An Operations Research Approach to Aviation Security by Susan Elizabeth Martonosi B.S., Cornell University, 1999 Submitted to the Sloan School of Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Operations Research at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2005 ( Massachusetts Institute of Technology 2005. All rights reserved. Author ................................ _is- Sloan School......... of Management......- 7> 11 Augut) 2005 Certified by ............ ................................................... Arnold I. Barnett George Eastman Professor of Management Science Thesis Supervisor Acceptedby.... James B. Orlin MASSACHUSETTS INSTITUTE ' Edward Pennell Brooks Professor of Operations Research OF TECHNOLOGY Co-Director, MIT Operations Research Center SEP 1 4 2005 LIBRARIES .~. 'f An Operations Research Approach to Aviation Security by Susan Elizabeth Martonosi Submitted to the Sloan School of Management on 11 August 2005, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Operations Research Abstract Since the terrorist attacks of September 11, 2001, aviation security policy has remained a focus of national attention. We develop mathematical models to address some prominent problems in aviation security. We explore first whether securing aviation deserves priority over other potential targets. We compare the historical risk of aviation terrorism to that posed by other forms of terrorism and conclude that the focus on aviation might be warranted. Secondly, we address the usefulness of passenger pre-screening systems to select poten- tially high-risk passengers for additional scrutiny. We model the probability that a terrorist boards an aircraft with weapons, incorporating deterrence effects and potential loopholes. We find that despite the emphasis on the pre-screening system, of greater importance is the effectiveness of the underlying screening process. Moreover, the existence of certain loopholes could occasionally decrease the overall chance of a successful terrorist attack. Next, we discuss whether proposed explosives detection policies for cargo, airmail and checked luggage carried on passenger aircraft are cost-effective. We define a threshold time such that if an attempted attack is likely to occur before this time, it is cost-effective to implement the policy, otherwise not. We find that although these three policies protect against similar types of attacks, their cost-effectiveness varies considerably. Lastly, we explore whether dynamically assigning security screeners at various airport se- curity checkpoints can yield major gains in efficiency. We use approximate dynamic program- ming methods to determine when security screeners should be switched between checkpoints in an airport to accommodate stochastic queue imbalances. We compare the performance of such dynamic allocations to that of pre-scheduled allocations. We find that unless the stochasticity in the system is significant, dynamically reallocating servers might reduce only marginally the average waiting time. Without knowing certain parameter values or understanding terrorist behavior, it can be difficult to draw concrete conclusions about aviation security policies. Nevertheless, these mathematical models can guide policy-makers in adopting security measures, by helping to identify parameters most crucial to the effectivenessof aviation security policies, and helping to analyze how varying key parameters or assumptions can affect strategic planning. 3 Thesis Supervisor: Arnold I. Barnett Title: George Eastman Professor of Management Science 4 Acknowledgments This thesis is the product not just of my own work but also that of many who have helped me along the way. First, I am indebted to my thesis advisor, Arnold Barnett, for the wisdom and patience he has exhibited with me. His constant reminders to think simply when modeling a problem helped me out of many ruts, and I hope to remember this wise rule throughout my career. A. talented writer and orator, he offered many useful suggestions and criticisms on the writing and presentation of this thesis, and this instruction will serve me well into the future. I also admire his commitment to problems of social importance. It was indeed this commitment that led me to work with him, and his emphasis on selecting methodology to suit the problem rather than the reverse has inspired me to continue in this domain. Professors Cynthia Barnhart and Amedeo Odoni, who served on my thesis committee, have offered support and guidance since well before the official formation of the committee. In particular, their suggestions helped to clarify the focus of the problem posed in Chapter 5 and provided fruitful directions of exploration. Their kindness and encouragement throughout my time here will remain a lasting memory for me. I am grateful for the generous support of the Alfred P. Sloan Foundation, via the MIT Global Airline Industry Program, who funded this work. Anthony Ventresca of Massport was also of great assistance in sharing data and expertise that were invaluable to the research in Chapter 5. The Operations Research Center has become a second home to me, thanks to the efforts of the co-directors (Jim Orlin and John Tsitsiklis) and the administrative staff (Paulette Mosley, Laura Rose, Veronica Mignot and Andrew Carvalho) to make it a welcoming environment in which to work:. I thank my fellow students for conversations, camaraderie, friendship, and, most of all, karaoke. A special thanks to those in my incoming class with whom I completed my coursework and studied for the qualifying exams. I learned so much by working with you. Outside of the ORC, I wish to thank Rambax MIT for being my African oasis here in Boston, and my students in Koundara, Guinea for inspiring me with their determination. Lastly, none of this would have been possible without the encouragement, unshakable confidence, and love of my family and God. It is the latter of these gifts for which I am most grateful. 5 6 Contents 1 Introduction 15 2 The Relative Risk of Aviation Terrorism 21 2.1 A relative risk factor. ............ 22 2.2 Terrorism against Americans, 1968-2001 . 23 2.2.1 Definition of terrorism. 23 2.2.2 Classification of terrorist attacks . .23 2.3 Fraction of time spent in aviation ............ 24 2.3.1 Estimation based on passenger enplanements . 25 2.3.2 Estimation based on time-use studies ...... 26 2.4 Death risk per hour. 26 2.5 Using the past to predict the future ........... .27 3 How Effective Might Passenger Profiling Be? 29 3.1 System and parameter description ............ .31 3 1 3.2 Opposing viewpoints ............ ...... 34 3.2.1 The "right answer" to security?. ,.. ... ... o 34 3.2.2 A carnival game? . 35 3.3 A general model . ... 36 3.3.1 How terrorists might probe the system ..... ... 36 3.3.2 Modeling the probability of a successful attack 37 3.3.3 Updating conditional probabilities of selection . 39 3.4 Analysis techniques. 42 3.5 Interpreting the model . 44 3.5.1 Effects of screening effectiveness parameters on attack success .... 44 3.5.2 Deterrence effects. 49 3.5.3 Role of random screening ......................... 54 3.6 Conclusions and policy implications. 55 4 Securing Cargo Holds of Passenger Aircraft: A Cost-Benefit Perspective 57 4.1 Three security measures. 58 4.1.1 Positive Passenger-Bag Match. 58 4.1.2 Moratorium on larger Postal Service airmail packages ......... 59 7 .. 60 4.1.3 A hypothetical policy on cargo .......... ... ..... 4.2 A cost-benefit model .................... ... ..... 60 4.2.1 Parameters and constants . ... ..... .. 61 4.2.2 Model based on cost per flight ........... ... ..... .. 62 4.2.3 A renewal model .................. ... ..... .. 62 4.2.4 Interpretation .............. ... ..... .. 64 4.3 Parameter Estimation. ... ..... .. 66 4.3.1 The cost of attack .................. ... ..... .. 66 4.3.2 Policy costs. ... ..... .. 67 4.3.3 Backup security effectiveness ............ ... ..... .. 69 4.3.4 Backup security costs ................ ... ..... .. 71 4.4 Comparing the cost-effectiveness of cargo-hold measures ... ..... .. 71 4.5 Diversion of threats ..................... ... ..... .. 76 4.6 Conclusion . ......................... ... ..... .. 78 5 Dynamic Security Screener Allocation 79 5.1 Problem description . .......... 80 5.2 Literature review. .......... 81 5.3 Problem formulations . .......... 85 5.3.1 Deterministic fluid model ............... .......... 89 5.3.2 Deterministic disruptions to passenger entry pattern .......... 95 5.3.3 Stochastic disruptions to passenger entry pattern .......... 97 5.3.4 Randomized entry rates ............... .......... 98 5.3.5 Stochastic service times .......... 99 5.4 Approximate dynamic programming solution techniques . .......... 99 5.4.1 Aggregated state space with limited lookahead . .......... 100 5.4.2 Approximating expected values ........... .......... 101 5.5 Data .............................. .......... 103 5.6 Results. ............................ .......... 106 5.6.1 Deterministic entries and services .......... .......... 106 5.6.2 Deterministically disrupted entries ......... .......... 110 5.6.3 Stochastically disrupted entries ........... .......... 112 5.6.4 Randomized entry rates ............... .......... 115 5.6.5 Stochastic service times ............... .......... 116 5.6.6 General observations. .......... 117 5.6.7 Sensitivity analysis .................. .......... 123 5.7 Model limitations. .......... 127 5.8 Are dynamic