DDQC 2022 – Optimization of Service Systems
From 21-23 September 2021, we held the inaugural multicontinent workshop on Data Driven Queueing Systems. The program, comprising 18 talks over 2 days across multiple timezones, attracted 300 registrants from 25 countries. The second multicontinent workshop will be held in April 2022 on the subject of Optimization of Service Systems.
Spatio-temporal data are playing an increasingly important role in domains such as urban resource planning and healthcare. The objective of this workshop is to highlight and discuss contemporary methodological approaches to the design of service systems, with a focus on approaches to optimization in a stochastic context.
The workshop is designed to appeal to researchers with backgrounds in statistics, stochastic modelling, data science and control to discuss contemporary challenges in operations research and management. Workshop sessions will span international time zones to be accessible to multiple audiences.
About DDQC 2021
The increasing availability of empirical data in the operation of large computer networks and in the management of human service systems is creating new opportunities for study in queueing theory. This workshop will bring together researchers with backgrounds in statistics, stochastic modelling, data science and control to discuss contemporary queueing theory challenges. The objective is to highlight and discuss future directions in data-driven queueing that arise in modelling, monitoring and controlling queues, and in dealing with parameter uncertainty, when there is access to operational data. The workshop sessions span international time zones so that parts of the workshop will be scheduled at times convenient to multiple audiences.
NETWORKS is a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research.
NETWORKS is a consortium of 50 researchers from 4 institutions. The programme started in the Summer of 2014 and covers a broad range of topics dealing with stochastic and algorithmic aspects of networks. The aim of the programme is to address the pressing challenges posed by large-scale networks. The focus is on modelling, understanding, controlling and optimizing networks that are complex and highly volatile.
NETWORKS is hosted by four research institutions: University of Amsterdam (UvA), Eindhoven University of Technology (TU/e), Leiden University (UL) and the Center for Mathematics and Computer Science (CWI)
Australian Research Council Centre of Excellence For Mathematical and Statistical Frontiers
The Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) brings together a critical mass of Australia’s best researchers in applied mathematics, statistics, mathematical physics and machine learning. With partner researchers from the University of Melbourne, University of Adelaide, University of Queensland, Queensland University of Technology, UNSW Sydney, University of Technology Sydney, and Monash University – ACEMS engages in research programs that combine innovative methods for the analysis of data with theoretical, methodological and computational foundations, provided by advanced mathematical and statistical modelling. ACEMS focus on the impact of new insights for end users working in the Collaborative Domains of Healthy People, Sustainable Environments and Prosperous Societies. ACEMS creates world-class research at the frontiers of the mathematical sciences dealing with probability and randomness, and to translate this research into new insights that benefit society.
The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence.
The Institute is named in honour of Alan Turing, whose pioneering work in theoretical and applied mathematics, engineering and computing is considered to have laid the foundations for modern-day data science and artificial intelligence. The Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, driving economic impact and societal good, lead the training of a new generation of scientists, and shape the public conversation around data and algorithms.