Date of Original Version
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version is available at http://dx.doi.org/10.1016/j.ces.2011.03.017
Abstract or Description
This paper addresses the solution of simultaneous scheduling and planning problems in a production–distribution network of continuous multiproduct plants that involves different temporal and spatial scales. Production planning results in medium and long-term decisions, whereas production scheduling determines the timing and sequence of operations in the short-term. The production–distribution network is made up of several production sites distributing to different markets. The planning and scheduling model has to include spatial scales that go from a single production unit within a site, to a geographically distributed network. We propose to use two decomposition methods to solve this type of problems. One method corresponds to the extension of the bi-level decomposition of Erdirik-Dogan and Grossmann (2008) to multi-site, multi-market networks. A second method is a novel hybrid decomposition method that combines bi-level and spatial Lagrangean decomposition methods. We present four case studies to study the performance of the full space planning and scheduling model, the bi-level decomposition, and the bi-level Lagrangean method in profit maximization problems. Numerical results indicate that in large-scale problems, decomposition methods outperform the full space solution and that as problem size increases the hybrid decomposition method becomes faster than the bi-level decomposition alone.
Chemical Engineering Science, 66, 19, 4307-4318.