Venue: Industriens Hus, Copenhagen — May 6th, 2019
Registration: is now open
Program: (the program is still subject to changes)
|09:00-10:00||Registration and refreshments|
|10:05-10:50||Magrét Vilborg Bjarnadóttir, Robert H. School of Business Maryland, USA
Operations Research and People Analytics
People Analytics are a fast growing field; quantitative methods are becoming main stream in HR departments. There is a great opportunity for the Operation Research Community to play a significant role in how HR decisions are made in the 21st century. In this talk we will review the growing field of People Analytics and take a deep dive into how data driven decision making can support salary decisions, focusing on demographic pay gaps.
The gender pay gap (and other demographic pay gaps) are a topic of discussion in the boardroom, in the media and among policy makers, with multiple new legislation being passed across Europe: in Great Britain, France, and Iceland to name a few. While the methodology for determining pay discrimination is known and mostly agreed upon (a log-regression model), how to close a pay gap has remained an open questions. Who should get raises and how much? We apply optimization and descriptive analytics to address this knowledge gap. We first describe a cost optimal approach based on statistics and optimization that can meet the “equal pay for equal work” standard for less than half the cost of the naive method of increasing all female workers’ wages equally. In order to balance cost efficiency with fairness we discuss other fairness driven algorithmic approaches that address and close the gender pay gap. These approaches while more expensive than the cost optimal approach can still save significant costs compared to the naïve approach. We further explore the impacts of closing the gap based solely on cost efficiency, which in some cases are surprising, for example we can show that there may exists men within a firm who if they receive salary increases will reduce the gender pay gap. These men strongly typify male employees in terms of traits. We demonstrate the above algorithmic approaches, savings and costs, using real data from our developing partners.
|10:55-11:40||Fernando Alvarez, Bunker Metric, Oslo Norway
Optimization of marine fuels procurement
Marine fuel typically constitutes close to half of a vessel operator’s running expenses. The procurement of this fuel is a fairly complex process: price and availability of fuels vary on a daily basis and significantly by location; quality problems are widespread, and can cause major damage to an engine; there are multiple restrictions related to tank sizes, fuel margins, fuel mixing, and emissions control areas; and finally, the future fuel requirements of the vessel are not known precisely, as the commercial schedule of the vessel may only be known a few days or weeks in advance.
BunkerPlanner is an optimization and data analytics platform that assists operators in the procurement of marine fuels. Based on the characteristics of the vessel (tank capacities, fuel consumption profile, among others), the latest fuel price information, and the known vessel schedule, we determine the optimal locations and quantities for fuel purchases. In addition, we use predictive analytics to estimate fuel consumption and needs beyond the currently known schedule. Based on ongoing pilots with several European vessel operators, our system can generate savings of 1-5% in fuel expenditures.
|11:45-13:00||Lunch, provided by Industriens Hus|
|13:00-13:45||Dolores Romero Morales, Professor, Copenhagen Business School
Data Science aims to develop models that extract knowledge from complex data and represent it to aid Data Driven Decision Making. Mathematical Optimization has played a crucial role across the three main pillars of Data Science, namely Supervised Learning, Unsupervised Learning and Information Visualization. In this lecture, we will navigate through recent Mathematical-Optimization based models in Data Science that illustrate the important role of Mixed-Integer NonLinear Programming in enhancing the interpretability of the tools, while preserving their good learning performance.
|13:50-14:35||Discussion Workshop on OR Questions|
|14:40-15:10||Coffee & networking|
|15:10-15:55||Torben Barth, Fraport, (Frankfurt Airport)
Applied analytics at Frankfurt Airport
This talk illustrates the different analytical approaches at Frankfurt Airport. The presentation shows typical examples of applied analytics at Frankfurt Airport. It sheds a light on the challenges in the application of optimization and data science solution in a real world environment. The differences and the dependencies of optimization and data science in the daily work at Frankfurt Airport will be shown. At one example of daily operations the key aspects of analytics in practice e.g. dealing with uncertainty will be demonstrated.