the logistics of hosting big events like the Democratic National Convention

The Democratic National Convention will be in Milwaukee next year. Since the announcement, journalists and experts have noted Milwaukee’s lack of hotel rooms for this event, with hotel reservations already stretching south to Chicago. Madison is about 75 miles away and might be completely booked for the DNC. The Milwaukee Journal Sentinel published an article about the logistical mess.

The DNC is similar to other large events hosted by cities.

Jacksonville, Florida hosted the 2005 Super Bowl. Jacksonville (population 892,000) was the smallest city ever to host the Super Bowl. It did not meet the National Football League (NFL) rule of having a single hotel with at least 750 rooms near the stadium. Predictably, hotel and travel logistics were a nightmare. Jacksonville made up for the lack of hotel rooms with seven cruise ships, which provided about 3,700 of the 17,500 4 or 5 star hotel rooms the NFL requires for Super Bowl week. The city in total had 35,000 rooms for Super Bowl week.

Milwaukee is on Lake Michigan, which probably does not have access to the same large cruise ships. While Milwaukee isn’t Jacksonville, perhaps temporary housing (on boats and Air BnB) could make the hotel shortage more manageable.

What are other ways cities have managed the logistics of large events?

 


Today I am officially a full professor!

Today I am officially a full professor! I’ve had a wonderful journey as a professor. Below I pasted my research statement that summarizes all of the research I’ve done since my PhD. I thought this was worth sharing with people other than my colleagues on the promotions committee.

Research statement

My research applies operations research methodologies to important societal applications.  My primary methodological base is discrete optimization, including integer programming and Markov decision processes (MDPs).  I have applied operations research methodologies to address public sector problems related to emergency medical services, homeland security and infrastructure protection, and disaster response and recovery.

The public sector applications I have studied are complex systems that span people, processes, vehicles, and critical infrastructure. My research has studied how to cost-effectively allocate resources, decisions that are not made in isolation. Rather, decisions are interrelated, with every decision potentially affecting every other decision due to congestion, processing delays, capacities, and uncertainty about what can happen next. While many papers in the literature apply operations research methods to public sector problems, these papers have limitations. First, research models make simplifying assumptions that limit the applicability of the research to real systems. Second, the papers in the literature have not been adequate for addressing problems in emerging areas, such as in homeland security, infrastructure protection, and disaster management, which have become important issues of national concern in recent years.

To overcome these limitations, I have identified problems that are important from a societal point of view and interesting from a methodological point of view. My approach has been to either lift simplifying assumptions made in the literature to broaden the scope of applicability and/or formulate new models to represent problems not yet considered in the literature. Moreover, I have worked with stakeholders to gain domain expertise and have used real-world data whenever possible to ensure that the most salient aspects of the problems are reflected in my models and analysis.

My research has made several substantive and technical contributions to operations research and public sector applications. I have identified important public sector problems that have not been addressed in the literature and can be modeled using operations research techniques, thereby expanding the operations research discipline to encompass new applications that motivate new models and solution techniques. I typically formulate a new optimization model for each problem at hand, drawing on methods and ideas relevant to the application, since one generic model cannot be meaningfully applied to many settings. I have employed a broad set of methods, drawing from integer programming, stochastic programming, network optimization, network interdiction, MDPs, and queueing, since a broad methodological toolkit is essential for solving real problems. I have analyzed each of the models to provide insights about a particular problem, facilitate numerical implementation, and identify exact and heuristic algorithmic approaches. Finally, I solve the models, using real-world data or representative data sets.

My emergency medical service (EMS) research dates to 2007 and focuses on how to enhance emergency healthcare by deploying and routing multiple types of vehicles to emergency calls for service. I formed a community partnership with local fire and EMS departments, and these collaborations provided the motivation and domain knowledge for my research. While there exist many models for EMS planning in the literature, most models assume a uniform fleet of homogenous ambulances and do not consider prioritized patients. My research has lifted these assumptions to design risk-based planning and response protocols using a heterogeneous set of vehicles to and to gain policy insights. My research has led to an analysis of performance measures to support patient outcomes and survivability, new models for informing ambulance deployment and routing decisions, and new insights for balancing multiple criteria in making these decisions. My research on deploying non-transport vehicles to emergency healthcare patients was the first of its kind, which led to important policy insights for public safety leaders. This research was put into practice by Hanover County Fire & EMS in Virginia. I was part of the Hanover County team that was awarded a National Association of Counties (NACo) award in 2010:  Best of Category for the Achievement Award in the Next-Generation Emergency Medical Response through Data Analysis & Planning based on the substantial improvement my research models made in a real system. My 2012 paper in the management journal Interfaces reports the impact of this research observed in practice.

Since tenure, my research has focused on fire and EMS planning problems in tiered systems with multiple types of vehicles. There is not an analytical framework to support making decisions in tiered fire and EMS systems that send a mixture of vehicles to prioritized patients, and my research has focused on filling this important knowledge gap. My research group has formulated new MDP, integer programming, and approximate queueing models to study several important issues, such as how to cost-effectively route ambulances to patients with uncertain medical conditions, how to make joint deployment and routing decisions instead of assuming that these two interrelated decisions are separate, how to deploy a heterogeneous set of vehicles on a network for responding to prioritized calls for service, and how to design new risk-based response paradigms in congested networks. In this stream of research, I produced a suite of new models to support the design and operation of fire and EMS systems. My research group developed the first queueing model to evaluate the performance of fire departments when multiple vehicles respond to calls for service. This past year, this model was incorporated into the software used to route fire engines to calls in Canada.

My homeland security and infrastructure protection research dates to 2001. My early research was in the area of aviation security, where I developed and analyzed new integer programming and MDP models to assess the viability of risk-based prescreening systems.  The common theme that pervades this research is the novel formulation and application of dynamic optimization models to capture and improve the operation of aviation security systems.  The approach uses real-time assignment policies to adapt to variations in the day-to-day airport threat environment. My research provided policy insights regarding risk-based aviation security, served as the model for the TSA PreCheck paradigm, and provided the technical validation that helped facilitate its launch by the TSA. For my pivotal role in the creation and widespread adoption of risk-based aviation security strategies, my collaborators and I were awarded the INFORMS Impact Prize in 2018.

My research in this area took a new direction into cybersecurity after tenure. I recognized an opportunity to apply operations research methods to supply chain risk management (SCRM) to protect critical information technology infrastructure and enhance cybersecurity. My research group has formulated the first optimization models for enhancing SCRM by identifying a set of cybersecurity mitigations that are effective with respect to cost and risk-reduction and are robust to uncertainty. My research has introduced integer programming models to address security mitigation prioritization as well as a new exact algorithm for solving bi-level network interdiction models for critical infrastructure protection. My research group has also developed a new computational algorithm that efficiently solves a difficult class of infrastructure protection problems that considers the impact of adaptive adversaries, an important feature of the models in this area, on a network.

My disaster response and recovery research studies how to respond to emergencies on a network during and immediately after mass casualty incidents and how to restore a network after a disaster. This research area draws upon my emergency management and infrastructure protection expertise gained through the previous two areas of research. In this area of research, my group has investigated how community-driven data can be used to manage interdependent networks to enhance their recovery after a disruption. My research group has formulated and analyzed new models that study how to coordinate the activities of multiple types of service providers to restore a network after a disaster. The results provide insight into our understanding and management of infrastructure recovery from natural disasters.

My research formulates new operations research models and algorithms for solving important and interesting real-world problems of national interest and concern. I take this responsibility to serve my profession and our nation quite seriously. I believe that it is essential for researchers who are working on problems in the public sector to disseminate their research findings to the public through outreach in addition to dissemination in academic journals. Translating research concepts into practical messages is critical for influencing public policy and transitioning research concepts into practice. This is a common theme that permeates all my research activities.


optimization for cyber-security and protecting critical infrastructure

In the past few years, I’ve been working on cyber-security and infrastructure protection research by applying stochastic programming and network interdiction methodologies. My department posted a news article about my research with former PhD student Kay Zheng that you can read here. The research was supported by the National Science Foundation #1422768

My oldest daughter helped me make a short (and campy) youtube video about my cyber-security research. It looks like a movie trailer simply because my daughter likes making movie trailers. I’d totally see this summer blockbuster 😉

 

 

The abstracts and links to my papers in cyber-security are below:

Zheng, K., Albert, L., Luedtke, J.R., Towle, E. 2019. A budgeted maximum multiple coverage model for cybersecurity planning and management, To appear in IISE Transactions. DOI: 10.1080/24725854.2019.1584832

Abstract: This article studies how to identify strategies for mitigating cyber-infrastructure vulnerabilities. We propose an optimization framework that prioritizes the investment in security mitigations to maximize the coverage of vulnerabilities. We use multiple coverage to reflect the implementation of a layered defense, and we consider the possibility of coverage failure to address the uncertainty in the effectiveness of some mitigations. Budgeted Maximum Multiple Coverage (BMMC) problems are formulated, and we demonstrate that the problems are submodular maximization problems subject to a knapsack constraint. Other variants of the problem are formulated given different possible requirements for selecting mitigations, including unit cost cardinality constraints and group cardinality constraints. We design greedy approximation algorithms for identifying near-optimal solutions to the models. We demonstrate an optimal (1–1/e)-approximation ratio for BMMC and a variation of BMMC that considers the possibility of coverage failure, and a 1/2-approximation ratio for a variation of BMMC that uses a cardinality constraint and group cardinality constraints. The computational study suggests that our models yield robust solutions that use a layered defense and provide an effective mechanism to hedge against the risk of possible coverage failure. We also find that the approximation algorithms efficiently identify near-optimal solutions, and that a Benders branch-and-cut algorithm we propose can find provably optimal solutions to the vast majority of our test instances within an hour for the variations of the proposed models that consider coverage failures.

Zheng, K., and Albert, L.A. A robust approach for mitigating risks in cyber supply chains, To appear in Risk Analysis. DOI: 10.1111/risa.13269

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the ( 1 − α ) worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.

Zheng, K., and Albert, L.A. Interdiction models for delaying adversarial attacks against critical information technology infrastructure. To appear in Naval Research Logistics.

Information technology (IT) infrastructure relies on a globalized supply chain that is vulnerable to numerous risks from adversarial attacks. It is important to protect IT infrastructure from these dynamic, persistent risks by delaying adversarial exploits. In this paper, we propose max‐min interdiction models for critical infrastructure protection that prioritizes cost‐effective security mitigations to maximally delay adversarial attacks. We consider attacks originating from multiple adversaries, each of which aims to find a “critical path” through the attack surface to complete the corresponding attack as soon as possible. Decision‐makers can deploy mitigations to delay attack exploits, however, mitigation effectiveness is sometimes uncertain. We propose a stochastic model variant to address this uncertainty by incorporating random delay times. The proposed models can be reformulated as a nested max‐max problem using dualization. We propose a Lagrangian heuristic approach that decomposes the max‐max problem into a number of smaller subproblems, and updates upper and lower bounds to the original problem via subgradient optimization. We evaluate the perfect information solution value as an alternative method for updating the upper bound. Computational results demonstrate that the Lagrangian heuristic identifies near‐optimal solutions efficiently, which outperforms a general purpose mixed‐integer programming solver on medium and large instances.

Zheng, K., and Albert, L.A. An exact algorithm for solving the bilevel facility interdiction and fortification problemOperations Research Letters 46(6), 573 – 578.

We present an exact approach for solving the r-interdiction median problem with fortification. Our approach consists of solving a greedy heuristic and a set cover problem iteratively that guarantees to find an optimal solution upon termination. The greedy heuristic obtains a feasible solution to the problem, and the set cover problem is solved to verify optimality of the solution and to provide a direction for improvement if not optimal. We demonstrate the performance of the algorithm in a computational study.

 


EURO Working Group on Locational Analysis meeting plenary

I gave a plenary talk at the EURO Working Group for Locational Analysis (EWGLA) XXV Conference entitled “On designing public sector systems.” I am grateful to Prof. Dr. Lieselot Vanhaverbeke, organizer of the 2019 EURO Working Group on Locational Analysis meeting and professor at Vrije Universiteit Brussel (VUB) for inviting me. Her hospitality and the hospitality of the entire conference organizing committee was amazing. They even gave me a box of very nice locally made gourmet speculoos cookies, which I greatly appreciate.

My slides are below.

The references from my presentation capture almost two decades of research. The papers can be found in the Research section of my blog and are listed at the bottom of this blog post.

 

I captured much of my visit on twitter. Here are some highlights:

References:

Aviation security

1.Jacobson, S. H., J. E. Virta, L. A. McLay, J. E. Kobza, 2005.  Integer Program Models for the Deployment of Airport Baggage Screening Security Devices, Optimization and Engineering 6(3) 339 – 359.

2.Jacobson, S. H., L. A. McLay, J. E. Kobza, J. M. Bowman, 2005. Modeling and Analyzing Multiple Station Baggage Screening Security System Performance, Naval Research Logistics 52(1), 30 – 45.

3.McLay, L. A., S. H. Jacobson, and J. E. Kobza, 2006. A Multilevel Passenger Prescreening Problem for Aviation Security, Naval Research Logistics 53 (3), 183 – 197.

4.Lee, A.J., L.A. McLay, and S.H. Jacobson, 2009. Designing Aviation Security Passenger Screening Systems using Nonlinear Control.  SIAM Journal on Control and Optimization 48(4), 2085 – 2105.

5.McLay, L. A., S. H. Jacobson, and A. G. Nikolaev, 2009.  A Sequential Stochastic Passenger Screening Problem for Aviation Security, IIE Transactions 41(6), 575 – 591.

6.McLay, L.A., S.H. Jacobson, A.J. Lee, 2010.  Risk-Based Policies for Aviation Security Checkpoint Screening.  Transportation Science 44(3), 333-349.

Infrastructure Protection

1.  Albert McLay, L., 2015. Discrete optimization models for homeland security and emergency management, TutORial at the 2015 INFORMS Annual Meeting, November 1-4, 2015, Philadelphia, PA.

2.Zheng, K., Albert, L., Luedtke, J.R., Towle, E. 2019. A budgeted maximum multiple coverage model for cybersecurity planning and management, To appear in IISE Transactions. DOI: 10.1080/24725854.2019.1584832

3.Zheng, K., and Albert, L.A. 2019. Interdiction models for delaying adversarial attacks against critical information technology infrastructure. To appear in Naval Research Logistics.

Emergency Medical Services

1.McLay, L.A., 2009.  A Maximum Expected Covering Location Model with Two Types of Servers, IIE Transactions 41(8), 730 – 741.

2.McLay, L.A., 2010. Emergency Medical Service Systems that Improve Patient Survivability. Encyclopedia of Operations Research in the area of “Applications with Societal Impact,” eds. J.J. Cochran, L. A. Cox, Jr., P. Keshinocak, J.C. Smith. John Wiley & Sons, Inc., Hoboken, NJ (published online: DOI: 10.1002/9780470400531.eorms0296).

3.McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures.  Health Care Management Science 13(2), 124 – 136.

4.McLay, L.A., Mayorga, M.E., 2011.  Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical Service. IIE Transactions on Healthcare Service Engineering 1, 185 – 196

5.Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A maximum expected covering problem for locating and dispatching servers. To appear in Transportation Science.

6.McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4), 380-394.

7.Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A Maximum Expected Covering Problem for District Design, Transportation Science 51(1), 376 – 390.

8.Grannan, B.C., Bastian, N., McLay, L.A. 2015. A Maximum Expected Covering Problem for Locating and Dispatching Two Classes of Military Medical Evacuation Air Assets. Operations Research Letters 9, 1511-1531.

9.Yoon, S. and Albert, L.A. 2018. Dynamic Resource Assignment for Emergency Response with Multiple Types of Vehicles, Under review at Operations Research, October 2018.

10.Yoon, S., and Albert, L.A. 2019. A dynamic ambulance routing model with multiple response. Under review at Transportation Research Part E: Logistics at Transportation Science.

 


2019 INFORMS Government and Analytics Summit: a recap

I again chaired the INFORMS Government & Analytics Summit in Washington, D.C. on May 20, 2019. The Summit brought together INFORMS experts with policymakers from Capitol Hill, federal agencies, and other policy stakeholders to showcase impactful ways in which operations research tools are being used to save lives, save money and solve problems in the public and private sectors. Pictures from the Summit are below.

Secretary John McHugh gave the keynote talk. INFORMS President Ramayya Krishnan and INFORMS Executive Director Melissa Moore also gave some remarks.

I chaired the panel with panelists Bala Ganesh (UPS), Karla Hoffman (George Mason University), Don Kleinmuntz (Kleinmuntz Associates), Sheldon Jacobson (University of Illinois), David Shmoys (Cornell University). The panel discussed successful case studies based on their work, research, and consulting. The panel was recorded, and you can watch it below:

I discussed the Summit on the INFORMS Resoundingly Human podcast. You can listen to it here.

Secretary John McHugh delivers the opening keynote

I introduce the panelists

Panelists David Shmoys, Don Kleinmuntz, Karla Hoffman, Sheldon Jacobson, and Bala Ganesh

I ask the panelists a question.

 

Read my post about last year’s INFORMS Government & Analytics Summit.


It’s National Emergency Medical Services Week #EMSweek2019. Check out my teaching materials about EMS systems.

Yesterday, I blogged about my research in emergency medical service systems.

I have used EMS research in my teaching. I once taught an entire course on public sector operations research. The blog post about this course is here, and it has a lot of materials.

I visited the University of Oklahoma, where I lectured about modeling service networks and focused on location problems using network optimization for public safety (such as EMS). I introduced public safety operations research and discussed several location models for modeling service networks. Read the blog post here.

I visited Oberlin College to deliver the Fuzzy Vance Lecture in Mathematics (see post here). In addition, I gave a lecture to Bob Bosch’s undergraduate optimization course that focused on ambulance location models and modeling integer programs. Read the blog post here.


It’s National Emergency Medical Services Week #EMSweek2019. Check out my papers and presentations about EMS systems.

This week is National Emergency Medical Services Week. I’ve published and spoken extensively about my research on emergency medical services.

Some blog posts about EMS include:

Mike Trick wrote a post about my semi-plenary talk at the 2014 German OR Society conference entitled “Using analytics for emergency response

 

 

Papers include:

  1. McLay, L.A., A Maximum Expected Covering Location Model with Two Types of ServersIIE Transactions 41(8), 730 – 741.
  2. McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance MeasuresHealth Care Management Science 13(2), 124 – 136.
  3. McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical ServiceIIE Transactions on Healthcare Service Engineering 1, 185 – 196.
  4. Chanta, S., Mayorga, M.E., Kurz, M.E., McLay, L.A., 2011. The minimum p-envy location problem: A new model for equitable distribution of emergency resourcesIIE Transactions on Healthcare Systems Engineering 1(2), 101 – 115.
  5. McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 CallsInterfaces 42(4), 380-394.
  6. Bandar, D., Mayorga, M.E., McLay, L.A., 2012. Optimal Dispatching Strategies for Emergency Vehicles to Increase Patient SurvivabilityInternational Journal of Operational Research.
  7. McLay, L.A., Brooks, J.P., Boone, E.L., 2012. Analyzing the Volume and Nature of Emergency Medical Calls during Severe Weather Events using Regression MethodologiesSocio-Economic Planning Sciences 46, 55 – 66.
  8. Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability modelHealth Care Management Science 16(1), 14 – 26.
  9. McLay, L.A., Mayorga, M.E., 2013. A model for optimally dispatching ambulances to emergency calls with classification errors in patient prioritiesIIE Transactions 45(1), 1—24. This paper was selected as a Best Paper Award for IIE Transactions Focused Issue on Scheduling and Logistics.
  10. Mayorga, M.E., Bandara, D., McLay, L.A., 2013. Districting and dispatching policies for emergency medical service systems to improve patient survivalIIE Transactions on Healthcare Systems Engineering 3(1), 39 – 56.
  11. Toro-Diaz, H., Mayorga, M.E., Chanta, S., McLay, L.A., 2013. Joint location and dispatching decisions for Emergency Medical ServicesComputers & Industrial Engineering 64(4), 917 – 928.
  12. Dreiding, R.A., McLay, L.A., An Integrated Screening Model for Screening Cargo Containers for Nuclear WeaponsEuropean Journal of Operational Research 230, 181 – 189.
  13. Chanta, S., Mayorga, M. E., McLay, L. A., 2014. Improving Rural Emergency Services without Sacrificing Coverage: A Bi-Objective Covering Location Model for EMS SystemsAnnals of Operations Research 221(1), 133 – 159.
  14. Sudtachat, K., Mayorga, M.E., McLay, L.A. 2014. Recommendations for Dispatching Emergency Vehicles under Multi-tiered Response via SimulationInternational Transactions in Operational Research 21(4), 581-617.
  15. Chanta, S., Mayorga, M.E., McLay, L.A., 2014. The minimum p-envy problem with requirement on minimum survival rateComputers & Industrial Engineering 74, 228 – 239.
  16. Bandara, D., Mayorga, M.E., McLay, L.A., 2014. Priority Dispatching Strategies for EMS SystemsThe Journal of the Operational Research Society 65, 572 – 587.
  17. Grannan, B.C., Bastian, N., McLay, L.A. A Maximum Expected Covering Problem for Locating and Dispatching Two Classes of Military Medical Evacuation Air AssetsOperations Research Letters 9, 1511-1531.
  18. Toro-Diaz, H., Mayorga, M.E., McLay, L.A., Rajagopalan, H., Saydem, C., Reducing disparities in large scale emergency medical service systemsJournal of the Operational Research Society 66, 1169-1181. doi:10.1057/jors.2014.83
  19. McLay, L.A., Mayorga, M.E., 2013. A dispatching model for server-to-customer systems that balances efficiency and equityManufacturing & Service Operations Management 15(2), 205 – 200.
  20. Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A Maximum Expected Covering Problem for District DesignTransportation Science 51(1), 376 – 390.
  21. Sudtachat, K., Mayorga, M.E., McLay, L.A., 2016. A Nested-Compliance Table Policy for Emergency Medical Service Systems under RelocationOMEGA 58, 154 – 168.
  22. Ansari, S., Yoon, S., Albert, L. A., 2017. An approximate Hypercube model for public service systems with co-located servers and multiple responseTransportation Research Part E: Logistics and Transportation Review. 103, 143 – 157. DOI: 1016/j.tre.2017.04.013.
  23. Yoon, S., Albert, L. An Expected Coverage Model with a Cutoff Priority QueueHealth Care Management Science 21(4), 517 – 533. DOI: https://doi.org/10.1007/s10729-017-9409-3.
  24. Enayati, S., Mayorga, M., Toro-Diaz, H., Albert, L. 2018. Identifying trade-offs in equity and efficiency for simultaneously optimizing location and multi-priority dispatch of ambulancesInternational Transactions in Operational Research 26, 415 – 438. DOI:1111/itor.12590
  25. Yoon, S. and Albert, L.A., 2018. Dynamic Resource Assignment for Emergency Response with Multiple Types of Vehicles, Submitted to Operations Research, October 2018.
  26. Yoon, S., and Albert, L.A. A dynamic ambulance routing model with multiple response. Submitted to Transportation Research Part E: Logistics and Transportation Review, January 2019.