Learning from Multi-Objective Optimization of Production Systems A method for analyzing solution sets from multi-objective optimization
Abstract: The process of multi-objective optimization involves finding optimal solutions to several objective functions. However, these are typically in conflict with each other in many real-world problems, such as production system design. Advanced post-optimization analysis can be used to provide the decision maker with information about the underlying system. The analysis can be based on the combination of simulation-based multi-objective optimization and learning from the obtained solution set. The goal of the analysis is to gain a deeper understanding of the problem at hand, to systematically explore and evaluate different alternatives, and to generate essential information and knowledge to support the decision maker to make more informed decisions in order to optimize the performance of the production system as a whole.The aim of this work is to explore the possibilities on how post-optimization analysis can be used in order to provide the decision maker with essential information about an underlying system and in what way this information can be presented. The analysis is mainly done on production system development problems, but may also be transferred to other application areas.The research process of the thesis has been iterative, and the initial approach for post-optimization analysis has been refined several times. The distance-based approach developed in the thesis is used to allow the extraction of information about the characteristics close to a user-defined reference point. The extracted rules are presented to the decision maker both visually, by mapping the rules to the objective space, and textually. The method has been applied to several industrial cases for proof-by-demonstration as well as to an artificial case with information known beforehand to verify the distance-based approach, and the extracted rules have also been used to limit the search space in the optimization. The major finding in the thesis is that to learn from optimization solution sets of production system problems with stochastic behavior, a distance-based approach is advantageous compared with a binary classification of optimal vs. non-optimal solutions.
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