Springer Advanced Manufacturing

The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms

By: Brahim Rekiek, Alain Delchambre

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Hardcover | 10 November 2005

At a Glance

Hardcover
180 Pages

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23.5 x 15.88 x 1.27

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Efficient assembly line design is a problem of industrial importance. Assembly line design is often complex due to the multiple components involved: efficiency, cost and space. The aim is to integrate the design with operations issues, minimising costs.

It is important to give the designer tools to help him meet the different objectives. 3 techniques based on the Grouping Genetic Algorithm are presented which can be used to aid assembly line design:

- 'equal piles for assembly lines' deals with assembly line balancing (balancing stations' loads);

- a method based on a multiple objective grouping genetic algorithm (MO-GGA) deals with resource planning (selection of equipment);

- 'balance for operation', deals with the changes during the operation of assembly lines.

This book will interest technical personnel in design, planning and production departments in industry as well as managers in industry. It will also be of use to researchers and postgraduates in mechanical, manufacturing or micro-engineering.

Industry Reviews

The design of efficient assembly lines is a problem of considerable industrial importance and contributions in this domain are always appreciated. This book aims to develop new and generic assembly line tools which are capable of evolving a wide range of different line designs with minimal reconfiguration by a designer. In particular, it presents new techniques to deal with assembly line balancing and resource planning.

The book is well written and documented. It is a good reference for postgraduate students and practitioners who are working in the field of resource management and scheduling in manufacturing systems.

International Journal of Advanced Manufacturing Technology

Reviewer: A A Afify, MEC, Cardiff University

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Engineering & Technology Industrial Chemistry & Manufacturing Technologies Technology in General Engineering in General Mechanical Engineering & Materials Production & Industrial Engineering Technical Design Graphical & Digital Media Applications

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Published: 28th October 2010

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Assembly Line Design Problems
Designing Assembly Lines	p. 3
Introduction	p. 3
Assembly Line Design	p. 3
Designing or Optimising?	p. 5
Layout of the Book	p. 6
Design Approaches	p. 7
Introduction	p. 7
Why the Design is Difficult?	p. 8
Design and Search Approaches	p. 8
The Gap Between Theory and Practice	p. 8
Input Data	p. 9
Multiple Objective Problem	p. 9
Variability	p. 9
Scheduling	p. 9
Layout	p. 10
About the Quality of a Design	p. 10
Assembly Line Design Evolution	p. 10
Assembly Line: History and Formulation	p. 13
Introduction	p. 13
Evolution of Today's Manufacturing Issues	p. 13
First Metals	p. 13
Carpenters and Smiths	p. 13
Cottage Industries	p. 14
Factory System	p. 14
Mass Production	p. 14
Computers in Manufacturing	p. 15
Assembly Line Systems	p. 15
Notation and Definitions	p. 16
Assembly Line Balancing Problems	p. 19
Assembly Line Models	p. 19
Variability of Tasks Process Time	p. 20
Line Configuration	p. 21
Additional Constraints	p. 23
Assembly Line Design Problems	p. 25
Why is the Balancing Problem Hard to Solve?	p. 27
Evolutionary Combinatorial Optimisation
Evolutionary Combinatorial Optimisation	p. 31
Introduction	p. 31
System Organisation	p. 31
How Do Genetic Algorithms Work?	p. 32
Representation	p. 33
Initialisation of the Population	p. 34
Sampling Mechanism	p. 35
Genetic Operators	p. 36
Landscapes and Fitness	p. 38
Population	p. 38
Simple...but it Works!	p. 38
Multiple Objective Grouping Genetic Algorithm	p. 39
Introduction	p. 39
Multiple Objective Optimisation	p. 39
The State of the Art	p. 40
The Use of Aggregating Functions	p. 41
Non-Pareto Approaches	p. 41
Pareto-based Approaches	p. 42
Preferences and Local Search Methods	p. 42
Constrained Problems	p. 43
Grouping Problems and the Grouping Genetic Algorithm	p. 44
Encoding Scheme	p. 44
Crossover Operator	p. 45
Mutation Operator	p. 46
Inversion Operator	p. 46
Multiple Objective Grouping Genetic Algorithm	p. 46
Control Strategy	p. 47
Individual Construction Algorithm	p. 48
Overall Architecture of the Evolutionary Method	p. 48
Branching on Populations	p. 49
The Detailed Example	p. 51
Assembly Line Layout
Equal Piles for Assembly Line Balancing	p. 59
Introduction	p. 59
The State of the Art	p. 59
Exact Methods	p. 59
Approximated Methods	p. 61
Equal Piles for Assembly Line Balancing	p. 62
Motivation and Inspiration From Nature	p. 63
Input Data	p. 64
Customising the Grouping Genetic Algorithm to the Equal Piles Assembly Line Problem	p. 64
Experimental Results	p. 69
Extension to Multi-product Assembly Line	p. 71
Multiple Objective Problem	p. 71
Overall Architecture	p. 72
The Resource Planning for Assembly Line	p. 77
Introduction	p. 77
The State of the Art	p. 78
Dealing with Real-world Hybrid Assembly Line Design	p. 79
Cost	p. 79
Process Time	p. 80
Availability	p. 82
Station Space	p. 83
Incompatibilities Among Several Types of Equipment	p. 84
Input Data	p. 84
Overall Method	p. 85
Distributing Tasks Among Stations	p. 85
Selecting Equipment	p. 86
Heuristics	p. 89
Dealing with a Multi-product Assembly Line	p. 90
Complying with Hard Constraints	p. 91
Application of the Method	p. 92
Balance for Operation	p. 93
Introduction	p. 93
Multi-product Assembly Line	p. 93
The State of the Art	p. 94
Classical Methods	p. 94
Heuristics	p. 95
Ordering Genetic Algorithm	p. 95
Algorithm	p. 95
Heuristics	p. 97
Balance for Operation Concept	p. 99
Non-fixed Number of Stations	p. 100
Fixed Number of Stations	p. 102
The Integrated Method
Evolving to Integrate Logical and Physical Layout of Assembly Lines	p. 105
Introduction	p. 105
The State of the Art	p. 105
Assembly Line Design	p. 106
Integrated Approach	p. 106
Development of the Interactive Method	p. 108
Global Search Phase	p. 115
Application	p. 116
Concurrent Approach to Design Assembly Lines	p. 121
Introduction	p. 121
Concurrent Approach	p. 121
Assembly Line Design	p. 122
Data Preparation Phase	p. 123
Optimisation Phase	p. 124
Mapping Phase	p. 124
Case Studies	p. 124
Assembly Line Balancing Application: Outboard Motor	p. 125
Resource Planning Application: Car Alternator	p. 128
A Real-world Example Optimised by the OptiLine Software	p. 137
Conclusions and Future Work	p. 145
We Attained	p. 145
Tendencies and Orientations	p. 145
Data Collection	p. 146
Model Formulation	p. 146
Validation and Output Analysis	p. 146
The Proposed Approach	p. 147
References	p. 149
Index	p. 159
Table of Contents provided by Ingram. All Rights Reserved.

Springer Advanced Manufacturing

The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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