Search

Typically, within facility location problems, fairness is defined in terms of accessibility of users. However, for facilities perceived as undesirable by communities hosting them, fairness between the usage of facilities becomes especially important. Limited research exists on this notion of fairness. To close this gap, we develop a series of optimization models for the allocation of populations of users to facilities such that access for users is balanced with a fair utilization of facilities. The optimality conditions of the underlying nonconvex quadratic models state the precise balance between accessibility and fairness. We define new classes of fairness and a metric to quantify the extent to which fairness is achieved in both optimal and suboptimal allocations. We show that a continuous relaxation of our central model is sufficient to achieve a perfect extent of fairness, while a special case reduces to the classical notion of proportional fairness. Our work is motivated by pervasive ecological challenges faced by the waste management community as policymakers seek to reduce the number of recycling centers in the last few years. As a computational case study, applying our models on data for the state of Bavaria in Germany, we find that even after the closure of a moderate number of recycling centers, large degrees of access can be ensured, provided that the closures are conducted optimally. Fairness, however, is impacted more, with facilities in rural regions shouldering larger loads of visiting populations than those in urban regions. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Computer resources and support provided by the Erlangen Regional Computing Center are gratefully acknowledged. B. Singh was partially financially supported by the Bavarian State Ministry for Science and Art (Bayerisches Staatsministerium für Wissenschaft und Kunst) under the Competence Network for Scientific High Performance Computing in Bavaria. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0308) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0308). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Turkey's 2009 series of banknotes feature six eminent scholarly figures, with at least two of interest to a mathematical audience. We lay these mathematicians within a historical context, and provide a short summary of how they have contributed to the country's promotion and revival of scientific knowledge. Such campaigns lead to an increased awareness for both mathematics and mathematicians in the general audience, and can also be an effective strategy in boosting public opinion of the science, technology, engineering, and mathematics (STEM) disciplines. [ABSTRACT FROM AUTHOR]

Motivated by a routing problem faced by banks to enhance their encashment services in the city of Perm, Russia, we solve versions of the traveling salesman problem (TSP) with clustering. To minimize the risk of theft, suppliers seek to operate multiple vehicles and determine an efficient routing; and, a single vehicle serves a set of locations that forms a cluster. This need to form independent clusters—served by distinct vehicles—allows the use of the so-called cluster-first route-second approach. We are especially interested in the use of heuristics that are easily implementable and understandable by practitioners and require only the use of open-source solvers. To this end, we provide a short survey of 13 such heuristics for solving the TSP, five for clustering the set of locations, and three to determine an optimal number of clusters—all using data from Perm. To demonstrate the practicality and efficiency of the heuristics, we further compare our heuristic solutions against the optimal tours. We then provide statistical guarantees on the quality of our solution. All of our anonymized code is publicly available allowing extensions by practitioners, and serves as a decision-analytic framework for both clustering data and solving a TSP. [ABSTRACT FROM AUTHOR]

Jaki further pointed out that Soldner's discovery was largely overlooked even by Soldner's biographers and in a later dissertation on his academic life [[10], footnote 3]. Soldner had calculated this same value as HT ht in 1801, but Einstein made no reference to Soldner's work. See's accusation concluded that the source of the error in Soldner's 1801 work is exactly the same as that in Einstein's 1911 work [[8]]. Jaki also provided an extensive historical context for Soldner's article and was remarkably candid in pointing out the irony of academicians "forgetting" Soldner's work. [Extracted from the article]

Vaccines are arguably the most important means of pandemic influenza mitigation. However, as during the 2009 H1N1 pandemic, mass immunization with an effective vaccine may not begin until a pandemic is well underway. In the U.S., state-level public health agencies are responsible for quickly and fairly allocating vaccines as they become available to populations prioritized to receive vaccines. Allocation decisions can be ethically and logistically complex, given several vaccine types in limited and uncertain supply and given competing priority groups with distinct risk profiles and vaccine acceptabilities. We introduce a model for optimizing statewide allocation of multiple vaccine types to multiple priority groups, maximizing equal access. We assume a large fraction of available vaccines are distributed to healthcare providers based on their requests, and then optimize county-level allocation of the remaining doses to achieve equity. We have applied the model to the state of Texas, and incorporated it in a Web-based decision-support tool for the Texas Department of State Health Services (DSHS). Based on vaccine quantities delivered to registered healthcare providers in response to their requests during the 2009 H1N1 pandemic, we find that a relatively small cache of discretionary doses (DSHS reserved 6.8% in 2009) suffices to achieve equity across all counties in Texas. [ABSTRACT FROM AUTHOR]

Objectives: We provide a methodology for estimating counts of single-year-of-age live-births, fetal-losses, abortions, and pregnant women from aggregated age-group counts. As a case study, we estimate counts for the 254 counties of Texas for the year 2010. Results: We use interpolation to estimate counts of live-births, fetal-losses, and abortions by women of each singleyear-of-age for all Texas counties. We then use these counts to estimate the numbers of pregnant women for each single-year-of-age, which were previously available only in aggregate. To support public health policy and planning, we provide single-year-of-age estimates of live-births, fetal-losses, abortions, and pregnant women for all Texas counties in the year 2010, as well as the estimation method source code. [ABSTRACT FROM AUTHOR]

There is a long list of Popov's honors and recognitions, but two of my favorites are the asteroid 3074 Popov and the crater Popov on the far side of the Moon. Under Berg's initiatives (together with Aleksei A. Lyapunov), some of the first translations of cybernetics and operations research books from English into Russian were made. [Extracted from the article]

We provide a data-driven method for optimizing pharmacybased distribution of antiviral drugs during an influenza pandemic in terms of overall access for a target population and apply it to the state of Texas, USA. We found that during the 2009 influenza pandemic, the Texas Department of State Health Services achieved an estimated statewide access of 88% (proportion of population willing to travel to the nearest dispensing point). However, access reached only 34.5% of US postal code (ZIP code) areas containing <1,000 underinsured persons. Optimized distribution networks increased expected access to 91% overall and 60% in hard-to-reach regions, and 2 or 3 major pharmacy chains achieved near maximal coverage in well-populated areas. Independent pharmacies were essential for reaching ZIP code areas containing <1,000 underinsured persons. This model was developed during a collaboration between academic researchers and public health officials and is available as a decision support tool for Texas Department of State Health Services at a Web-based interface. [ABSTRACT FROM AUTHOR]

The article offers the author's insights on Dmitry Borisovich Gnedenko, a professor at Lomonosov Moscow State University (MSU), and his experience of meeting Gnedenko. Topics discussed by the author include how Gnedenko shows kindness to a stranger like him, the interest of Gnedenko in queueing and reliability theory in the Probability Department of MSU and in actuarial mathematics, and the lack of research written on the scientific achievement of Gnedenko.

Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing coronavirus disease 2019 (COVID- 19) social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a strategy for triggering short-term shelter-in-place orders when hospital admissions surpass a threshold. We use stochastic optimization to derive triggers that ensure hospital surges will not exceed local capacity and lockdowns are as short as possible. For example, Austin, Texas— the fastest-growing large city in the United States—has adopted a COVID-19 response strategy based on this method. Assuming that the relaxation of social distancing increases the risk of infection sixfold, the optimal strategy will trigger a total of 135 d (90% prediction interval: 126 d to 141 d) of sheltering, allow schools to open in the fall, and result in an expected 2,929 deaths (90% prediction interval: 2,837 to 3,026) by September 2021, which is 29% of the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices, and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives. [ABSTRACT FROM AUTHOR]

Tuberculosis was ranked No. 3 among 29 types of infectious diseases in January 2022 of Jiangsu Province, in the southeast of China, and it has strong seasonality whose incidence rate has been high in springs and low in winters. This comprehensive study extends the spreading dynamics to the control strategies of TB in Jiangsu Province with time‐varying transmission rate, which is the first one in this type. The periodic transmission rate is incorporated to the classic SEIR model with susceptible, latent, infectious, and recovered compartments. The analysis shows that when the basic reproduction number R0$$ {R}_0 $$ is less than one, the epidemic will eventually disappear but, if R0$$ {R}_0 $$ is larger than one, the disease may persist with a periodic solution. The R0$$ {R}_0 $$ value of Jiangsu TB is calculated as 1.297. In addition, the model predicts that the epidemic will not disappear over time and there will be a major outbreak after certain years. To eradicate TB, this work introduces three control strategies on susceptible, latent, and infectious populations. It turns out that the optimal control solutions can reduce the incidence rate in 2035 by 90% from 2015, the goal of World Health Organization. Simultaneously, we find that the optimal controls have the same seasonality as the transmission rate. This indicates that Jiangsu Province needs to invest more control efforts in springs than in winters. In addition, the cost of control strategies is analyzed in terms of the unit cost, total cost, and incremental cost‐effectiveness. Summarizing all results, we recommend targeting the latent patients, or both latent and infectious individuals, with the transmission rate‐synchronized control strength as the most effective control strategy, to mitigate or even eradicate TB disease in Jiangsu Province. [ABSTRACT FROM AUTHOR]