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Multi-Stage Simultaneous Lot-Sizing and Scheduling : Planning of Flow Lines with Shifting Bottlenecks

By: Seeanner, Florian.
Material type: TextTextSeries: 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.
Contents:
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
Summary: ¿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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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

Description based upon print version of record.

Author notes provided by Syndetics

<p>Dr. Florian Seeanner wrote his dissertation under Prof. Dr. Herbert Meyr's supervision at the Chair of Production and Supply Chain Management at the Technische .Universität Darmstadt</p>

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