updates. Open sparql endpoints are available, and one of the latest addition is "Data Stories", the ability to create and store sparql queries and create a story with the data, and share it on a website. The results of the innovation activities, such as AI, machine learning, data visualisation, BI integration, are being published on data.labs.pdok.nl.
Bio: In 2001 Erwin joined TNO, and became a senior scientist on the topic of interoperability and standards. From 2009 he part-time joined the University of Twente to start a Ph.D. research on the standardization topic, while continuing his work for TNO. In 2012 received his Ph.D. based on the "Quality of Semantic Standards" thesis. In 2013-2014 Erwin was visiting researcher at ERCIS/University of Munster. From 2015 onwards Erwin joined Kadaster, to lead "data" related activities, and continued part-time working within the IEBIS department at the University of Twente.
Title: Dynamic clustering of maintenance activities with common and shared setups
Abstract: Maintenance of assets often requires setup activities. Setups cause unnecessary downtime of the assets, which are important for continuous delivery of goods and services. This leads to loss of revenue and an increase in costs. To reduce setups and increase uptime of assets, it is crucial to cluster (group) maintenance activities that require common or shared setups and execute them together. In this paper, we consider dynamic clustering of maintenance activities with common or shared setups to minimize maintenance and setup costs over a finite horizon. Different from the literature, we take into account a predetermined fixed schedule of the assets, their maintenance requirements, and the finite maintenance capacity. The dynamic aspect arises from changes in the operational schedule of the assets in short notice. We formulate a MILP model to determine the optimal dynamic maintenance schedule for both the single and multiple assets. The MILP model is tractable for small to medium problem instances. To solve the large-scale problem instances, we develop a column generation based heuristic and a MILP based heuristic. Using the case maintenance data of an airline operator, we test the performance of the heuristics against the MILP model. We find that our MILP based heuristic performs quite well in both solution quality and computational effort on solving practical problems.
Bio: Atıl Kurt received his BS and MS in Industrial Engineering from the Çankaya University and Ph.D. in Industrial Engineering from the Middle East Technical University. His Ph.D. Thesis was focused on the distribution of Blood Products. His research areas are humanitarian logistics, optimization, production planning and scheduling, and maintenance planning.