Multi-objective constrained optimization for decision making and optimization for system architectures

Abstract

This thesis proposes new methods to solve three problems: 1) how to model and solve decision-making problems, 2) how to translate between a graphical representation of systems and a matrix representation of systems, and 3) how to cluster single and multiple Design Structure Matrices (DSM). To solve the first problem, the thesis provides an approach to model decisionmaking problems as multi-objective Constraint Optimization Problems (COP) based on their common structures. A set of new algorithms to find Pareto front of multi objective COP is developed by generalizing upon the Conflict-directed A* (CDA*) algorithm for single-objective COPs. Two case studies - Apollo mission mode study and earth science decadal survey study - are provided to demonstrate the effectiveness of the modelling approach and the set of algorithms when they are applied to real world problems. For the second problem, the thesis first extends classical DSMs to incorporate different relations between components in a system. The Markov property of the extended DSM is then revealed. Furthermore, the thesis introduces the concept of "projection", which maps and condenses a system graph to a DSM based on the Markov property of DSM. For the last problem, an integer programming model is developed to encode the single DSM clustering problem. The thesis tests the effectiveness of the model by applying it to a part of a real-world jet engine design project. The model is further extended to solve the multiple DSM clustering problems.