1997 | OriginalPaper | Buchkapitel
Lower Bounds
verfasst von : Dr. Alf Kimms
Erschienen in: Multi-Level Lot Sizing and Scheduling
Verlag: Physica-Verlag HD
Enthalten in: Professional Book Archive
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Where standard MIP-solvers fail to compute optimum objective function values for PLSP-instances, lower bounds may be used as a point of reference for evaluation purposes. In this chapter, we compute lower bounds for the PLSP-MM. Solving the LP—relaxation of a PLSP—MM—model optimally is a straightforward idea. Section 5.1 deals with this approach and discusses a network reformulation of the model. Another way to get lower bounds is to ignore some of the constraints and to solve the remaining problem optimally. This path is followed in Section 5.2 where a B&B- procedure is used to attack the uncapacitated, multi—level, multi—machine lot sizing and scheduling problem.1 On the basis of this. Section 5.3 introduces a method to solve a Lagrangean relaxation of the capacity constraints. Finally, Section 5.4 summarizes the lower bounds obtained.