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Christoph Schwindt

Bio: Christoph Schwindt is an academic researcher from Clausthal University of Technology. The author has contributed to research in topics: Scheduling (production processes) & Batch production. The author has an hindex of 20, co-authored 59 publications receiving 1854 citations. Previous affiliations of Christoph Schwindt include Karlsruhe Institute of Technology & Martin Luther University of Halle-Wittenberg.


Papers
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Book
24 Jul 2012
TL;DR: The proposed Resource-Constrained Project Scheduling - Minimization of General Objective Functions incorporated branch-and-bound algorithms for resource investment, resource levelling, and resource renting problems, and additional types of shifts and sets of schedules.
Abstract: 1 Temporal Project Scheduling.- 1.1 Minimum and maximum time lags.- 1.2 Activity-on-node project networks.- 1.3 Temporal project scheduling computations.- 1.4 Orders in the set of activities.- 2 Resource-Constrained Project Scheduling - Minimization of Project Duration.- 2.1 Formulation of the problem.- 2.2 Cycle structures in activity-on-node project networks.- 2.3 Properties of the feasible region.- 2.3.1 Strict orders and order polyhedra.- 2.3.2 Forbidden sets and resolution of resource conflicts.- 2.4 Different types of shifts and sets of schedules.- 2.5 Branch-and-bound and truncated branch-and-bound methods.- 2.5.1 Enumeration scheme.- 2.5.2 Preprocessing.- 2.5.3 Lower bounds.- 2.5.4 Branch-and-bound algorithm.- 2.5.5 Truncated branch-and-bound methods.- 2.5.6 Alternative enumeration schemes.- 2.5.7 Alternative preprocessing and constraint propagation.- 2.5.8 Alternative lower bounds.- 2.6 Priority-rule methods.- 2.6.1 Direct method.- 2.6.2 Decomposition methods.- 2.6.3 Priority rules.- 2.6.4 Serial generation scheme.- 2.6.5 Parallel generation scheme.- 2.7 Schedule-improvement procedures.- 2.7.1 Genetic algorithm.- 2.7.2 Tabu search.- 2.8 Experimental performance analysis.- 2.8.1 Random generation of projects.- 2.8.2 Computational experience.- 2.9 Application to make-to-order production in manufacturing industry.- 2.10 Regular objective functions different from project duration.- 2.11 Calendarization.- 2.12 Project scheduling with cumulative resources.- 2.12.1 Discrete cumulative resources.- 2.12.2 Continuous cumulative resources.- 2.13 Project scheduling with synchronizing resources.- 2.14 Project scheduling with sequence-dependent changeover times.- 2.15 Multi-mode project scheduling problems.- 2.15.1 Problem formulation and basic properties.- 2.15.2 Solution methods.- 2.16 Application to batch production in process industries.- 2.16.1 Case study.- 2.16.2 Batching problem.- 2.16.3 Project scheduling model for batch scheduling.- 2.16.4 Solution procedure for batch scheduling.- 3 Resource-Constrained Project Scheduling - Minimization of General Objective Functions.- 3.1 Different objective functions.- 3.2 Additional types of shifts and sets of schedules.- 3.3 Classification of objective functions.- 3.3.1 Separable and resource-utilization dependent objective functions.- 3.3.2 Class 1 of regular objective functions.- 3.3.3 Class 2 of antiregular objective functions.- 3.3.4 Class 3 of convex objective functions.- 3.3.5 Class 4 of binary-monotone objective functions.- 3.3.6 Class 5 of quasiconcave objective functions.- 3.3.7 Class 6 of locally regular objective functions.- 3.3.8 Class 7 of locally quasiconcave objective functions.- 3.4 Time complexity of time-constrained project scheduling.- 3.5 Relaxation-based approach for function classes 1 to 5.- 3.5.1 General enumeration scheme.- 3.5.2 Branch-and-bound algorithm for the net present value problem.- 3.5.3 Branch-and-bound algorithm for the earliness-tardiness problem.- 3.6 Tree-based approach for function classes 6 and 7.- 3.6.1 General enumeration scheme.- 3.6.2 Branch-and-bound algorithms for resource investment, resource levelling, and resource renting problems.- 3.6.3 Experimental performance analysis.- 3.6.4 Alternative lower bounds.- 3.7 Priority-rule methods.- 3.7.1 Time-constrained project scheduling.- 3.7.2 Resource-constrained project scheduling.- 3.7.3 Experimental performance analysis.- 3.8 Schedule-improvement procedures.- 3.8.1 Neighborhoods for project scheduling problems.- 3.8.2 A tabu search procedure.- 3.9 Application to investment projects.- 3.9.1 Computation of the net present value function.- 3.9.2 Decision support.- 3.10 Hierarchical project planning.- References.- List of Symbols.- Three-Field Classification for Resource-Constrained Project Scheduling.

341 citations

BookDOI
01 Jan 2002
TL;DR: In this article, the authors propose a method for reducing the time complexity of time-constrained project scheduling in an activity-on-node project network, based on a tree-based approach and a truncated branch-and-bound algorithm.
Abstract: 1 Temporal Project Scheduling.- 1.1 Minimum and maximum time lags.- 1.2 Activity-on-node project networks.- 1.3 Temporal project scheduling computations.- 1.4 Orders in the set of activities.- 2 Resource-Constrained Project Scheduling - Minimization of Project Duration.- 2.1 Formulation of the problem.- 2.2 Cycle structures in activity-on-node project networks.- 2.3 Properties of the feasible region.- 2.3.1 Strict orders and order polyhedra.- 2.3.2 Forbidden sets and resolution of resource conflicts.- 2.4 Different types of shifts and sets of schedules.- 2.5 Branch-and-bound and truncated branch-and-bound methods.- 2.5.1 Enumeration scheme.- 2.5.2 Preprocessing.- 2.5.3 Lower bounds.- 2.5.4 Branch-and-bound algorithm.- 2.5.5 Truncated branch-and-bound methods.- 2.5.6 Alternative enumeration schemes.- 2.5.7 Alternative preprocessing and constraint propagation.- 2.5.8 Alternative lower bounds.- 2.6 Priority-rule methods.- 2.6.1 Direct method.- 2.6.2 Decomposition methods.- 2.6.3 Priority rules.- 2.6.4 Serial generation scheme.- 2.6.5 Parallel generation scheme.- 2.7 Schedule-improvement procedures.- 2.7.1 Genetic algorithm.- 2.7.2 Tabu search.- 2.8 Experimental performance analysis.- 2.8.1 Random generation of projects.- 2.8.2 Computational experience.- 2.9 Application to make-to-order production in manufacturing industry.- 2.10 Regular objective functions different from project duration.- 2.11 Calendarization.- 2.12 Project scheduling with cumulative resources.- 2.12.1 Discrete cumulative resources.- 2.12.2 Continuous cumulative resources.- 2.13 Project scheduling with synchronizing resources.- 2.14 Project scheduling with sequence-dependent changeover times.- 2.15 Multi-mode project scheduling problems.- 2.15.1 Problem formulation and basic properties.- 2.15.2 Solution methods.- 2.16 Application to batch production in process industries.- 2.16.1 Case study.- 2.16.2 Batching problem.- 2.16.3 Project scheduling model for batch scheduling.- 2.16.4 Solution procedure for batch scheduling.- 3 Resource-Constrained Project Scheduling - Minimization of General Objective Functions.- 3.1 Different objective functions.- 3.2 Additional types of shifts and sets of schedules.- 3.3 Classification of objective functions.- 3.3.1 Separable and resource-utilization dependent objective functions.- 3.3.2 Class 1 of regular objective functions.- 3.3.3 Class 2 of antiregular objective functions.- 3.3.4 Class 3 of convex objective functions.- 3.3.5 Class 4 of binary-monotone objective functions.- 3.3.6 Class 5 of quasiconcave objective functions.- 3.3.7 Class 6 of locally regular objective functions.- 3.3.8 Class 7 of locally quasiconcave objective functions.- 3.4 Time complexity of time-constrained project scheduling.- 3.5 Relaxation-based approach for function classes 1 to 5.- 3.5.1 General enumeration scheme.- 3.5.2 Branch-and-bound algorithm for the net present value problem.- 3.5.3 Branch-and-bound algorithm for the earliness-tardiness problem.- 3.6 Tree-based approach for function classes 6 and 7.- 3.6.1 General enumeration scheme.- 3.6.2 Branch-and-bound algorithms for resource investment, resource levelling, and resource renting problems.- 3.6.3 Experimental performance analysis.- 3.6.4 Alternative lower bounds.- 3.7 Priority-rule methods.- 3.7.1 Time-constrained project scheduling.- 3.7.2 Resource-constrained project scheduling.- 3.7.3 Experimental performance analysis.- 3.8 Schedule-improvement procedures.- 3.8.1 Neighborhoods for project scheduling problems.- 3.8.2 A tabu search procedure.- 3.9 Application to investment projects.- 3.9.1 Computation of the net present value function.- 3.9.2 Decision support.- 3.10 Hierarchical project planning.- References.- List of Symbols.- Three-Field Classification for Resource-Constrained Project Scheduling.

287 citations

Book ChapterDOI
01 Jan 1999
TL;DR: With the development of project scheduling models and methods arose the need for data instances in order to benchmark the solution procedures, and characteristics of the projects have to be identified to allow a systematic evaluation of the performance of algorithms.
Abstract: With the development of project scheduling models and methods arose the need for data instances in order to benchmark the solution procedures. Generally, benchmark instances can be distinguished by their origin into real world problems and artificial problems. The analysis of algorithmic performance on real world problem instances is of a high practical relevance, but at the same time it is only an analysis of individual cases. Consequently, general conclusions about the algorithms cannot be drawn. A solution procedure which shows very good performance on one real world instance might produce poor results on another. In order to allow a systematic evaluation of the performance of algorithms, characteristics of the projects have to be identified. The characteristics can then serve as the parameters for the systematic generation of artificial instances. The variation of the levels of these problem parameters in a full factorial design study allows to produce a set of well-balanced instances (cf. Montgomery 1976).

140 citations

Journal ArticleDOI
TL;DR: A new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far, and the new approach decomposes detailed production scheduling for batch production into batching and batch scheduling.
Abstract: An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.

113 citations

Journal ArticleDOI
TL;DR: Some properties of the feasible region of the project scheduling problem with inventory constraints and general temporal constraints are studied and it is shown how to resolve so-called resource conflicts.
Abstract: Inventory constraints refer to so-called cumulative resources, which can store a single or several different products and have a prescribed minimum and maximum inventory, where the inventory is depleted and replenished over time. Some additional applications of cumulative resources, e.g. to investment projects, are also discussed in this paper.

113 citations


Cited by
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Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
01 May 1975
TL;DR: The Fundamentals of Queueing Theory, Fourth Edition as discussed by the authors provides a comprehensive overview of simple and more advanced queuing models, with a self-contained presentation of key concepts and formulae.
Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

2,562 citations

Book
01 Jan 2006
TL;DR: Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.
Abstract: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming - Survey-style chapters - Five chapters on applications Table of Contents Foreword (Ugo Montanari) Part I : Foundations Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) Chapter 3. Constraint Propagation (Christian Bessiere) Chapter 4. Backtracking Search Algorithms (Peter van Beek) Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel) Chapter 7. Tractable Structures for CSPs (Rina Dechter) Chapter 8. The Complexity of Constraint Languages (David Cohen, Peter Jeavons) Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) Chapter 10. Symmetry in Constraint Programming (Ian P. Gent, Karen E. Petrie, Jean-Francois Puget) Chapter 11. Modelling (Barbara M. Smith) Part II : Extensions, Languages, and Applications Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte) Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) Chapter 17. Constraints over Structured Domains (Carmen Gervet) Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh) Chapter 19. Temporal CSPs (Manolis Koubarakis) Chapter 20. Distributed Constraint Programming (Boi Faltings) Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel) Chapter 22. Constraint-Based Scheduling and Planning (Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten) Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) Chapter 24. Configuration (Ulrich Junker) Chapter 25. Constraint Applications in Networks (Helmut Simonis) Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

1,527 citations

Journal ArticleDOI
TL;DR: A classification scheme is provided, i.e. a description of the resource environment, the activity characteristics, and the objective function, respectively, which is compatible with machine scheduling and which allows to classify the most important models dealt with so far, and a unifying notation is proposed.

1,489 citations

Journal ArticleDOI
TL;DR: An overview over various extensions of the basic RCPSP, including popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, is given.

856 citations