Prompted by advances in computer technology and the increasing confidence of decision makers in large-scale market models, practitioners of operations research are now tackling problems of increasing detail, complexity and size. This necessitates the development of new solution algorithms that exploit problem structure as well as the properties of the target hardware, in order to minimize turnaround time and maximize model utilization. Many models in planning and scheduling exhibit a block-angular structure, that can represent spatial or temporal partial decomposability: decision variables can be broken down to largely independent blocks, that correspond to first-level decisions satisfying a subset of the constraints, which may represent a time period, or a geographical region, or a commodity. The blocks interact via coupling constraints related to second-level coordination of block decisions, such as shared resource allocation restrictions. In this thesis we construct three efficient decomposition algorithms for such block-angular problems. These algorithms belong to the family of alternating directions methods, and can be thought of as block Gauss-Seidel iterative schemes for an augmented Lagrangian, that exploit the block structure.
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