Wednesday 09.00 - 10.30, HFB|Audimax
Green Logistics: Decision Analytics for Sustainable Transportation
The environmental effects of transporting goods are of increasing concern to managers and policy makers. The use of fossil fuels, such as petrol and diesel oil, in transport produces air pollutants that can have a toxic effect on people and the environment. However, one of the main drivers for the concern over the environmental effects of freight transport has been the potential effects of the production of greenhouse gas (GHG) emissions on climate change from the use of carbon-based fuels.
Research into models for transportation and logistics has been active for many years. Much of the modelling has been with the aim of optimising economic objectives or improving measures of customer service. However, in recent years, more research has been undertaken where environmental objectives have also been considered, so that supply chain and other logistic services can be delivered in a more sustainable way.
For the OR analyst, there are many choices to be made about how to model freight transport operations. Can old models be revised with a simple change of objective or are more radical changes needed? We shall examine some of these choices and illustrate the issues with cases studies to show what contribution can be made to environmental and other objectives through the use of decision analytic models. This will include issues raised by the use of new technologies, such as the use of electric or other alternatively powered vehicles.
Richard Eglese is a Professor of Operational Research in the Department of Management Science at Lancaster University Management School.
He was President of the Operational Research Society in the UK in 2010-2011 and is currently a member of its General Council and Chair of its Publications Committee. He is also now President of EURO (The Association of European Operational Research Societies) until the end of 2018.
His research interests include combinatorial optimisation using mathematical programming and heuristic methods. He is concerned with applications to vehicle routing problems, particularly models for time-dependent problems and for problems in Green Logistics where environmental considerations are taken into account to provide more sustainable distribution plans.
Friday 13.45 - 15:00, HFB|Audimax
On big data, optimization and learning
In this talk I review a couple of applications on Big Data that I personally like and I try to explain my point of view as a Mathematical Optimizer – especially concerned with discrete (integer) decisions – on the subject. I advocate a tight integration of Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science.
For such an integration I try to answer three questions:
- What can optimization do for machine learning?
- That can machine learning do for optimization?
- Which new applications can be solved by the combination of machine learning and optimization?
Andrea Lodi received the PhD in System Engineering from the University of Bologna in 2000 and he has been Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005–2006. He has been full professor of Operations Research at DEI, University of Bologna between 2007 and 2015. Since 2015 is Canada excellence Research Chair in “Data Science for Real-time Decision Making” at the École Polytechnique de Montréal.
His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science and his work has received several recognitions including the IBM and Google faculty awards. He is author of more than 80 publications in the top journals of the field of Mathematical Optimization.
He serves as Editor for several prestigious journals in the area. He has been network coordinator and principal investigator of two large EU projects/networks, and, since 2006, consultant of the IBM CPLEX research and development team. Finally, Andrea Lodi is the co-principal investigator (together with Yoshua Bengio) of the project "Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution", recently generously funded by the Canadian Federal Government under the Apogée Programme.