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.

American Red Cross Uses Analytics-Based Methods to Improve Blood-Collection Operations

Turgay Ayer, Can Zhang, Chenxi Zeng, Chelsea C. White III, V. Roshan Joseph, Mary Deck, Kevin Lee, Diana Moroney, Zeynep Ozkaynak, American Red Cross Uses Analytics-Based Methods to Improve Blood-Collection Operations, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 24–34.

In this study, we describe a regional-level cryoprecipitate (cryo)-collection project at the American Red Cross Southern Region, one of the 36 Red Cross regions in the United States, which serves more than 120 hospitals in the Southern part of the country. Managing collections for cryo units is particularly challenging because producing cryo requires the collected whole blood to be processed within 8 hours after collection; for all other blood products, this time constraint is at least 24 hours. This project focuses on dynamically determining when and from which mobile collection sites the American Red Cross Southern Region should collect whole blood for cryo production, such that it meets its weekly collection targets and minimizes its collection costs. To solve this problem, we developed a new collection model, which allows different types of collections at the same collection site and developed a dynamic programming approach to solve the problem to near optimality. Analyzing the dynamic programming results led us to create a greedy-algorithm heuristic, which we implemented in a decision support tool (DST) to systematize the selection of the collection sites. The implementation of the DST in the Red Cross Southern Region resulted in an increase in the number of whole blood units that can be shipped back to the production facility and processed within eight hours after collection. During the fourth quarter of 2016, this facility processed about 1,000 more units of cryo per month (an increase of 20 percent) at a slightly lower collection cost, resulting in an approximately 40 percent reduction in the per-unit collection cost for cryo. Based on the successful implementation in the Southern Region, the American Red Cross also implemented our DST in its St. Louis facility and plans to implement it at its 10 other cryo production facilities.