Real Optimization with SAP® APO

B́a trước
Springer Science & Business Media, 2 thg 9, 2006 - 321 trang
Optimization is a serious issue, touching many aspects of our life and activity. But it has not yet been completely absorbed in our culture. In this book the authors point out how relatively young even the word “model” is. On top of that, the concept is rather elusive. How to deal with a technology that ?nds applicationsinthingsasdi?erentaslogistics,robotics,circuitlayout,?nancial deals and tra?c control? Although, during the last decades, we made signi?cant progress, the broad public remained largely unaware of that. The days of John von Neumann, with his vast halls full of people frantically working mechanical calculators are long gone. Things that looked completely impossible in my youth, like solving mixed integer problems are routine by now. All that was not just achieved by ever faster and cheaper computers, but also by serious progress in mathematics. But even in a world that more and more understands that it cannot a?ord to waste resources, optimization remains to a large extent unknown. R It is quite logical and also fortunate that SAP , the leading supplier of enterprise management systems has embedded an optimizer in his software. The authors have very carefully investigated the capabilities and the limits of APO. Remember that optimization is still a work in progress. We do not have the tool that does everything for everybody.
 

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8222 Index Sets and Indicator Tables
148
8223 The Problem Data
149
8224 Time Discretization
151
8225 The Concept of Modes
153
8226 The Variables
154
823 The Mathematical Model the System of Constraints
156
8232 Modeling Modechanging Reactors
157
8233 Multistage Production
162

142 SNP CapabletoMatch
16
143 SNP Optimization
18
Introduction Models Model Building and Optimization
21
22 Mathematical Optimization
23
23 The Main Ingredients of Optimization Models
26
231 Indices and Index Sets
27
232 Data
28
233 Variables
29
234 Constraints
30
235 The Objective Function
31
24 Classes of Problems in Mathematical Optimization
32
2411 Comments on Solution Algorithms
33
2412 Optimization Versus Simulation
35
243 Optimization Under Uncertainty
36
25 Implementing Models and Solving Optimization Problems
38
252 Solving Optimization Problems
39
Model Building in SAP APO Supply Network Planning
41
31 The Example Supply Chain Model
42
312 Constraints and Costs in the Example Model
43
313 A Mathematical Formulation of the Example Model
45
32 Supply Chain Model Master Data
51
322 Locations
53
323 Products and Location Products
55
324 Resources
59
3242 Resource Capacity Variants
61
3251 General PPM Data
62
3252 Components
64
3253 Modes
65
3254 Product Plan Assignment
67
3256 PPM Data in the Example Model
68
326 Assembling the Parts with the Supply Chain Engineer
69
327 Transportation Lanes
72
Optimization in SAP APO
79
42 Optimizer Setup
80
421 The Optimization Profile
81
422 Scaling the Costs in the Master Data the SNP Cost Profile and SNP Cost Maintenance
86
423 Working Towards Steady Results the SNP Optimization Bound Profile
87
424 Lot Sizes for Shipments the SNP Lot Size Profile
88
425 Decomposition Methods and the SNP Priority Profile
89
427 The Time Bucket Profile
90
43 The Planning Run
91
432 Continuous Variant of the Model
93
433 Discrete Variant of the Model
98
44 Some Observations
100
442 The Standard Business Software View
101
Detailed CaseStudies
103
Planning in Semiconductor Manufacturing
105
511 The Manufacturing Process
106
512 Semiconductor Business Challenges
107
52 Supply Chain Business Practices
108
521 Semiconductor Capacity and Master Planning
109
522 Semiconductor Supply Chain Modeling
110
523 Semiconductor Supply Chain Planning and SAP APO
111
5232 Optimization for Semiconductor
113
53 The Semiconductor Case Study
115
532 The Supply Chain Structure
116
533 SNP Implementation in SAP APO
117
Consumer Products
119
62 The Carlsberg Case
120
621 The Carlsberg Business Objectives and Project Scope
121
623 SNP Optimization Implementation in SAP APO
123
Customized Optimization Solutions for the Automotive and Chemical Industries
125
711 The Complete Planning System
126
713 MidRange Budget and Master Production Planning
128
7131 Goals
129
7133 Decomposition
130
7135 User Interface
131
7141 Decomposition
132
7143 Constraints
133
7145 User Interface
134
721 The Architecture of the Complete Planning System
135
722 Production Planning and Detailed Scheduling
136
723 Approximation Methods in SAP APO PPDS
138
7241 Cartridge Planning Scenario
139
7244 SmelterSimple Extruder Model
140
7245 Extruder Model
141
725 Conclusion
142
Operative Planning in the Process Industry
143
82 A Tailored MILP Model
146
821 Basic Assumptions and Limitations of the Model
147
8234 Minimum Production Requirements
164
8235 Batch and Campaign Production
167
8236 Modeling Stock Balances and Inventories
169
8238 Modeling Transport
176
8239 Keeping Track of the Origin of Products
179
82310 Including Demands and Demand Constraints
180
824 Defining the Objective Functions
181
8241 Maximizing Contribution Margin
182
8242 Maximizing Margin Satisfying Demand
185
8245 Maximizing Net Profit
187
8247 Maximizing Total Production
188
8251 Estimating the Quality of the Solution
189
8252 Comparing Solutions of Different Scenarios
190
8253 Description of the Output
191
826 Real Life Issues
193
8262 Seemingly Implausible Results
195
83 The SAP APO View on this Problem
196
831 Nonlinear Costs
197
833 Demand Satisfaction
198
834 Detailed Comments on the Tailored MILP Model
199
8341 Basic Assumptions and Limitations
200
8343 The Mathematical Model the Constraints
201
8344 Description of the Outputs
203
Case Studies Interfacing Tailored Models to SAP APO
205
92 The ILOG Cartridge Concept
206
921 ILOG Background
207
9211 The Optimization Development Framework and Cartridges
208
9222 SAP APO Project
209
9224 Solution Outline
211
9225 Integration
215
923 The Load Builder Case
216
9232 SAP APO Project
218
9234 Solution Outline
219
9235 Integration
222
924 The Cartridge Architecture
223
9241 External Architecture
224
9242 Data Integration
227
925 About Cartridge Projects
228
9252 The Inception Phase
230
9253 Elaboration
233
9255 Transition
234
93 Production and Sales Planning in the Chemical Industry
237
933 Solution
238
9331 Data Download from SAP R3 and SAP APO
239
9332 Checks on Completeness and Consistency
240
9334 Optimization
241
9336 Reporting
242
9341 The Human Factor
243
935 Future Enhancements
245
9352 Task and Objectives
246
Concluding Considerations The Future
250
Summary Visions and Perspective
253
102 A Summary of Experience in Optimization Projects
255
1022 Data and Optimization Model
261
1023 Rules in Planning and Scheduling Problems
262
1024 Implementation
263
1025 Interfacing Tailored Models
265
1031 Simultaneous Operative and Strategic Optimization
266
1032 Rigorous Approaches to Scheduling
267
1033 Planning and Scheduling Under Uncertainty
268
1035 SAP APO as a Modeling Tool
269
Appendix
271
The Hitchhikers Guide to SAP APO
272
A2 Hierarchical Planning
274
Mathematical Foundations of Optimization
277
B11 A Primal Simplex Algorithm
279
B12 Computing Initial Feasible LP Solutions
283
B13 LP Problems with Upper Bounds
284
B14 Dual Simplex Algorithm
286
B15 Interiorpoint Methods
287
B2 Mixed Integer Linear Programming
290
B3 Multicriteria Optimization and Goal Programming
294
Glossary
297
List of Figures
303
List of Tables
305
References
307
About the Authors
313
Index
315
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