Assembly line design : the balancing of mixed-model hybrid assembly lines with genetic algorithms

Title:

Personal Author:

Rekiek, Brahim

Series:

Springer series in advanced manufacturing

Publication Information:

London : Springer, 2006

ISBN:

9781846281129

Subject Term:

Assembly-line methods

Genetic algorithms

Added Author:

Delchambre, Alain

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010114007	TS178.4 R44 2006	Open Access Book	Book	Searching... Unknown

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.

Author Notes

Rekiek Brahim received a license in Physics from the Univetsité Abdel Malek Essaadi, Tetouan and the D.E.S in production and robotics at the Université Libre de Bruxelles (U.L.B.), Brussels in 1994 and 1996, respectively. He received his Ph.D. degree in Artificial Intelligence in 2000 from the Université Libre de Bruxelles. Much of his work was carried out in collaboration with industrial companies. From 2001 to 2002, he worked as a member of the Sales Marketing Service at Fabricom Airports Systems, Brussels, Belgium. Since 2002, he has been working as a member of the Projects Management team. He has been an analyst and project manager responsible for the development of the baggage handling systems of many airports. His interests include software architecture, systems design, concurrent engineering, and artificial intelligence.

Alain Delchambre obtained his Master and Phd degrees in Mechanical Engineering from the University of Brussels (ULB) in 1983 and 1990 respectively. After three years in industry, he joined a research centre for the Belgian Metalworking Industry (CRIF/WTCM). Since 1994, he has been a Professor at the Faculty of Applied Sciences in ULB and is head of the CADCAM department. He has published three books and more than 80 papers in the areas of concurrent engineering, computer aided design and genetic algorithms.

Part I Assembly Line Design Problems
1 Designing Assembly Lines	p. 3
1.1 Introduction	p. 3
1.2 Assembly Line Design	p. 3
1.3 Designing or Optimising?	p. 5
1.4 Layout of the Book	p. 6
2 Design Approaches	p. 7
2.1 Introduction	p. 7
2.2 Why the Design is Difficult?	p. 8
2.3 Design and Search Approaches	p. 8
2.4 The Gap Between Theory and Practice	p. 8
2.4.1 Input Data	p. 9
2.4.2 Multiple Objective Problem	p. 9
2.4.3 Variability	p. 9
2.4.4 Scheduling	p. 9
2.4.5 Layout	p. 10
2.5 About the Quality of a Design	p. 10
2.6 Assembly Line Design Evolution	p. 10
3 Assembly Line: History and Formulation	p. 13
3.1 Introduction	p. 13
3.2 Evolution of Today's Manufacturing Issues	p. 13
3.2.1 First Metals	p. 13
3.2.2 Carpenters and Smiths	p. 13
3.2.3 Cottage Industries	p. 14
3.2.4 Factory System	p. 14
3.2.5 Mass Production	p. 14
3.2.6 Computers in Manufacturing	p. 15
3.3 Assembly Line Systems	p. 15
3.4 Notation and Definitions	p. 16
3.5 Assembly Line Balancing Problems	p. 19
3.5.1 Assembly Line Models	p. 19
3.5.2 Variability of Tasks Process Time	p. 20
3.5.3 Line Configuration	p. 21
3.5.4 Additional Constraints	p. 23
3.5.5 Assembly Line Design Problems	p. 25
3.6 Why is the Balancing Problem Hard to Solve?	p. 27
Part II Evolutionary Combinatorial Optimisation
4 Evolutionary Combinatorial Optimisation	p. 31
4.1 Introduction	p. 31
4.2 System Organisation	p. 31
4.3 How Do Genetic Algorithms Work?	p. 32
4.3.1 Representation	p. 33
4.3.2 Initialisation of the Population	p. 34
4.3.3 Sampling Mechanism	p. 35
4.3.4 Genetic Operators	p. 36
4.4 Landscapes and Fitness	p. 38
4.5 Population	p. 38
4.6 Simple...but it Works!	p. 38
5 Multiple Objective Grouping Genetic Algorithm	p. 39
5.1 Introduction	p. 39
5.2 Multiple Objective Optimisation	p. 39
5.3 The State of the Art	p. 40
5.3.1 The Use of Aggregating Functions	p. 41
5.3.2 Non-Pareto Approaches	p. 41
5.3.3 Pareto-based Approaches	p. 42
5.3.4 Preferences and Local Search Methods	p. 42
5.3.5 Constrained Problems	p. 43
5.4 Grouping Problems and the Grouping Genetic Algorithm	p. 44
5.4.1 Encoding Scheme	p. 44
5.4.2 Crossover Operator	p. 45
5.4.3 Mutation Operator	p. 46
5.4.4 Inversion Operator	p. 46
5.5 Multiple Objective Grouping Genetic Algorithm	p. 46
5.5.1 Control Strategy	p. 47
5.5.2 Individual Construction Algorithm	p. 48
5.5.3 Overall Architecture of the Evolutionary Method	p. 48
5.5.4 Branching on Populations	p. 49
5.6 The Detailed Example	p. 51
Part III Assembly Line Layout
6 Equal Piles for Assembly Line Balancing	p. 59
6.1 Introduction	p. 59
6.2 The State of the Art	p. 59
6.2.1 Exact Methods	p. 59
6.2.2 Approximated Methods	p. 61
6.3 Equal Piles for Assembly Line Balancing	p. 62
6.3.1 Motivation and Inspiration From Nature	p. 63
6.3.2 Input Data	p. 64
6.3.3 Customising the Grouping Genetic Algorithm to the Equal Piles Assembly Line Problem	p. 64
6.3.4 Experimental Results	p. 69
6.4 Extension to Multi-product Assembly Line	p. 71
6.4.1 Multiple Objective Problem	p. 71
6.4.2 Overall Architecture	p. 72
7 The Resource Planning for Assembly Line	p. 77
7.1 Introduction	p. 77
7.2 The State of the Art	p. 78
7.3 Dealing with Real-world Hybrid Assembly Line Design	p. 79
7.3.1 Cost	p. 79
7.3.2 Process Time	p. 80
7.3.3 Availability	p. 82
7.3.4 Station Space	p. 83
7.3.5 Incompatibilities Among Several Types of Equipment	p. 84
7.4 Input Data	p. 84
7.5 Overall Method	p. 85
7.5.1 Distributing Tasks Among Stations	p. 85
7.5.2 Selecting Equipment	p. 86
7.5.3 Heuristics	p. 89
7.5.4 Dealing with a Multi-product Assembly Line	p. 90
7.5.5 Complying with Hard Constraints	p. 91
7.6 Application of the Method	p. 92
8 Balance for Operation	p. 93
8.1 Introduction	p. 93
8.2 Multi-product Assembly Line	p. 93
8.3 The State of the Art	p. 94
8.3.1 Classical Methods	p. 94
8.4 Heuristics	p. 95
8.5 Ordering Genetic Algorithm	p. 95
8.5.1 Algorithm	p. 95
8.5.2 Heuristics	p. 97
8.6 Balance for Operation Concept	p. 99
8.6.1 Non-fixed Number of Stations	p. 100
8.6.2 Fixed Number of Stations	p. 102
Part IV The Integrated Method
9 Evolving to Integrate Logical and Physical Layout of Assembly Lines	p. 105
9.1 Introduction	p. 105
9.2 The State of the Art	p. 105
9.3 Assembly Line Design	p. 106
9.4 Integrated Approach	p. 106
9.4.1 Development of the Interactive Method	p. 108
9.4.2 Global Search Phase	p. 115
9.5 Application	p. 116
10 Concurrent Approach to Design Assembly Lines	p. 121
10.1 Introduction	p. 121
10.2 Concurrent Approach	p. 121
10.3 Assembly Line Design	p. 122
10.3.1 Data Preparation Phase	p. 123
10.3.2 Optimisation Phase	p. 124
10.3.3 Mapping Phase	p. 124
10.4 Case Studies	p. 124
10.4.1 Assembly Line Balancing Application: Outboard Motor	p. 125
10.4.2 Resource Planning Application: Car Alternator	p. 128
11 A Real-world Example Optimised by the OptiLine Software	p. 137
12 Conclusions and Future Work	p. 145
12.1 We Attained	p. 145
12.2 Tendencies and Orientations	p. 145
12.3 Data Collection	p. 146
12.4 Model Formulation	p. 146
12.5 Validation and Output Analysis	p. 146
12.6 The Proposed Approach	p. 147
References	p. 149
Index	p. 159

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Table of Contents