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 |  موضوع: كتاب Assembly Line Design الأحد 11 أكتوبر 2020, 10:11 pm | |
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Assembly Line Design
The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms
With 95 Figures
Brahim Rekiek and Alain Delchambre
و المحتوى كما يلي :
Contents
Part I Assembly Line Design Problems
1 Designing Assembly Lines . 3
1.1 Introduction 3
1.2 Assembly Line Design 3
1.3 Designing or Optimising? . 5
1.4 Layout of the Book 6
2 Design Approaches 7
2.1 Introduction 7
2.2 Why the Design is Difficult? 8
2.3 Design and Search Approaches 8
2.4 The Gap Between Theory and Practice . 8
2.4.1 Input Data . 9
2.4.2 Multiple Objective Problem 9
2.4.3 Variability 9
2.4.4 Scheduling 9
2.4.5 Layout . 10
2.5 About the Quality of a Design 10
2.6 Assembly Line Design Evolution . 10
3 Assembly Line: History and Formulation 13
3.1 Introduction 13
3.2 Evolution of Today’s Manufacturing Issues 13
3.2.1 First Metals 13
3.2.2 Carpenters and Smiths . 13
3.2.3 Cottage Industries . 14
3.2.4 Factory System . 14
3.2.5 Mass Production 14
3.2.6 Computers in Manufacturing . 15
3.3 Assembly Line Systems 15
xiiixiv Contents
3.4 Notation and Definitions . 16
3.5 Assembly Line Balancing Problems 19
3.5.1 Assembly Line Models . 19
3.5.2 Variability of Tasks Process Time . 20
3.5.3 Line Configuration 21
3.5.4 Additional Constraints . 23
3.5.5 Assembly Line Design Problems . 25
3.6 Why is the Balancing Problem Hard to Solve? 27
Part II Evolutionary Combinatorial Optimisation
4 Evolutionary Combinatorial Optimisation 31
4.1 Introduction 31
4.2 System Organisation . 31
4.3 How Do Genetic Algorithms Work? 32
4.3.1 Representation 33
4.3.2 Initialisation of the Population 34
4.3.3 Sampling Mechanism 35
4.3.4 Genetic Operators . 36
4.4 Landscapes and Fitness 38
4.5 Population . 38
4.6 Simple... but it Works! . 38
5 Multiple Objective Grouping Genetic Algorithm . 39
5.1 Introduction 39
5.2 Multiple Objective Optimisation 39
5.3 The State of the Art . 40
5.3.1 The Use of Aggregating Functions . 41
5.3.2 Non-Pareto Approaches 41
5.3.3 Pareto-based Approaches . 42
5.3.4 Preferences and Local Search Methods . 42
5.3.5 Constrained Problems 43
5.4 Grouping Problems and the Grouping Genetic Algorithm . 44
5.4.1 Encoding Scheme 44
5.4.2 Crossover Operator 45
5.4.3 Mutation Operator 46
5.4.4 Inversion Operator 46
5.5 Multiple Objective Grouping Genetic Algorithm . 46
5.5.1 Control Strategy 47
5.5.2 Individual Construction Algorithm 48
5.5.3 Overall Architecture of the Evolutionary Method 48
5.5.4 Branching on Populations 49
5.6 The Detailed Example . 51Contents xv
Part III Assembly Line Layout
6 Equal Piles for Assembly Line Balancing 59
6.1 Introduction 59
6.2 The State of the Art . 59
6.2.1 Exact Methods 59
6.2.2 Approximated Methods 61
6.3 Equal Piles for Assembly Line Balancing . 62
6.3.1 Motivation and Inspiration From Nature . 63
6.3.2 Input Data . 64
6.3.3 Customising the Grouping Genetic Algorithm to the
Equal Piles Assembly Line Problem 64
6.3.4 Experimental Results 69
6.4 Extension to Multi-product Assembly Line . 71
6.4.1 Multiple Objective Problem 71
6.4.2 Overall Architecture . 72
7 The Resource Planning for Assembly Line . 77
7.1 Introduction 77
7.2 The State of the Art . 78
7.3 Dealing with Real-world Hybrid Assembly Line Design . 79
7.3.1 Cost . 79
7.3.2 Process Time . 80
7.3.3 Availability . 82
7.3.4 Station Space . 83
7.3.5 Incompatibilities Among Several Types of Equipment 84
7.4 Input Data . 84
7.5 Overall Method . 85
7.5.1 Distributing Tasks Among Stations 85
7.5.2 Selecting Equipment . 86
7.5.3 Heuristics 89
7.5.4 Dealing with a Multi-product Assembly Line 90
7.5.5 Complying with Hard Constraints . 91
7.6 Application of the Method 92
8 Balance for Operation . 93
8.1 Introduction 93
8.2 Multi-product Assembly Line . 93
8.3 The State of the Art . 94
8.3.1 Classical Methods . 94
8.4 Heuristics 95
8.5 Ordering Genetic Algorithm 95
8.5.1 Algorithm 95
8.5.2 Heuristics 97xvi Contents
8.6 Balance for Operation Concept 99
8.6.1 Non-fixed Number of Stations . 100
8.6.2 Fixed Number of Stations 102
Part IV The Integrated Method
9 Evolving to Integrate Logical and Physical Layout of
Assembly Lines 105
9.1 Introduction 105
9.2 The State of the Art . 105
9.3 Assembly Line Design 106
9.4 Integrated Approach . 106
9.4.1 Development of the Interactive Method 108
9.4.2 Global Search Phase . 115
9.5 Application . 116
10 Concurrent Approach to Design Assembly Lines . 121
10.1 Introduction 121
10.2 Concurrent Approach 121
10.3 Assembly Line Design 122
10.3.1 Data Preparation Phase 123
10.3.2 Optimisation Phase 124
10.3.3 Mapping Phase . 124
10.4 Case Studies 124
10.4.1 Assembly Line Balancing Application: Outboard Motor 125
10.4.2 Resource Planning Application: Car Alternator . 128
11 A Real-world Example Optimised by the OptiLine Software137
12 Conclusions and Future Work . 145
12.1 We Attained.. . 145
12.2 Tendencies and Orientations 145
12.3 Data Collection . 146
12.4 Model Formulation 146
12.5 Validation and Output Analysis . 146
12.6 The Proposed Approach 147
References . 149
Index 159List of Abbreviations
AI Artificial intelligence
AL Assembly line
ALB Assembly line balancing
ALD Assembly line design
B&B Branch and bound
B&C Branch and cut
BD Balance delay
BFO Balance for operation
BPP Bin packing problem
CAD Computer aided-design
CE Concurrent engineering
CISAL Outils d’aide `a la conception interactive des produits
et de leur ligne d’assemblage
CM Cellular manufacturing
COP Combinatorial optimisation problem
CS Capacity supply
DFA Design for assembly
DM Decision maker
DP Dynamic programming
E Line efficiency
EPALP Equal piles for assembly line problem
ES Evolutionary strategies
FABLE Fast algorithm for balancing line effectively
FFD First fit decreasing
FG Functional group
GA Genetic algorithm
GC Goal chasing method
GGA Grouping genetic algorithm
GT Group technology
HAL Hybrid assembly line
I Line idle time
xviixviii List of Abbreviations
IB Imbalance
ICA Individual construction algorithm
JIT Just in time
LL Logical layout
LP Linear programming
MAL Manual assembly line
MCDA Multi-criteria decision-aid
ML Model launching
MOALBP Multiple objective ALBP
MOB-ES Multiple objective evolution strategy
MOEA Multiple objective evolutionary algorithm
MOGLS Multiple objective genetic local search
MOGA Multiple objective genetic algorithm
MOGGA Multiple objective grouping genetic algorithm
MOP Multiple objective problem
MPAL Multi product assembly line
MWkCALB Multiple workcentres ALBP
NPGA Niched pareto genetic algorithm
NSGA Non-dominated sorting genetic algorithm
OGA Ordering genetic algorithm
OMT Operating modes and techniques
OV Ordering variants
OX Order crossover
PBX Position based crossover
PG Precedence graph
PL Physical layout
PMX Partially mapped crossover
PROMETHEE Preference ranking organisation Method
for Enrichment evaluations
PSGA Problem space genetic algorithm
RD-MOGLS Random directions multiple objective genetic local search
RP Resource planning
RPW Ranked positional weight
RRPW Reversed ranked positional weight
RWS Roulette wheel selection
SA Simulated annealing
SALBP Simple ALBP
SMCT Scheduling method choice tool
SPAL Simple assembly line balancing
SPEA Strength pareto evolutionary algorithm
ST Station time
SX Smoothness index (SX)
TALB Tree assembly line balancing
TS Tabu search
TVR Time variability ratio
VEGA Schaffer’s vector evaluated GA
Index
AL, see Assembly lines
Artificial intelligence, 49
Assembly lines, 3–6, 10, 13, 15–21, 25,
26, 44, 71, 73, 79, 90, 95, 97, 105,
106, 121, 122, 147
Assembly lines balancing, 4, 6, 13, 16,
19, 20, 25, 26, 48, 59, 62, 121
Assembly lines design, 3, 9, 10, 25
B&C, see Branch and cut
Balance for operation, 6, 9, 93, 99, 121,
122
Batch, 62, 97, 110, 145
Batch production, 11, 20
BF, 62
BFO, see Balance for operation
Bin packing problem, 27, 62
Boundary stones algorithms, 65, 66, 68,
145
BPP, see Bin packing problem
Branch and bound, 59, 60
Branch and cut, 48, 85–87
Branching on population, 49
Capacity supply, 17
Cellular manufacturing, 105
Clustering, 67, 105, 107, 108, 110, 111
Combinatorial optimisation problems,
6, 60
Computer-aided design, 10, 145, 146
Concurrent engineering, 4, 7, 8, 100
Constraint, 23, 24, 60, 85, 87, 90, 110
Cost function, 69, 74, 115
Crossover, 32, 33, 35–38, 44, 45, 67, 96
Cycle time, 67–70, 73, 74, 79, 86, 88, 92,
98–100, 102, 107, 110, 113, 114,
116, 118, 121, 123, 131, 133, 141
Decision maker, 39, 46, 51, 135
Design for assembly, 3
Deterministic time, 20
DFA, see Design for assembly
Diversity, 35, 37, 38, 41, 42, 56
DP, see Dynamic programming
Dynamic programming, 59, 60
Dynamic time, 21
Elitist model, 35
Equal piles, 6, 26, 48, 59, 62, 63, 78, 85,
116, 126
Evolutionary strategies, 41
Exact methods, 59
Feasibility, 34, 78
FF, 62
FFD, see First fit descending
FG, see Functional groups
First fit descending, 62, 63
Fixed operations on stations, 23
Functional groups, 79, 81, 82
GA, see Genetic algorithms
Genetic algorithms, 34, 36, 70, 78, 95,
148
Genetic operator, 34, 36, 38
Graph search, 61
Group technology, 15, 105
Grouping, 72, 111, 145
159160 Index
Grouping GA, 62–64, 78, 91, 92, 145
Grouping genetic algorithm, 45
HAL, see Hybrid assembly line
Heuristics, 61, 78, 89, 95, 97, 115, 124,
145
Hidden time, 21, 80, 81, 84, 91
Hybrid AL, 79
Hybrid assembly line, 4, 89
ICA, 48, see Individual construction
algorithm
Idle time, 18, 59, 89, 97, 106
Imbalance, 17, 74
Individual construction algorithm, 49,
85
Input data, 73, 88, 115, 123, 146
Integrated approach, 105, 106, 121
Inversion, 36, 37, 44, 46, 96
JIT, 21
Just in time, 21
Line configuration, 21, 61
Line efficiency, 18, 26, 59
Linear programming, 60
Local search, 40, 42, 43
Logical layout, 105, 108, 111, 121, 124,
126
Logical line layout, 4
Max peak time, 79, 100, 102
MCDA, see Multi criteria decision aid
Metaheuristic, 61, 62, 69
Mixed production, 11, 146
Model launching, 93, 94
MOGA, see Multiple objective GA
Multi criteria decision aid, 39, 40,
46–48, 74, 86, 148
Multi objectives, 71, 132, 145, 148
Multiple objective GA, 39, 42, 43, 46
Mutation, 32, 35, 36, 44, 48, 56, 67, 96
Natural evolution, 32, 33
OGA, 93, 95
OptiLine, 6, 138, 141
Optimisation, 60, 78, 105, 108, 111, 123,
124, 133, 135
Order crossover, 96
Ordering variants, 25, 26, 93–96
OV, see Ordering variants
Parallel lines, 21
Parallel stations, 4, 21
Partially mapped crossover, 96
Physical layout, 4, 6, 10, 25, 83, 105,
107, 124, 126
Population, 32, 35, 38, 41, 42, 46, 47,
49, 50, 64, 67, 72, 100
Precedence constraints, 3, 4, 16, 17,
25, 61–63, 67, 68, 78, 79, 84, 89,
92, 100, 108, 110, 115–117, 123,
125–127, 129–131, 142
Precedence graph, 64
Process time, 7, 10, 17, 18, 20, 21, 25,
61, 63, 80, 85, 88, 90, 98, 102, 113,
114, 116, 125, 129, 132, 145
Product analysis, 3, 7
PROMETHEE II, 46, 48, 49, 72, 74, 85,
86, 88, 90, 145
Resource planning, 4, 6, 8, 25, 26, 48,
90, 121, 124, 128
Robot, 3, 4, 16, 20, 24, 97, 124
RP, see Resource planning
Scheduling, 9, 20, 93, 94, 99, 142, 145
Serial lines, 21
Simulated annealing, 37
Single product line, 19
Smoothness index, 19
Stochastic time, 20
Stochastic universal sampling, 36
Tabu search, 37
TALB, 95
Throughput, 3, 18, 20, 63, 93, 110
Time interval, 18
Time variability ratio, 18
Variant, 9, 11, 19, 72, 79, 90, 94, 107,
110
Vector evaluated genetic algorithm, 41
VEGA, see Vector evaluated genetic
algorithm, 43
Work content, 18
Workcentre
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