Improving U.S. Navy Campaign Analyses (海軍戰役分析) with Big Data

Brian L. Morgan, Harrison C. Schramm, Jerry R. Smith, Jr., Thomas W. Lucas, Mary L. McDonald, Paul J. Sanchez, Susan M. Sanchez, Stephen C. Upton, Improving U.S. Navy Campaign Analyses with Big Data, Interfaces, Volume 48, Issue 2, March-April 2018, pp. 130–146.

Decisions and investments made today determine the assets and capabilities of the U.S. Navy for decades to come. The nation has many options about how best to equip, organize, supply, maintain, train, and employ our naval forces. These decisions involve large sums of money and impact our national security. Navy leadership uses simulation-based campaign analysis to measure risk for these investment options. Campaign simulations, such as the Synthetic Theater Operations Research Model (STORM), are complex models that generate enormous amounts of data. Finding causal threads and consistent trends within campaign analysis is inherently a big data problem. We outline the business and technical approach used to quantify the various investment risks for senior decision makers. Specifically, we present the managerial approach and controls used to generate studies that withstand scrutiny and maintain a strict study timeline. We then describe STORMMiner, a suite of automated postprocessing tools developed to support campaign analysis, and provide illustrative results from a notional STORM training scenario. This new approach has yielded tangible benefits. It substantially reduces the time and cost of campaign analysis studies, reveals insights that were previously difficult for analysts to detect, and improves the testing and vetting of the study. Consequently, the resulting risk assessment and recommendations are more useful to leadership. The managerial approach has also improved cooperation and coordination between the Navy and its analytic partners.

The New York City Off-Hour Delivery Program

Jose Holguin-Veras, et al., The New York City Off-Hour Delivery Program: A Business and Community-Friendly Sustainability Program, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 70–86.

The New York City Off-Hour Delivery (NYC OHD) program is the work of a private-public-academic partnership—a collaborative effort of leading private-sector groups and companies, public-sector agencies led by the New York City Department of Transportation, and research partners led by Rensselaer Polytechnic Institute. The efforts of this partnership have induced more than 400 commercial establishments in NYC to accept OHD without supervision. The economic benefits are considerable: the carriers have reduced operational costs and parking fines by 45 percent; the receivers enjoy more reliable deliveries, enabling them to reduce inventory levels; the truck drivers have less stress, shorter work hours, and easier deliveries and parking; the delivery trucks produce 55–67 percent less emissions than they would during regular-hour deliveries, for a net reduction of 2.5 million tons of CO2 per year; and citizens’ quality of life increases as a result of reduced conflicts between delivery trucks, cars, bicycles, and pedestrians, and through the use of low-noise delivery practices and technologies that minimize the impacts of noise. The total economic benefits exceed $20 million per year. The success of the OHD program is due largely to the policy design at its core, made possible with the behavioral microsimulation. This unique optimization-simulation system incorporates the research conducted into an operations research/management science tool that assesses the effectiveness of alternative policy designs. This enabled the successful implementation of the project within the most complex urban environment in the United States.

Barco Implements Platform-Based Product Development in Its Healthcare Division

Robert N. Boute, Maud M. Van den Broeke, and Kristof A. Deneire, Barco Implements Platform-Based Product Development in Its Healthcare Division, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 35–44.

In this article, we present how Barco, a global technology company, used an operations research optimization model, which was supported by an efficient solution method, to implement platforms—common structures from which sets of products could be made—for the design and production of its high-tech medical displays. Our optimization model captures all cost aspects related to the use of platforms; thus, it is an objective tool that considers the input from marketing, sales, research and development (R&D), operations, and the supply chain. This comprehensive view allowed Barco to avoid the excessive costs that may result from the implementation of an incorrect platform. Our model supported Barco in determining the elements that should comprise each platform, the number of platforms to develop, and the products to derive from each platform. The results of the project led to reductions in safety stock and increased flexibility due to the use of platforms: R&D can now introduce twice as many products using the same resources, thus increasing Barco’s earnings by more than five million euros annually and reducing product introduction time by nearly 50 percent.

Discrete-Event Simulation Modeling Unlocks Value for the Jansen Potash Project

Sylvie C. Bouffard, Peter Boggis, Bryan Monk, Marianela Pereira, Keith Quan, Sandra Fleming, Discrete-Event Simulation Modeling Unlocks Value for the Jansen Potash Project, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 45–56.

BHP plans to enter the bulk fertilizer market by developing its first potash operation, the Jansen Potash Mine, in Saskatchewan, Canada. In conjunction with Amec Foster Wheeler, the Jansen project team developed a model of the Jansen production and logistics chain to understand the drivers of production capacity. The Detailed Integrated Capacity Estimate model (DICE) is a comprehensive discrete-event simulation model of Jansen’s upstream production (mining, hoisting, and ore processing) and downstream logistics (rail, port, and marketing). DICE provides an unprecedented combination of complexity, granularity, and scalability, which informs ore storage capacities, product sizing infrastructure, critical-equipment redundancies, bypasses, and operational practices. The team used DICE during the prefeasibility study of the Jansen project. The model provided the justification for the removal of about $300 million in capital expenses to equip the second of two hoisting shafts, the reduction of planned maintenance, and the increase of the degree of mining automation. Throughout the prefeasibility study, Jansen’s annual production in stage 1 of operations was estimated to increase by 15–20 percent, with two-thirds of this gain credited to DICE. This potential additional production added $500 million to the net present value of Jansen stage 1. In consideration of this, among other factors, the BHP board of directors approved the transition of the Jansen project from a prefeasibility to a feasibility study.

A Novel Movement Planner System for Dispatching Trains

Srinivas Bollapragada, Randall Markley, Heath Morgan, Erdem Telatar, Scott Wills, Mason Samuels, Jerod Bieringer, Marc Garbiras, Giampaolo Orrigo, Fred Ehlers, Charlie Turnipseed, Jay Brantley, A Novel Movement Planner System for Dispatching Trains, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 57–69.

General Electric Company (GE) partnered with Norfolk Southern Railroad (NS) to create and implement an optimization algorithm-based software system that dispatches thousands of trains in real time, increases their average speed, and allows NS to realize annual savings in the hundreds of millions of dollars. NS handles a range of rail traffic that includes intermodal, automobile transport, manifest freight, and passenger, all with unique priorities and scheduling requirements. Previously, dispatching for each geographic area was managed manually from regional dispatch centers and did not encompass a view of the entire rail network. The algorithm that we developed incorporates data about the properties of the rail networks (e.g., track layout, speed restrictions, height and weight restrictions), data about the trains (e.g., schedules, operating costs, train characteristics), and additional activities associated with train dispatching, such as crew changes and inspections. In doing so, we created a novel system to manage all train dispatching, increased the average speed of trains by two miles per hour, and decreased operating costs, while significantly improving schedule adherence and crew expirations. Every mile-per hour increase in average speed translates to $200 million savings in capital and operational expenses annually for NS. GE is currently implementing this system at two other railroads and is gaining additional important benefits from the project.