Knowledge that Transforms

In “The Weighted Nonnegative Least-Squares Problem with Implicitly Characterized Points,” A. Fattahi, S. Dasu, and R. Ahmadi study a new variation of nonnegative least-squares problem when the disc...

A dramatic increase in U.S. mortgage foreclosures during and after the great economic recession of 2007–2009 had devastating impacts on the society and the economy. In response to such negative impacts, nonprofit community development corporations (CDCs) throughout the United States use various resources, such as grants and lines of credit, in acquiring and redeveloping foreclosed housing units to support neighborhood stabilization and revitalization. Given that the cost of all such acquisitions far exceeds the resources accessible by these nonprofit organizations, we identify socially optimal policies for CDCs for dynamically selecting foreclosed properties to target for potential acquisition as they become available over time. We evaluate our analytical results in a numerical study involving a CDC serving a major city in the United States and specify social return-based thresholds defining selection decisions at different funding levels. We also find that, for most foreclosed properties, CDCs should not offer more than the asking price and should typically consider overbidding only when the total available budget is low. Overall, comparisons of optimal policies with historical acquisition data suggest a potential improvement of around 20% in expected total impacts of the acquisitions on nearby property values. Considering a CDC with annual fund availability of $4 million for investment, this corresponds to an estimated additional value of around $280,000 for the society.

Discrete convexity, which extends submodularity to integer vectors, has been used in the economics and management literature to characterize the behavior of optimal policies. One of its variants, called L♮-convexity, has enabled recent advances in various operations management systems. In a paper published by Operations Research in 2005, an assemble-to-order inventory system was shown to have the L♮-convexity property, which was used to motivate an efficient algorithm. In a technical note, “Error Noted in ‘Order-Based Cost Optimization in Assemble-to-Order Systems’ by Lu and Song (2005)” by Bolandnazar, Huh, McCormick, and Murota, the authors show that the proof in that paper is incorrect and L♮-convexity may not hold. Despite this error, the authors credit Lu and Song for introducing this useful concept to the operations management community.

Catastrophic events can dramatically disrupt distribution networks and supply chains; think, for example, about the threat of a hurricane, which causes significant spikes in demands for gasoline, water, and other products. What are the weak links in such a supply network, or what is the most likely way in which such a network can be disrupted? In “Rare Event Simulation for Distribution Networks,” J. Blanchet, J. Li, and M. K. Nakayama devise optimal simulation algorithms for estimating the occurrence of disruptive events in complex distribution networks. This development is noteworthy because it can be used to mitigate the impact in populations that are susceptible to these types of events, which can incur tremendous costs to society.

Public health security—achieved by effectively preventing, detecting, and responding to events that affect public health, such as bioterrorism, disasters, and naturally occurring disease outbreaks—is a key aspect of national security. However, effective public health preparedness depends on answering largely unanswerable questions such as determining the chance of a bioterror attack in the United States over a given time horizon, or chance of an anthrax attack, or the location and magnitude of such an attack. In “Public Health Preparedness: Answering (Largely Unanswerable) Questions with Operations Research—The 2016–2017 Philip McCord Morse Lecture,” Margaret Brandeau describes the important role that OR-based analyses can play in providing insight into complex public health preparedness planning problems, thereby supporting good decisions. The author presents three examples from her work: logistics of response to an anthrax attack, prepositioning of medical countermeasures for anthrax, and stockpiling decisions for the United States’ Strategic National Stockpile.

Hospital care is one of the fundamental components in any healthcare delivery system. Within hospital care, surgical procedures account for the largest share of the revenue generated. Traditionally, the operating room (OR) capacity is viewed as a major constraint limiting a hospital's ability to increase the number of surgical procedures and the accompanying revenues. However, each procedure consumes not only the OR capacity but also, the hospital bed capacity. In “Managing Portfolio of Elective Surgical Procedures: A Multidimensional Inverse Newsvendor Problem,” Hessam Bavafa, Charles M. Leys, Lerzan Örmeci, and Sergei Savin investigate the effects of the interaction between the two resources (i.e., OR and recovery beds) on the optimal number of elective surgical procedures to be performed daily. They evaluate the performance of the “front-end” approach, which considers only the OR capacity, in different settings. Moreover, they show how the variability of the resource utilization by surgical procedures affects the optimal elective portfolio.