 Logic-Based Benders Decomposition for Large-Scale OptimizationJohn N. Hooker ()
A chapter in Large Scale Optimization in Supply Chains and Smart Manufacturing, 2019, pp 1-26 from Springer Abstract: Abstract Logic-based Benders decomposition (LBBD) is a substantial generalization of classical Benders decomposition that, in principle, allows the subproblem to be any optimization problem rather than specifically a linear or nonlinear programming problem. It is amenable to a wide variety of large-scale problems that decouple or otherwise simplify when certain decision variables are fixed. This chapter presents the basic theory of LBBD and explains how classical Benders decomposition is a special case. It also describes branch and check, a variant of LBBD that solves the master problem only once. It illustrates in detail how Benders cuts and subproblem relaxations can be developed for some planning and scheduling problems. It then describes the role of LBBD in three large-scale case studies. The chapter concludes with an extensive survey of the LBBD literature, organized by problem domain, to allow the reader to explore how Benders cuts have been developed for a wide range of applications. Date: 2019 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-22788-3_1 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-22788-3_1 Access Statistics for this chapter More chapters in Springer Optimization and Its Applications from Springer |
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