Doctoral Dissertation

15.10.2018 M.Sc. Yue Zhou-Kangas (Facul­ty of In­for­ma­tion Tech­no­lo­gy, Mathematical Information Technology)


15.10.2018 12:00 — 15:00

Location: Mattilanniemi , Agora, Lea Pulkkisen sali
Release: 15.10.2018 How to make good decisions for unknown future? (Zhou-Kangas)
M.Sc. Yue Zhou-Kangas' defends her doctoral dissertation in Mathematical Information Technology "Interactive Methods for Multiobjective Robust Optimization". Opponent Professor Margaret Wiecek (Clemson University, USA) and Custos Professor Kaisa Miettinen (University of Jyväskylä). The doctoral dissertation is held in English.

Yue Zhou-Kangas transforms the unknown into useful insights for decision making. The insights support decision makers in making good decisions for now and unknown future.

In modern society, people have to make decisions with respect to multiple aspects. In many cases, these decisions are made without knowing exactly how the future looks like. For example, investors have to make investment decisions by considering return on investments and different risk factors. The investment decisions are made without the exactly information on the future stock market. In this kind of situation, we say that decisions are made under uncertainty. 

Even in our daily life, we have to make decisions with respect to multiple aspects and under uncertainty. For example, a car buyer (i.e., the decision maker) wants to buy a comfortable car at a reasonable price. In addition, (s)he also would like to buy an environment-friendly car because (s)he can protect our environment and enjoy low taxes. However, on the one hand, usually, more comfortable and environment-friendly cars are more expensive. In this case, the car buyer needs support to understand the best compromises. Based on the understanding, (s)he can find a best compromise based on her or his preferences and then choose a car to buy. On the other hand, (s)he does not know how the tax policies will change in the future. So, (s)he needs further support. (S)he needs to understand how the possible future (e.g., the tax can increase) affects on the best compromises.

The doctoral dissertation transforms the unknown future into different types of insights. With the insights, decision makers are supported to first grasp a balance on multiple aspects.  And then,  they are supported to understand the effects of the unknown future. Further more, decision makers can also be supported to find a balance between the now and future.  By using the methods developed, the car buyer can be supported to understand the car with respect to the three considered aspects. At the same time, (S)he is also supported to know the effects of possible future policy changes.  As a result, the car buyer can buy a car which is a best compromise for now. Further more, (s)he can have a car with acceptable tax no matter how the policies change in the future.

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M.Sc. Yue Zhou-Kangas will defend her doctoral dissertation ‘Interactive Methods for Multiobjective Robust Optimization’ in in building Agora, Lea Pulkkinen hall, on October 15 2018 at 12 o’clock noon. Opponent Professor Margaret Wiecek (Clemson University) and Custos Professor Kaisa Miettinen (University of Jyväskylä). The doctoral dissertation is held in English.

Yue Zhou-Kangas has graduated with a master of science degree in mathematical information technology in Faculty of Information Technology at University of Jyväskylä. Since 2015, she has been working as a doctoral student in the Industrial Optimization Group. Her research interests include multiobjective optimization under uncertainty, decision support, nature-inspired methods, data-driven optimization, prescriptive analytics.

Where the dissertation is published: JYU Dissertations number 16, Jyväskylä 2018, ISSN 2489-9003, ISBN 978-951-39- 7549-4 (PDF) http://urn.fi/URN:ISBN:978-951-39-7549-4 (PDF).