Multi-Stage Simultaneous Lot-Sizing and Scheduling : Planning of Flow Lines with Shifting Bottlenecks
By: Seeanner, Florian.Material type: TextSeries: eBooks on Demand.Produktion Und Logistik: Publisher: Dordrecht : Springer, 2013Description: 1 online resource (200 p.).ISBN: 9783658020897.Subject(s): Production control | Production management | Production scheduling -- Mathematical modelsGenre/Form: Electronic books.Additional physical formats: Print version:: Multi-Stage Simultaneous Lot-Sizing and Scheduling : Planning of Flow Lines with Shifting BottlenecksDDC classification: 658.53 Online resources: Click here to view this ebook.
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Foreword; Acknowledgments; Contents; List of Figures; List of Tables; List of Algorithms; List of Abbreviations; Chapter 1 Introduction; 1.1 Cost pressure in the consumer packaged goods industry; 1.2 Motivation and goals of this thesis; 1.3 Outline; Chapter 2 Production planning in the consumer packaged goods industry; 2.1 Supply Chain Planning; 2.2 Supply chain typology; 2.2.1 Functional attributes; 2.2.2 Structural attributes; 2.3 The supply chain of the consumer packaged goods industry; 2.3.1 Topography; 2.3.2 Integration and coordination; 2.3.3 Functional attributes of the manufacturer
2.4 Lot-sizing and scheduling in the consumer packaged goods industry2.5 Advanced Planning Systems; Chapter 3 Multi-level lot-sizing and scheduling; 3.1 General assumptions and limitations; 3.1.1 Objective; 3.1.2 Time structure; 3.2 Single-level models with different time structures; 3.2.1 The Capacitated Lot-sizing Problem (CLSP); 3.2.2 The Discrete Lot-sizing and Scheduling Problem (DLSP); 3.2.3 The Continuous Setup Lot-sizing Problem (CSLP); 3.2.4 The Proportional Lot-sizing and Scheduling Problem (PLSP); 3.2.5 The Capacitated Lot-sizing problem with Sequence-Dependent setup costs (CLSD)
3.2.6 The General Lot-sizing and Scheduling Problem (GLSP)3.3 Multi-level models in the literature; 3.3.1 Synchronization between the levels; 3.3.2 PLSP-based models; 3.3.3 CLSD-based models; 3.3.4 GLSP-based models; 3.3.5 Other models; 3.4 Conclusions; Chapter 4 Improvements of the GLSPMS; 4.1 Shortcomings of the basic GLSPMS; 4.2 Improved GLSPMS; 4.2.1 New line synchronization; 4.2.2 Model formulation; 4.3 Properties of the improved GLSPMS; 4.3.1 Complexity; 4.3.2 Advantages and disadvantages; 4.3.3 Generalization of other models; 4.4 Computational tests; 4.4.1 Base scenarios
4.4.2 Results with different settings of a standard MIP-solverChapter 5 Reformulations of the improved GLSPMS; 5.1 General reformulation techniques; 5.2 Selected approaches for lot-sizing and scheduling models; 5.2.1 Extended reformulations; 5.2.2 Valid inequalities; 5.3 Extended reformulations of the GLSPMS; 5.3.1 SPL formulation (S); 5.3.2 Flow formulation (F); 5.4 Valid Inequalities for the GLSPMS; 5.4.1 Stock inequalities (K); 5.4.2 Tighter setup forcing constraints (M); 5.5 Computational results; 5.5.1 Comparison of the different model formulations
5.5.2 Influence of varying problem characteristicsChapter 6 Heuristics for the improved GLSPMS; 6.1 Selected meta-heuristic approaches; 6.1.1 Truncated MIP; 6.1.2 LP&Fix; 6.1.3 Relax&Fix; 6.1.4 Exchange; 6.1.5 Local Search; 6.1.6 Variable Neighborhood Search (VNS); 6.1.7 Variable Neighborhood Decomposition Search (VNDS); 6.2 Heuristics for the GLSPMS; 6.2.1 Truncated MIP (TM); 6.2.2 LP&Fix (LF); 6.2.3 Relax&Fix (RF); 6.2.4 Trivial solution (TS); 6.2.5 Repeating setup sequence (RS); 6.2.6 GLSPMS tailored Fix&Optimize (FO); 6.2.7 Variable Neighborhood Decomposition Search with Exchange (VNDS+E)
6.3 Computational tests
¿Due to a varying product demand (changing product mix) and different production speeds, bottlenecks may shift between the stages. In that case, a simultaneous lot-sizing and scheduling of these stages is recommendable. Hence, an improved version of the General Lot-Sizing and Scheduling Problem for Multiple production Stages (GLSPMS) was developed. Moreover, several reformulation techniques were applied to this model to solve it exactly. Besides, a new meta-heuristic which combines the principles of Variable Neighborhood Decomposition Search (VNDS) and Exchange was implemented to find good sol
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