Das schonste GlUck des denkenden M ens chen ist, das Erforschliche erforscht zu haben und das Unerforschliche ruhig zu verehren JOHANN WOLFGANG VON Gm~THE In the sense of Goethe, I hope this book helps to shift the fine line between the inaccessible and the explorable a little bit in the direction of the latter. It has been accepted as a doctoral dissertation at the Faculty of Business Admin istration and Economics of the Catholic University of Eichstatt-Ingolstadt, Germany, and has been a great pleasure to write~at least most of the time. At the other times, the following people helped me to get around the moun tains that seemed too high to climb: First of all, these were my parents, whom I thank for their continuing support. This 'thank you' is extended to my brothers Marcel and Dominik and of course to Angelika, who has strengthened me with love through the sometimes arduous campaign. Further, I thank my academic advisor Prof. Dr. Heinrich Kuhn and my colleagues Dr. Georg N. Krieg and Florian Defregger for many fruitful discussions and helpful thoughts. I also thank Prof. Dr. Klaus D. Wilde for refereeing the thesis.

1 Introduction

1.1 Lot-Sizing and Scheduling
1.2 Flexible Flow Lines
1.3 Characteristics of the Problem
1.4 Scope and Outline of the Study
2 Lack of Solution Procedures in the Literature
2.1 Lot-Sizing Problems
2.2 Scheduling Problems
2.2.1 Branch & Bound Procedures
2.2.2 Local Search and Metaheuristics
2.2.3 Stage Decomposition Approaches
2.2.4 Job-Oriented Procedures
2.2.5 (Sequential) Loading and Sequencing Algorithms
2.3 Summary and Conclusion
3 An Integrative Solution Approach
4 Phase I: Bottleneck Planning
4.1 Lot-sizing and Scheduling on Parallel Machines
4.2 Literature Review
4.3 Standard Models with Extensions
4.4 Solution Approach
4.5 Computational Results
4.6 Summary
5 Phase II: Schedule Roll-Out
5.1 Premises and Scope
5.2 Scheduling Non-Bottleneck Production Stages
5.3 Two Remarks on the Resulting Schedule
5.4 Summary
6 Phase III: Product-to-Slot Assignment
6.1 Goal Trade-Off
6.2 Genetic Algorithms
6.3 Solution Procedure: Two Nested Genetic Algorithms
6.4 Computational Results
6.5 Summary
7 An Illustrative Example
7.1 Data of the Example
7.2 Solution after Phase I
7.3 Solution after Phase II
7.4 Solution after Phase III
8 An Application in the Semiconductor Industry
8.1 Semiconductor Process Flow
8.2 Problem Specifics and Dimensions
8.3 Modeling Assumptions
8.4 Short-Term Production Planning
8.5 Medium-Term Capacity Planning
9 Summary and Conclusions
List of Abbreviations
List of Symbols
References.