Applicants should have completed (or be close to completion of) a Master's degree in mathematics, operations management, operations research, econometrics, industrial engineering, or a closely related discipline, with a solid background in mathematical methods. Fluency in English is required.
Demand uncertainty is one of the main challenges in supply chains. Optimizing inventory to maximize profit without knowing the demand, has never been an easy task for the decision makers in practice. When the demand is highly uncertain without knowing the distribution, the inventory decisions will be even more complex.
To tackle this problem, we will conduct a comprehensive study of the data-driven robust optimization (DDRO) approach, theoretically, empirically, and behaviourally. In theory, we will develop a new DDRO approach. In collaboration with companies, we will analyse the empirical data carrying out the new approach. In the behavioural perspective, we will validate and evaluate the DDRO solution and other AI solutions (e.g. obtained from reinforcement learning) to support human decision makers.
We expect you to:
- develop the project proposal based on academic literature;
- build mathematical models in the DDRP approach, and compare DDRO approach and reinforcement learning approach;
- Apply new approach in empirical data
- Design lab/online experiments;
- Analyse behavioural data obtained from experiment;
- communicate the practical implications of this research to specific stakeholders and the general public;
- and present the findings at conferences and publish papers in internationally renowned journals.
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