Learning in Automated Manufacturing: A Local Search Approach
Author:   Erwin Pesch
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   Softcover reprint of the original 1st ed. 1994
ISBN:  

9783790807929


Pages:   257
Publication Date:   30 August 1994
Format:   Paperback
Availability:   Out of stock   Availability explained
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Learning in Automated Manufacturing: A Local Search Approach


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Overview

The central purpose of this book is to acquaint the reader especially with the cases of local-search-based learning, as well as to introduce methods of contraint-based reasoning, both with respect to their use in automated manufacturing. Attention is restricted to job-shop scheduling, as well as to one-machine scheduling with sequence-dependent setup times. Additionally, some design and planning issues in flexible manufacturing systems are considered. General-purpose search methods - which in particular include methods from local search, such as simulated annealing, tabu search and genetic algorithms - are the basic ingredients of the proposed intelligent knowledge-based local decision rules in order to introduce problem-specific knowledge.

Full Product Details

Author:   Erwin Pesch
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Physica-Verlag GmbH & Co
Edition:   Softcover reprint of the original 1st ed. 1994
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   0.421kg
ISBN:  

9783790807929


ISBN 10:   3790807923
Pages:   257
Publication Date:   30 August 1994
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

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.- 3. Bounded Genetic Local Search.- 4. Variable Depth Search Based Learning.- III. Job Shop Scheduling.- 1. Introduction — Conventional and New Solution Techniques.- 2. Evolution Based Learning.- 3. Learning by Constraint Propagation.- 4. Decomposition Based Learning.- IV. Flexible Manufacturing Systems.- 1. Clustering in Cellular Manufacturing.- 2. Factory Layout Planning.- 3. Workload Balancing.- Epilogue.- References.

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