• Genetic algorithms for advanced planning and scheduling in supply networks 50% rabatu

Genetic algorithms for advanced planning and scheduling in supply networks 50% rabatu

  • Autor: Anna Ławrynowicz
  • Wydawca: Difin
  • ISBN: 978-83-7641-778-3
  • Data wydania: 2013
  • Liczba stron/format: 212/B5
  • Oprawa: miękka

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Advances in intelligent methods have significantly modified the business organization of enterprises and the way they do business. The efficient management of the new form of business needs new tools. Therefore, this book presents an optimization method with genetic algorithms for operating decision making in supply networks. This book focuses on both the theory and applications of genetic algorithms for planning and scheduling in supply networks and is divided into two parts.

Part I presents the general aspects of the supply network management, with background information on the planning and scheduling methods. The main objective of studies presented in Part I is to analyze the concept and forms of inter-organizational cooperation from a viewpoint of the operating decision making.

Part II introduces to genetic algorithms and presents their applications. The theoretical side deals with the procedures of genetic algorithms, representation, selection and genetic operators. The purpose of this book is to pay special attention to applications of genetic algorithms to the supply network planning and scheduling. The applications of genetic algorithms concern mainly production environments.

CONTENTS:

Introduction  

PART I.  BASICS OF THE SUPPLY NETWORK MANAGEMENT

1. Theoretical foundations of the production in network   

1.1. Definition of key terms
1.2. Supply network management

2. Planning and scheduling in production environments   

2.1. Production planning
2.2. Production scheduling
2.3. Advanced planning and scheduling
2.4. Planning and scheduling methods

PART II. GENETIC ALGORITHMS AND THEIR APPLICATIONS  

3. Introduction to genetic algorithms

3.1. Terms and definitions
3.2. Procedure of the genetic algorithm
3.3. Representation
3.4. Selection
3.5. Crossover
3.6. Mutation   

4. Optimization of the supply network configuration  

4.1. Genetic algorithm for the plant layout problem
4.2. Facility layout optimization using genetic algorithms
4.3. Grouping of parts and machines by genetic algorithms

5. A review of the evolutionary-based methods for planning  and scheduling

5.1. Applications of genetic algorithms for solving production planning problems
5.2. Development of genetic algorithms for production scheduling problems
5.3. Using genetic algorithms to solve the lot-sizing problem
5.4. Assembly line balancing problem with genetic algorithms

6. New approaches to planning and scheduling in supply networks  

6.1. Hybrid approach with an expert system and a genetic algorithm to production  management
6.2. An application of the genetic approach for advanced scheduling in industrial clusters
6.3. A two-phase system for planning and scheduling in an industrial cluster
6.4. A novel intelligent method to support operations management in clusters
 
Summary and conclusions

References

Appendix: Production plans

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