| Nowadays, highly integrated manufacturing has resulted in more and more large-scale industrial operations. As one of the most effective strategies to ensure high-level operations in modern industry, large-scale engineering optimization has garnered a great amount of interest from academic scholars and industrial practitioners.; Large-scale optimization problems frequently occur in industrial applications, and many of them naturally present special structure or can be transformed to taking special structure. Some decomposition and coordination methods have the potential to solve these problems at a reasonable speed. This thesis focuses on three classes of large-scale optimization problems: linear programming, quadratic programming, and mixed-integer programming problems. The main contributions include the design of structural complexity analysis for investigating scaling behavior and computational efficiency of decomposition strategies, novel coordination techniques and algorithms to improve the convergence behavior of decomposition and coordination methods, as well as the development of a decentralized optimization framework which embeds the decomposition strategies in a distributed computing environment. The complexity study can provide fundamental guidelines to practical applications of the decomposition and coordination methods.; In this thesis, several case studies imply the viability of the proposed decentralized optimization techniques for real industrial applications. A pulp mill benchmark problem is used to investigate the applicability of the LP/QP decentralized optimization strategies, while a truck allocation problem in the decision support of mining operations is used to study the MILP decentralized optimization strategies. |
