MISTA 2017: Plenary Speakers

We are delighted to present the line up of our plenary speakers for the MISTA 2017 conference, which takes place in Kuala Lumpur (5- 8 Dec 2017). We hope that you are able to join us, as we believe that the plenary talks, along with the rest of the program that we are putting together, will make for an exciting conference.


Jacek Błaźewicz, Institute of Computing Science, Poznan University of Technology

(Web Site)

Multi-agent based approach for the origins of life hypothesis

Multi- agent systems have been used extensively in scheduling, but the methodology has many other applications. One of those appears to be the analysis of the origins of life hypothesis. One of the most recognized hypotheses for the origins of life is the RNA world hypothesis. Laboratory experiments have been conducted to prove some assumptions of that hypothesis. However, despite some successes in the "wet-lab" experiments, we are still far from a complete explanation. Bioinformatics, supported by operations research and in particular by multi-agent approach, appears to provide perfect tools to model and test various scenarios of the origins of life where wet-lab experiments cannot reflect the true complexity of the problem. This paper illustrates some recent advancements in that area and points out possible directions for further research.


Ender Özcan, University of Nottingham

Dr Ender Özcan is a member of the Automated Scheduling, Optimisation and Planning (ASAP) research group in the School of Computer at UoN. He received his PhD from the Department of Computer and Information Science at Syracuse University in 1998. Then, he worked as a lecturer in the Department of Computer Engineering at Yeditepe University from 1998-2007. He established and led the ARTIficial Intelligence research group from 2002. He served as Deputy Head of the Computer Engineering Department from 2004-2007. Dr Özcan was appointed as a senior research fellow in 2008 to the EPSRC funded LANCS initiative, which is one of the largest Science and Innovation Rewards given by EPSRC in the field of Operational Research across the world. He became a lecturer in 2009, served as an executive committee member for the LANCS initiative. His research interests and activities lie at the interface of Computer Science, Artificial Intelligence and Operational Research, with a focus on intelligent decision support systems embedding data science techniques and (hyper-/meta)heuristics applied to real world problems, particularly in scheduling and timetabling. He has been awarded grants as principal investigator, co-investigator or named researcher from various funding bodies, including TUBITAK, DPT (Turkey), TSB, The Royal Society, EPSRC (UK) and CONACyT (Mexico). He is co-chair of the newly established EURO working group of ‘Data Science meets Optimisation’. He is Steering Committee member and Executive Officer of the International Conference Series on the Practice and Theory of Automated Timetabling (PATAT). He is Associate Editor of the Journal of Scheduling, International Journal of Applied Metaheuristic Computing and IEEE Transactions on Emerging Topics in Computational Intelligence.

(Web Site)

A Review of Selection Hyper-heuristics: Recent Advances

Hyper-heuristics emerged as general purpose optimisation methodologies that search the space of heuristics, rather than candidate solutions directly, for solving computationally difficult problems. The current state-of-the-art in hyper-heuristic development involves designing adaptive search methods that are applicable to instances with different characteristics not only from a single problem domain, but also across multiple domains. A key goal is enabling ‘plug-and-play’ search components, including data science techniques (e.g., machine learning and statistics) to be applied to optimisation without them having to be re-implemented for every problem domain. Selection hyper-heuristics separate the high level automated search control embedding learning heuristic selection and move acceptance methods from the low level problem domain details. In the last two decades, particularly after the cross-domain heuristic search challenge in 2011, there has been an extremely rapid growth in this area of research, leading to many highly-effective selection hyper-heuristics applied to various problem domains. As a means of achieving generality, the initially proposed interface between the selection hyper-heuristic and domain layers was extremely restrictive allowing no problem specific information flow. However, there is a current trend towards moving away from this type of interface to facilitate more expressive selection hyper-heuristics capable of operating in an information rich environment, whilst still maintaining domain independence of the search control. This talk provides a review of selection hyper-heuristics focusing on the recent advances in the field.


Hoong Chuin Lau, Singapore Management University

Hoong Chuin LAU is Professor of Information Systems and Director of the Fujitsu-SMU Urban Computing and Engineering Corp Lab at the Singapore Management University. The common thread running through his research is a focus on going beyond publications to build usable novel tools and prototypes, a number of which have been testbedded and deployed in industry. His research in the interface of Artificial Intelligence and Operations Research has contributed to advances of algorithms in a variety of complex optimization problems in logistics, transportation, travel planning, safety and security. He currently serves on the editorial board of the IEEE Transactions on Automation Science and Engineering.

(Web Site)

Combining Machine Learning and Optimization for Real-World Scheduling Applications

In this Big Data era, data can and should be exploited for more effective resource scheduling. In this talk, I will discuss a framework that combines data analytics, machine learning and optimization to solve real-world complex scheduling problems effectively. I will illustrate with three diverse scheduling problems ranging from crowd logistics to police officer scheduling to maritime traffic coordination, showing how spatial-temporal patterns can be learnt from data (both historical and real-time), and utilized to generate effective schedules.