Analysis of Manufacturing Supply Chains Using System Dynamics and Multi- Objective Optimization

Title: Analysis of manufacturing supply chains using system dynamics and multi-objective optimization I ABSTRACT Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. Hence, due to these multiple performance measures, supply chain decision making is much more complex than treating it as a single objective optimization problem. The aim of this work is thus to introduce a methodology to address supply chain management (SCM) problems within a truly Pareto-based multi-objective optimization (MOO) context and utilize knowledge extraction techniques to extract valuable and useful information from the Pareto-optimal solutions. By knowledge extraction , it means to detect hidden interrelationships between the Pareto solutions, identify their common properties and characteristics as well as to discover concealed structures in the Pareto-optimal data set in order to support managers in their decision making. This thesis proposes a new simulation-optimization framework, in which the simulation is based on system dynamics (SD) and the optimization utilizes multi-objective meta-heuristic search algorithms, such as the well-known NSGA-II. In order to connect the SD and MOO software, this thesis introduces a novel SD and MOO interface application which allows the modeling and optimization applications to interact. Additionally, this work also presents an iterative SD-MOO methodology that addresses the issue of the curse of dimensionality in MOO for higher dimensional problems, with the aim to execute SD-MOO in a computa-tionally cost-efficient way, in terms of convergence, solution intensification and accuracy of obtaining the Pareto-optimal front for complex SCM problems. In order to detect evident and hidden structures, characteristics and properties of the Pareto-optimal solutions, this work utilizes Parallel Coordinates, Clustering and Automated Innovization, which are three different types of tools for post-optimality analysis and facilitators of discovering and retrieving knowledge from the Pareto-optimal set. The developed SD-MOO interface and methodology are then verified and validated through two academic application studies and a real-world industrial application study. While not all the insights generated in these application studies can be generalized for other supply-chain systems, the analysis results provide strong indications that the methodology and techniques introduced in this thesis are capable to generate knowledge to support academic SCM research and real-world SCM decision making, which to our knowledge cannot be performed by other methods.