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  • Produktbild: Learning in Automated Manufacturing
  • Produktbild: Learning in Automated Manufacturing

Learning in Automated Manufacturing A Local Search Approach

Aus der Reihe Production and Logistics

Fr. 72.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.08.1994

Verlag

Physica

Seitenzahl

257

Maße (L/B/H)

23.5/15.5/1.5 cm

Gewicht

417 g

Auflage

Softcover reprint of the original 1st edition 1994

Sprache

Englisch

ISBN

978-3-7908-0792-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.08.1994

Verlag

Physica

Seitenzahl

257

Maße (L/B/H)

23.5/15.5/1.5 cm

Gewicht

417 g

Auflage

Softcover reprint of the original 1st edition 1994

Sprache

Englisch

ISBN

978-3-7908-0792-9

Herstelleradresse

Physica Verlag
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Learning in Automated Manufacturing
  • Produktbild: Learning in Automated Manufacturing
  • I. Local Search and Extensions.- 1. Introduction — Local Search.- 2. Infamous Scheduling Problems.- 3. Simulated Annealing.- 4. Tabu Search.- 5. Genetic Algorithms.- II. The Traveling Salesman Problem.- 1. Introduction and Survey.- 2. Effective Genetic Local Search.- 2.1 Numerical Results.- 2.2 Discussion.- 3. Bounded Genetic Local Search.- 3.1 Implementation Details and Numerical Results.- 3.2 Conclusions.- 4. Variable Depth Search Based Learning.- 4.1 Ejection Chains.- 4.2 Computational Results.- 4.3 Conclusions.- III. Job Shop Scheduling.- 1. Introduction — Conventional and New Solution Techniques.- 2. Evolution Based Learning.- 2.1 Genetic Enumeration.- 2.2 Heuristics for the Job Shop Scheduling Problem.- 2.3 Learning by Population Genetics.- 2.4 Details of Implementation and Computational Results.- 2.5 Conclusions.- 3. Learning by Constraint Propagation.- 3.1 Constraint Propagation and Backtrack Search.- 3.2 The Job Shop Constraint Satisfaction Problem.- 3.3 An Immediate Selection Heuristic.- 3.4 Computational Results.- 3.5 Conclusions.- 4. Decomposition Based Learning.- 4.1 Opportunistic Scheduling Heuristics.- 4.2 Constraint Propagation, Local Consistency, and Genetic Based Learning.- 4.3 Computational Results.- 4.4 Conclusions.- IV. Flexible Manufacturing Systems.- 1. Clustering in Cellular Manufacturing.- 1.1 Introduction and Background.- 1.2 The Clique-Partitioning Problem.- 1.3 An Ejection Chain Heuristic.- 1.4 Computational Results of the Heuristics.- 1.5 An EC-Based Branch and Bound Algorithm.- 1.6 Computational Results of the EC-Based Branch and Bound Algorithm.- 1.7 Conclusions.- 2. Factory Layout Planning.- 2.1 Introduction and Background.- 2.2 Fundamental Results.- 2.3 Advanced Moves.- 2.4 Updates and Data Structure Management for Advanced Moves.- 2.5 Algorithms for Generating and Improving Maximally Planar Graphs.- 2.6 Ejection Chains.- 2.7 Computational Results and Comments.- 3. Workload Balancing.- 3.1 Introduction and Background.- 3.2 The Condorcet Model.- 3.3 The Model.- 3.4 Basic Results.- 3.5 Main Results.- 3.6 Polynomial Algorithms.- 3.7 Examples and Counterexamples.- 3.8 Conclusions.- Epilogue.- References.