
Learning in Automated Manufacturing
A Local Search Approach
Erwin Pesch(Author)
Physica (Publisher)
Published on 30. August 1994
Book
Paperback/Softback
XIII, 257 pages
978-3-7908-0792-9 (ISBN)
Description
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 constraint based reasoning, both with respect to their use in automated manufacturing. We restrict our attention 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 scheduling systems, enriched by a number of constraint-based local decision rules in order to introduce problem specific knowledge.
More details
Series
Edition
Softcover reprint of the original 1st ed. 1994
Language
English
Place of publication
Heidelberg
Germany
Target group
Professional and scholarly
Research
Illustrations
3 s/w Abbildungen
XIII, 257 p. 3 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
417 gr
ISBN-13
978-3-7908-0792-9 (9783790807929)
DOI
10.1007/978-3-642-49366-9
Schweitzer Classification
Person
Prof. Dr. Florian Jaehn ist Wirtschaftsmathematiker und Betriebswirt und nach einer Professur an der Universität Augsburg seit 2017 Inhaber eines Lehrstuhls für Management Science und Operations Research an der Helmut-Schmidt-Universität in Hamburg.
Prof. Dr. Erwin Pesch ist Mathematiker und Betriebswirt und nach einer Professur an der Universität Bonn seit 2001 Inhaber eines Lehrstuhls für Wirtschaftsinformatik an der Universität Siegen. Seit 2016 ist er zudem Direktor des Centers für Advanced Studies in Management an der HHL Leipzig.
Content
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.