New mathematical programming approaches for assemble-to-order system optimization

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Assemble-To-Order (ATO) systems represent a large class of important manufacturing systems in the contemporary economy, especially in the high-tech sector. The optimization of ATO system operations involves difficult stochastic optimization problems. Due to this difficulty, there is a big gap between the industrial practice and the fast growing academic literature on this topic. In this research, we develop a conceptual innovation called "multi-matching" on characterizing the ATO systems. Starting from this innovation, we propose new stochastic programming models that can incorporate the critical stochastic elements in ATO systems, while embracing the computational power of mathematical programming. We aim at solving small- to medium-sized instances accurately, and providing good solutions with performance guarantee for large-scale systems in real life. The proposed approaches have several important implications in both academia and industry. Firstly, the stochastic programming models inject new insights into the ATO systems, and can provide a uniform benchmark for the practice in the industry and the existing heuristics in the literature. Secondly, the conceptual innovation and the corresponding mathematical programs can be applied to very general inventory systems. They can be used to study the impact of customer-differentiated service measures, supply risks, stochastic lead times and yield uncertainty. They can also be extended to multi-product and multi-echelon inventory systems. Thirdly, this research provides an important application of the state-of-the-art stochastic programming techniques, and brings new challenges and opportunities to both the modeling and computation of mathematical programming. The proposed research program will train Ph.D.'s with the expertise in inventory theory, stochastic programming / mathematical programming, and ERP software, and M.Sc.'s with the expertise in real life ATO systems, and ERP software.