Multi-objective stochastic simulation optimization
Multiobjective optimization problems are common in reality; they do not only occur frequently in engineering, but also in business settings. In many real life situations, the multiple objectives cannot be determined analytically; they need to be observed through physical or computer experiments. The goal is to find or approximate a set of optimal solutions that reveal the essential trade-offs between the objectives (i.e., the Pareto front), where optimality means that no objective can be improved without deteriorating the quality of any other objective. This postdoctoral fellowship application focuses on multiobjective stochastic simulation optimization, and is situated at the interface of two research fields: operations research and machine learning. We highlight the current challenges in multi-objective stochastic simulation optimization, and propose a series of critical research milestones in view of providing a leap forward in the application of such methods in practice. More specifically, the project aims to include user preferences in the optimization, and to provide an indifference zone approach for the identification of the Paretooptimal solutions. Novel quality indicators need to be developed to assess the quality of stochastic Pareto fronts, and a publicly available test suite will be built to facilitate the performance comparison of different stochastic MO algorithms put forward by the research community.
Period of project
01 October 2020 - 30 September 2023