Robust Scheduling Methodology to Reduce Risk in Aerospace Production Systems

355 Pages·2016·19.55 MB·English

ROBUST SCHEDULING METHODOLOGY TO REDUCE
RISK IN AEROSPACE PRODUCTION SYSTEMS
A Dissertation
Presented to
The Academic Faculty
by
Dennis J.L. Siedlak
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy in the
School of Aerospace Engineering
Georgia Institute of Technology
December 2016
Copyright © 2016 by Dennis J.L. Siedlak
ROBUST SCHEDULING METHODOLOGY TO REDUCE
RISK IN AEROSPACE PRODUCTION SYSTEMS
Approved by:
Professor Dimitri Mavris, Doctor Olivia Pinon-Fischer
Committee Chair School of Aerospace Engineering
School of Aerospace Engineering Georgia Institute of Technology
Georgia Institute of Technology
Professor Daniel Schrage Don Farr
School of Aerospace Engineering Boeing Research & Technology
Georgia Institute of Technology The Boeing Company
Professor Graeme Kennedy Date Approved: 7 November 2016
School of Aerospace Engineering
Georgia Institute of Technology
ACKNOWLEDGEMENTS
I would like to thank the many people who have helped me throughout this disserta-
tion process. First, Prof. Dimitri Mavris, my advisor and thesis committee chairman.
Thank you for bringing me to ASDL and giving me the freedom and opportunity to
explore this dissertation topic. I have truly learned a great deal throughout this ex-
perience. Further, I would like to thank the rest of my thesis committee, Prof. Daniel
Schrage, Prof. Graeme Kennedy, Dr. Olivia Pinon-Fischer, and Don Farr, for their
input and support during the process.
To Olivia especially, thank you for the support and encouragement throughout.
You helped me so much to formulate my ideas and, most importantly, graduate in a
reasonable amount of time. Thank you for reading everything when it was not very
intelligible and helping to make this document somewhat readable. Also, to Don,
thank you for supporting the project that led to this thesis and providing guidance
throughout. I would also like to thank everyone else within Boeing who supported
the project, especially Phil and Scott.
I would also be remiss without thanking my family for the support and encourage-
ment throughout these last 4 years. Particularly, thanks to my parents for enabling
me to pursue my doctorate while also spurring me to finish. Also, to my sister, for
graduating from law school in May 2017 and pushing me to get a job before her. It
was nice to know that I had support but also a clear goal.
Thanks to my friends at Georgia Tech. I enjoyed meeting everyone and having
people to bounce ideas off of. I’m also glad we were able to talk about things other
than Aerospace (I was a bit worried after quals). Specifically, thanks to Seth for being
my roommate (and fellow Pittsburgh fan) the past 3 years. I truly value everyone’s
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friendship and hope to continue being friends throughout our lives.
Ultimately, I have enjoyed many parts of this PhD process, and I am excited to
move forward with the experiences and friendships that I have gained. Thank you
again to everyone who has supported me in one way or another, and I look forward
to seeing and taking part in what everyone accomplishes!
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
NOMENCLATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx
I INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Lack of Schedule Robustness . . . . . . . . . . . . . . . . . . . . . . 5
1.1.1 Current Scheduling Strategy Overview . . . . . . . . . . . . . 7
1.1.2 ChallengestoIntegratingRobustDesignPrincipleswithSchedul-
ing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
II PROBLEM DEFINITION . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Review of Scheduling Strategies . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Static, Deterministic Scheduling Strategies . . . . . . . . . . 15
2.2.2 Non-Deterministic Scheduling Strategies . . . . . . . . . . . . 23
2.2.3 Discussion and Summary . . . . . . . . . . . . . . . . . . . . 36
2.3 Techniques for Modeling Complex Systems . . . . . . . . . . . . . . 41
2.3.1 Discrete-Event Simulation . . . . . . . . . . . . . . . . . . . 42
2.3.2 Agent-based Modeling . . . . . . . . . . . . . . . . . . . . . . 43
2.3.3 System Dynamics . . . . . . . . . . . . . . . . . . . . . . . . 44
2.3.4 ComparisonofDiscrete-eventSimulationandAgent-basedMod-
eling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.4 Overview of Discrete-Event Simulation . . . . . . . . . . . . . . . . 47
2.4.1 Common Discrete-event Simulation Implementation Process . 48
2.4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
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2.5 Implementation Challenges . . . . . . . . . . . . . . . . . . . . . . . 58
2.5.1 Methodology Implementation Time Reduction . . . . . . . . 61
2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.7 Gap Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.8 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
2.9 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
III PROPOSED METHODOLOGY . . . . . . . . . . . . . . . . . . . . 75
3.1 PORRTSS Methodology Overview . . . . . . . . . . . . . . . . . . . 75
3.1.1 Step 1: Model Generation . . . . . . . . . . . . . . . . . . . . 75
3.1.2 Step 2: Simulation-based Optimization . . . . . . . . . . . . 77
3.1.3 Step 3: Schedule Down-Selection & Modification . . . . . . . 78
3.1.4 Step 4: Schedule Propagation & System-wide Analysis . . . . 78
3.2 Case Study Description . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.2.1 Establish Need . . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.2.2 Define the Problem . . . . . . . . . . . . . . . . . . . . . . . 81
3.2.3 Establish Value . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.2.4 Generate Feasible Alternatives . . . . . . . . . . . . . . . . . 83
3.2.5 Evaluate Alternatives . . . . . . . . . . . . . . . . . . . . . . 84
3.2.6 Make Decisions . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.3 Current vs. Proposed Planning Process . . . . . . . . . . . . . . . . 88
3.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
IV IMPLEMENTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.1 Implementation Stakeholders . . . . . . . . . . . . . . . . . . . . . . 98
4.2 Step 1: Model Generation . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2.1 Software Implementation . . . . . . . . . . . . . . . . . . . . 110
4.3 Step 2: Simulation-based Optimization . . . . . . . . . . . . . . . . 111
4.3.1 Production Logic . . . . . . . . . . . . . . . . . . . . . . . . 112
4.3.2 Compatibility Matrix Generation . . . . . . . . . . . . . . . . 115
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4.3.3 Optimization Routine Requirements . . . . . . . . . . . . . . 116
4.3.4 Simulation-based Optimization Implementation . . . . . . . . 118
4.3.5 Optimization Computing Budget Allocation . . . . . . . . . 130
4.3.6 Simulation-based Optimization Component Integration . . . 134
4.4 Step 3: Sensor Installation Schedule Down-Selection . . . . . . . . . 134
4.4.1 Visualization Development . . . . . . . . . . . . . . . . . . . 137
4.5 Step 4: Schedule Propagations & System-wide Analysis . . . . . . . 146
4.6 Experimental Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
4.6.1 Experiment 1.1: Effectiveness of Discrete-event Simulation to
Model the Production System of Interest . . . . . . . . . . . 146
4.6.2 Experiment 1.2: Determine Optimization Strategy’s Ability
to Improve System Performance . . . . . . . . . . . . . . . . 148
4.6.3 Experiment 1.3: Impact of Robust Optimization Considerations149
4.6.4 Experiment 1: Methodology’s Capability to Reduce Risk in
Production Schedules . . . . . . . . . . . . . . . . . . . . . . 150
4.6.5 Experiment 2.1: Modeling Speed Improvements . . . . . . . . 151
4.6.6 Experiment 2.2: Improve Optimization Convergence Quality
and Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
4.6.7 Experiment2: Methodology’sPotentialforImplementationto
Handle “Real-World” Problems of Interest . . . . . . . . . . 161
4.6.8 Experimental Plan Summary . . . . . . . . . . . . . . . . . . 163
V EXPERIMENTATION & RESULTS . . . . . . . . . . . . . . . . . . 164
5.1 Experiment 1: Methodology’s Capability to Reduce Risk in Produc-
tion Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
5.1.1 Experiment 1.1: Effectiveness of Discrete-event Simulation in
Modeling Production Systems of Interest . . . . . . . . . . . 166
5.1.2 Experiment 1.2: Optimization’s Ability to Improve System
Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
5.1.3 Experiment 1.3: Impact of Robust Optimization Considera-
tions Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
5.2 Experiment 1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . 214
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5.3 Experiment 2: Methodology’s Potential for Deployment to “Real-
World” Problems of Interest . . . . . . . . . . . . . . . . . . . . . . 215
5.3.1 Experiment 2.1: Methodology Implementation Time Reduction215
5.3.2 Experiment 2.2: Improve Optimization Convergence Quality
and Time Results . . . . . . . . . . . . . . . . . . . . . . . . 224
5.4 Experiment 2: Methodology Implementation . . . . . . . . . . . . . 236
5.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
VI USE CASE & DECISION SUPPORT . . . . . . . . . . . . . . . . . 241
6.1 Compatibility Matrix Development . . . . . . . . . . . . . . . . . . . 242
6.2 Automated Model Generation Deployment . . . . . . . . . . . . . . 243
6.3 Simulation-based Optimization Deployment . . . . . . . . . . . . . . 243
6.4 Sensor Installation Scenario Down-Selection and Manual Modification 245
6.4.1 Initial Down-Selection . . . . . . . . . . . . . . . . . . . . . . 245
6.4.2 Initial Down-Selection Summary . . . . . . . . . . . . . . . . 257
6.4.3 Scenario Comparison . . . . . . . . . . . . . . . . . . . . . . 258
6.4.4 Scenario Modification . . . . . . . . . . . . . . . . . . . . . . 263
6.5 Methodology Implementation in the Industrial Setting . . . . . . . . 270
6.6 Use Case Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
6.6.1 Multi-objective, System-level Impact Assessment . . . . . . . 272
6.6.2 Manual Plan Modification and Assessment . . . . . . . . . . 272
6.6.3 Manpower Requirements Analysis . . . . . . . . . . . . . . . 273
6.7 Experiment 2 Results Discussion . . . . . . . . . . . . . . . . . . . . 273
6.7.1 Methodology Completion within the Required Time Horizon 275
6.7.2 Optimization Results to Support Implementation . . . . . . . 276
6.7.3 Decision Support Environment’s Importance to Successful Im-
plementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
6.8 Experiment 2 Results Review . . . . . . . . . . . . . . . . . . . . . . 279
6.9 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
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VIISUMMARY & CONCLUSIONS . . . . . . . . . . . . . . . . . . . . 282
7.1 Summary of Thesis Objectives . . . . . . . . . . . . . . . . . . . . . 282
7.2 Research Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
7.3 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . 291
7.3.1 Contribution 1: Improved Interaction between Scheduling and
Simulation Practices . . . . . . . . . . . . . . . . . . . . . . . 292
7.3.2 Contribution 2: Improved Interface between Human Sched-
ulers and the Planning Methodology . . . . . . . . . . . . . . 294
7.4 Methodology Limitations . . . . . . . . . . . . . . . . . . . . . . . . 295
7.5 Recommendations for Future Work . . . . . . . . . . . . . . . . . . 297
7.5.1 Compatibility Matrix & Primary Production Flow Generation 297
7.5.2 Schedule Mining to Support Simulation . . . . . . . . . . . . 298
7.5.3 Expanded Investigation of the Robust Simulation-based Op-
timization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
7.5.4 Decision Support Improvements . . . . . . . . . . . . . . . . 301
APPENDIXA —OPTIMIZATIONALGORITHMPSEUDO-CODE
302
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
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LIST OF TABLES
1 Examples of Increased Sensors and Electronics on Current Generation
Aerospace Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Examples of Common Objective Functions for Deterministic Schedul-
ing Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Summary of Schedule Solution Strategies . . . . . . . . . . . . . . . . 39
4 Summary of Modeling Frameworks . . . . . . . . . . . . . . . . . . . 46
5 Examples of the Proposed Methodology’s Benefit Throughout an Or-
ganization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6 Notional Sensor and Process Compatibility Matrix . . . . . . . . . . . 83
7 Overview of Objective Functions . . . . . . . . . . . . . . . . . . . . . 87
8 Classification of Scheduling Levels (Based on Reference [136] and Sup-
plemented by References [47,60,132,165,175]) . . . . . . . . . . . . . 89
9 Schedule Information Used to Generate Simulation Model . . . . . . 103
10 Scheduling Constraints (Reproduced from Reference [59]) . . . . . . . 104
11 Optimization Algorithms to Investigate in Experiment 2.2 . . . . . . 153
12 Schedule Robustness Considerations to Investigate in Experiment 2.2 153
13 Experiment 2.2 Integrated Strategies to Improve Optimization Perfor-
mance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
14 Summary of Concurrent Sensor Installations’ Impact on Process Time 179
15 Overview of Model Sizes and Complexity Modeled Throughout this
Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
16 Summary of Deterministic Down-Selection Results . . . . . . . . . . . 230
17 TOPSIS Algorithm Weightings when Considering Flow Time and Cost 247
18 TOPSIS Algorithm Weightings when Considering Flow Time, Cost,
and the Slack Time for All Sensors . . . . . . . . . . . . . . . . . . . 249
19 TOPSIS Algorithm Weightings when Considering Flow Time, Cost,
and the Slack Time for All and Critical Sensors . . . . . . . . . . . . 251
20 TOPSIS Algorithm Weightings when Considering Flow Time, Cost,
the Neighboring Sensor Installation Metric, and the Slack Time for All
and Critical Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
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