كتاب Design of Advanced Manufacturing Systems
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 كتاب Design of Advanced Manufacturing Systems

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عدد المساهمات : 16344
التقييم : 28228
تاريخ التسجيل : 01/07/2009
العمر : 32
الدولة : مصر
العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
الجامعة : المنوفية

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مُساهمةموضوع: كتاب Design of Advanced Manufacturing Systems    كتاب Design of Advanced Manufacturing Systems  Emptyالسبت 14 نوفمبر 2020, 4:45 pm

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Design of Advanced Manufacturing Systems
Models for Capacity Planning in Advanced Manufacturing Systems
Andrea Matta and Quirico Semeraro
Editors  

كتاب Design of Advanced Manufacturing Systems  D_o_a_11
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Contents
List of Figures vii
List of Tables xii
Preface xiii
Contributing Authors xv
1
A framework for long term capacity decisions in AMSs 1
A. Matta, Q. Semeraro, T. Tolio
1 Introduction 1
2 Manufacturing capacity 3
3 Manufacturing strategy 5
4 Advanced Manufacturing Systems 10
5 A framework for capacity problems 13
2A
DSS for strategic planning 37
M. Bruccoleri, G. Lo Nigro, S. Noto La Diega, P. Renna, G. Perrone
1 The strategic planning process 40
2 Models for Production Strategy Planning 44
3 Models for Long-term Capacity Planning 49
4 DSS description 55
5 Tests and results 58
6 Conclusions 67
3S
tochastic programming models for manufacturing applications 73
A. Alfieri, P. Brandimarte
1 Introduction 73
2 The newsvendor problem 75
3 Stochastic linear programming 78
4 General structure of two-stage stochastic linear programs 86
5 Solution methods 88
6 Multi-stage stochastic programming models 91
7 Strong mixed-integer model formulations 98
8 Scenario generation 103
vvi DESIGN OF ADVANCED MANUFACTURING SYSTEMS
9 Models for capacity planning 111
10 An alternative approach to cope with uncertainty: robust optimization 116
11 Conclusions 119
4
Configuration of AMSs 125
A. Matta, Q. Semeraro, T. Tolio
1 Introduction 125
2 Problem description 126
3 Description of Automated Manufacturing Systems 129
4 Design of Automated Manufacturing Systems 136
5 Performance evaluation of Dedicated Manufacturing Flow Lines
145
6 Performance evaluation of Flexible Manufacturing Systems 157
7 Conclusions 173
5
Selecting capacity plan 191
A. Anglani, P. Caricato, A. Grieco, F. Nucci
1 Introduction 192
2 Problem statement 193
3 The proposed methodology 200
4 Case study 220
5 Conclusions 230
6F
uzzy performance evaluator of AMSs 233
F. Caiazzo, R. Pasquino, V. Sergi, B. Spiezio
1 Introduction 233
2 Fuzzy sets and fuzzy numbers 235
3 Describing uncertainty 242
4 Linguistic modifiers 243
5 Constructing fuzzy sets 247
6 Queuing systems 249
7 Open queuing network models 252
8 Closed queuing network models 253
9 The method proposed: single-class case 258
10 The method proposed: multi-class case 260
11 The algorithm for the method proposed: single-class case 261
12 A sample application 264
13 Conclusions 265List of Figures
1.1 Links of manufacturing strategy with environment,
competitive strategy and performance. 6
1.2 Timing of capacity: lead or follow the market demand ? 9
1.3 Example of the capacity problem solution. 16
1.4 A0 context diagram. 16
1.5 A0 level diagram. 32
1.6 A1 context diagram. 33
1.7 A2 context diagram. 33
1.8 Example of internal capacity input for each AMS. 34
1.9 Example of graph of feasible alternatives. 34
1.10 A3 context diagram. 34
1.11 A4 context diagram. 35
2.1 A1 level diagram: the strategic planning process. 40
2.2 Inputs and outputs of the model [A1-1]-M1. 45
2.3 Membership functions for the values low”, medium”,
and high”. 46
2.4 Inputs and outputs of the four fuzzy systems of the
model [A1-1]-M1. 47
2.5 Inputs and outputs of the models [A1-1]-M2. 48
2.6 Inputs and outputs of the two fuzzy systems of the
model [A1-1]-M2. 48
2.7 The initial menu form. 56
2.8 The form Models for project mapping. 57
2.9 The form for outsourcing constraint determination. 58
2.10 The form for flexibility identification. 59
2.11 The form for the strategic evaluation of product flexibility. 60
2.12 Form for the strategic evaluation of the manufacturing capacity. 61
2.13 The form of the optimization results. 62
vii

“viii DESIGN OF ADVANCED MANUFACTURING SYSTEMS
2.14 A sketch of the output txt file. 63
2.15 Scenario 1 Output. 64
2.16 Scenario 2 Output. 71
3.1 Scenario tree for a two-stage problem. 80
3.2 Convexity of the recourse function and approximation by support hyperplanes. 90
3.3 Scenario tree for multi-stage stochastic programming. 93
3.4 Split-variable view of an event tree. 95
3.5 Graphical interpretation of plant location formulation. 100
4.1 A3 context diagram. 127
4.2 Example of the output of A3. 128
4.3 Flow lines. 130
4.4 Lay-out of FMS. 134
4.5 A3 level diagram. 138
4.6 Example of unreachable node. 145
4.7 Example of unleaveble node. 145
4.8 Decomposition method of a flow line with 5 machines. 146
4.9 Flow line with K machines. 147
4.10 Two-machine line. 150
4.11 Queueing network of modelled FMS. 158
4.12 Multiple-class server in isolation. 162
4.13 Decomposition of a multiple-class server in isolation mode. 163
4.14 Aggregation of customers in one class. 164
4.15 Product mix 1: average value of class throughput
as a function of N with t1 = 500s. 170
4.16 Product mix 1: average value of class throughput
as a function of N with t1 = 1750s. 177
4.17 Product mix 1: average value of class throughput
as a function of N with t1 = 3500s. 178
4.18 Product mix 1: average value of equivalent throughput as a function of N for different values of t1. 179
4.19 Product mix 1: percentage errors on equivalent throughput as a function of N for several values of t1. 180
4.20 Product mix 2: average PIPPO value of equivalent
throughput with 2 machines as a function of N for
different values of t1. 181
4.21 Product mix 2: percentage errors on equivalent throughput with 2 machines as a function of t1 for different
values of N. 182List of Figures ix
4.22 Product mix 2: average value of equivalent throughput with 4 machines as a function of N for different
values of t1. 183
4.23 Product mix 2: percentage errors on equivalent throughput with 4 machines as a function of N for different
values of t1. 184
4.24 Product mix 2: average value of equivalent throughput with 6 machines as a function of N for different
values of t1. 185
4.25 Product mix 2: percentage errors on equivalent throughput with 6 machines as a function of N for different
values of t1. 186
4.26 Real case with two-machines: average value of equivalent throughput and relative error as a function of
additional workload. 187
4.27 Real case with three-machines: average value of
equivalent throughput and relative error as a function of additional workload. 188
4.28 Real case with four-machines: average value of equivalent throughput and relative error as a function of
additional workload. 189
5.1 A4 level diagram. 197
5.2 Feasible transitions. 206
5.3 System evolution graph in sub-periods. 208
5.4 Fuzzy profits comparing. 209
5.5 Pareto’s dominance. 211
5.6 Example: system evolution as modeled by the proposed Fuzzy-DEVS enhancement. 213
5.7 Example: “No-reaction” gathering charts. 214
5.8 Example: “Very reactive” gathering charts. 215
5.9 Example: cost EEM charts detail. 217
5.10 Example: profit EEM charts detail. 217
5.11 Dominance analysis diagram. 219
5.12 Complete set of the solution graph. 224
5.13 Dominant solutions. 224
6.1 Representation of a fuzzy number in line with A.
Irion’s proposal. 241
6.2 Structure of a linguistic variable. 243
6.3 The triangular fuzzy number for the phrase “processing takes about 3 minutes”. 244x DESIGN OF ADVANCED MANUFACTURING SYSTEMS
6.4 The trapezoidal fuzzy number for the phrase “interarrival time takes between 3 and 5 minutes”. 244
6.5 Applying the modifiers “quite” and “enough” to a
fuzzy set. 246
6.6 The variation in truth value produced by applying
the modifier “quite” and enough to the fuzzy set
“short processing time”. 247
6.7 Configuration of a client-server system. 250
6.8 The birth/death process. 251
6.9 Model of a typical totally interconnected closed queuing network with four nodes. 254
6.10 Fuzzy sets reflecting lead time and average number
of tasks in the system. 256
6.11 Fuzzy sets reflecting average number and lead time
of tasks within the system. 258
6.12 Configuration of a queuing system with one reiteration. 264List of Tables
3.1 Expected newsvendor’s profit as a function of order
quantity Q. 77
3.2 Bill of material for the ATO example. 81
3.3 Process plan, available capacity, and component cost
for the ATO example. 81
3.4 Demand scenarios, average demand, sale price. 82
4.1 Simulation vs analytical methods. 142
4.2 DMFL Real case: description of machines’ failures. 155
4.3 Test case: Product mix 1 with long processing times [s]. 169
4.4 Test case: Product mix 2 with brief processing times [s]. 171
4.5 Real case: part mix data [min]. 172
4.6 Real case: pallet combinations. 173
5.1 Transition Matrix. 202
5.2 Mapping of Fuzzy-DEVS elements within the modeled system 203
5.3 Example of state evolution. 204
5.4 Example: economical and technical parameters. 212
5.5 Example: expansion capacity strategies. 213
5.6 Example: expansion actions. 218
5.7 Example: system state evolution. 219
5.8 Example: dominant solutions. 220
5.9 Number of states for each period. 222
5.10 Dominant solutions. 223
5.11 Performance of dominant solutions. 225
5.12 Final states for the dominant solutions. 225
5.13 Part type characteristics. 226
5.14 Production capacity acquisition for Solution A. 227
5.15 Production capacity acquisition for Solution B. 227
5.16 Production capacity acquisition for Solution C. 228
5.17 Production capacity acquisition for Solution D. 228
xixii DESIGN OF ADVANCED MANUFACTURING SYSTEMS
5.18 Production capacity acquisition for Solution E. 229
5.19 Production capacity acquisition for Solution F. 229
6.1 Arithmetic operations with fuzzy numbers in line
with Irion’s representation method. 241  


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