Andrew P. Armacost

Composite Variable Formulations for Express Shipment Service Network Design

In this paper we describe a new approach to solving the express shipment service network design problem. Conventional polyhedral methods for network design and network loading problems do not consistently solve instances of the planning problem we consider. Under a restricted version of the problem, we transform conventional formulations to a new formulation using what we termcomposite variables. By removing flow decisions as explicit decisions, this extended formulation is cast purely in terms of the design elements. We establish that its linear programming relaxation gives stronger lower bounds than conventional approaches. We apply this composite variable formulation approach to the UPS Next Day Air delivery network and demonstrate potential annual cost savings in the hundreds of millions of dollars.

Network design formulations for scheduling U.S. Air Force channel route missions

Each month, the United States Air Forces Air Mobility Command is responsible for designing a large-scale air mobility network-called the channel route network-that is used to transport military personnel and cargo throughout the world. Traditionally, planning the channel route network has been a manual process, requiring numerous hours to generate a monthly channel route schedule. We formulate the monthly channel route scheduling problem using a traditional network design formulation, and we use price-directive decomposition to overcome tractability issues. The resulting linear programming bounds on the optimal integer solution are weak. To overcome these challenges, we apply a variable redefinition technique known as composite variable modelling. Using this technique, we alleviate the need to explicitly include cargo flow decisions by implicitly capturing them in the design variables. The resulting formulation is computationally superior to the traditional network design formulation because it achieves tighter bounds, allowing excellent integer solutions to be found quickly.

An integer programming approach to support the US Air Force's air mobility network

The United States Air Forces air mobility command is responsible for creating a schedule and executing that schedule for a large-scale air mobility network that encompasses aircraft with prioritized missions. Aerial ports (airports) can process or park a maximum number of aircraft, called the maximum on ground (MOG). As the schedule changes due to disruptions, such as equipment failure or weather, the MOG constraint can cause the new schedule to be infeasible. Traditionally, re-planning the channel route schedule to adhere to MOG constraints has been a manual process that usually stops after the first feasible set of changes is found, due to the challenges of large amounts of data and urgency for a re-plan. We extend Bertsimas and Stocks integer program formulation for the commercial airline Multi-Airport Ground-Holding Problem to the air mobility network. Our integer programming formulation recommends delays to certain aircraft on the ground to minimize the effects of system-wide disruptions while taking account mission priorities of the aircraft.

Allocation of Air Resources Against and Intelligent Adversary

Abstract : In a battlefield situation, the use of air assets can have a large impact upon the outcome. The problem we consider is allocating scarce resources among activities that conduct pre-strike Intelligence, Surveillance, and Reconnaissance (ISR), take strike actions against, or gather battle damage assessment (BDA) information about a set of targets in order to perform the targeting cycle. We explore methods that combine Partially Observable Markov Decision Processes (POMDPs), which prescribe strike and observation policies, and integer programing formulations, which pick the optimal set of policies given resource constraints. This work adds five major contributions beyond previous work on similar problems. The first improvement is the introduction of allocation decisions for ISR assets, which search out and identify new targets. Also included is a model of an intelligent adversary, specifically representations of regenerative and mobile targets. In addition to incorporating Chengs Linear Support algorithm for solving two-dimensional targeting POMDPs, we incorporate the Incremental Pruning algorithm to solve higher dimensional POMDPs for target discovery and identification. Finally, we introduce a new initialization technique as well as two integer programming formulations of the targeting cycle problem. We demonstrate the computational benefits of this decomposition through a number of parameter variation tests and targeting cycle vignettes and discuss the qualitative characteristics of the solutions generated.

Editorial---Introduction to the Special Issue: Student Projects with Industry

In our undergraduate program at the U.S. Air Force Academy, we have relied on our senior capstone course to provide an integrative and experiential learning environment for our students. Over the 18 years we have offered cadet senior design project, we have noted many educational benefits. We have witnessed our students boasting of their project years after graduating. We have boosted the reputation of our program among external stakeholders who benefit from the practical benefits associated with having a group of undergraduates study their business process and make recommendations for change based upon the use of Operations Research and Analytics. Over this same time, however, we also wondered about the effectiveness of the program: were we really making an impact on the educational experiences of the students? Were the benefits really worth the enormous cost of executing such projects, particularly faculty time? We also explored possibilities of expanding our program from strictly pro bono work to work with paying clients, but were unsure whether this would negatively impact the primary educational goals of the experience. Over the last decade, we have made several presentations about our capstone course at INFORMS annual conferences, receiving many suggestions of how we could improve the course and many comments about how ideas from our course could be exploited at other schools. A vehicle for sharing best practices in projects-based courses seemed to be of value to many schools, and when the editors of ITE approached us about editing a special issue focusing on project-based course, we jumped at the chance. We knew the value that this type of endeavor would make. We hope that you find this collection of papers that address the design and delivery of projects courses to be useful. As we worked with the authors on this collection of papers, we have been impressed with their passion about their courses. They represent many types of schools from around the globe: engineering to business, from small to large, from undergraduate to graduate, and from technical to managerial foundations. Each focuses on the use of OR and Analytics in a practical setting and on the engagement of clients and we encouraged authors to provide “how to” comments based on their experiences. We encourage you to rely on this special issue as a valuable resource. Amongst the articles, you are sure to find a variety of approaches and innovative practices to assist you as you design, modify, or dream about your projectbased course. The five Special Issue articles in this issue form Part 1 of the special issue on student projects with industry. Part 2, containing three more articles, will be published in the next issue.

Abstracts for the 2001 Transportation Science Section Dissertation Prize Competition

The accuracy of multilateration systems can be greatly improved by using a correction method based on the SLS (Sideband Lobe Suppression) signal produced by a Secondary Surveillance Radar (SSR). Multilateration is a cooperative surveillance technique for aircraft equipped with Air Traffic Control Radar Beacon System (ATCRBS), Mode S, or Automatic Dependent Surveillance Broadcast (ADS-B) transponders. When one of these transponders aboard a vehicle is interrogated, it responds by broadcasting a message based on what the interrogation requests. These reply messages may be multilaterated to determine the source position of the transmission. Multilateration is a Time Difference of Arrival (TDOA) technique similar to triangulation. Multilateration can be performed to locate the transmission source of any SSR signal. Error detection and correction may be performed on the system by conducting a comparison of a known TDOA for the receiver/transmitter geometry, to the measured TDOA from a Side Lobe Suppression (SLS) pulse emanating from a primary radar.