BACKGROUND: Intrahospital patient transport is pivotal in enabling hospital operations and facilitating safe and efficient patient movement. However, transport delays are common in hospitals, signaling a need for improvement. This study develops, implements, and evaluates a proximity-based transporter-to-request assignment system aimed at improving transport service system efficiency. MATERIALS AND METHODS: In this observational study, we used discrete-event simulation to design and optimize an enhancement to an electronic medical record's original first-in, first-out transporter-to-request assignment system, and we implemented it at a quaternary care academic medical center. Our enhancement prioritizes requests based on the proximity of available transporters within pre-specified areas. We compared transport request completion time (primary outcome) and the percentage of transports exceeding 45 minutes (secondary outcome) during control (01/2021-02/2022) and intervention (02/2022-03/2023) periods and estimated their differences using multivariate generalized linear models to adjust for confounding factors including variable workforce levels and workload. RESULTS: A total of 136 414 transport requests were included in the study. The intervention was associated with an adjusted 5.0% (95% confidence interval 1.8%-8.5%) reduction in completion times and a 16.0% (7.4%-23.9%) relative reduction in the percentage of trips exceeding the 45-minute completion time target. DISCUSSION: The intervention's improvements stem from reductions in unnecessary travel time between transport requests, common to first-in first-out assignment systems. The intervention was designed to be natively integrated into existing electronic health record systems, reducing barriers to real-world adoption. CONCLUSION: Implementing a proximity-based assignment system designed based on simulation-optimization modeling improved intrahospital patient transport efficiency without requiring additional staff.

This paper provides the first large-scale data-driven analysis to evaluate the predictive power of digital supply chain attributes for assessing risk of cyberattack data breaches. Motivated by rapid increase in the complexity of digital supply chains and related third-party enabled cyberattacks, the paper provides the first quantitative empirical evidence that digital supply-chain attributes are significant predictors of enterprise cyber risk. The analysis leverages externally observable cybersecurity ratings that aim to capture the quality of the enterprise internal cybersecurity management, but augments these with original supply chain features that are inspired by observed third-party cyberattack scenarios, as well as concepts from network science research. The main quantitative result of the paper is to show that these supply chain network features add significant detection power to predicting enterprise cyber risk, relative to merely using enterprise-only attributes. In particular, compared to a base model that relies only on internal enterprise features, the supply chain network features improve the out-of-sample AUC by 2.3%. Given that a cyber data breach on a specific enterprise is a low probability high impact risk event, these improvements in the prediction power have significant value. Additionally, the model highlights several cybersecurity risk drivers related to third-party cyberattack and breach mechanisms and provides important insights as to what key metrics should be monitored and what interventions might be effective to mitigate these risks.

This paper provides the first sample complexity lower bounds for the estimation of simple diffusion models which seek to explain the diffusion of an epidemic in a network. The Susceptible-Infected-Recovered (SIR) model is a classic example, proposed nearly a century ago [2]. The SIR model remains a cornerstone for the forecasting of epidemics. The so-called Bass model [1] remains a basic building block in forecasting consumer adoption of new products and services. The durability of these models arises from the fact that they have shown an excellent fit to data, in numerous studies spanning both the epidemiology and marketing literatures. Somewhat paradoxically, using these same models as reliable forecasting tools presents a challenge.

In the rectangle stabbing problem, we are given a set of axis parallel rectangles and a set of horizontal and vertical lines, and our goal is to find a minimum size subset of lines that intersect all the rectangles. In this article, we study the capacitated version of this problem in which the input includes an integral capacity for each line. The capacity of a line bounds the number of rectangles that the line can cover. We consider two versions of this problem. In the first, one is allowed to use only a single copy of each line ( hard capacities ), and in the second, one is allowed to use multiple copies of every line, but the multiplicities are counted in the size (or weight) of the solution ( soft capacities ). We present an exact polynomial-time algorithm for the weighted one dimensional case with hard capacities that can be extended to the one dimensional weighted case with soft capacities. This algorithm is also extended to solve a certain capacitated multi-item lot-sizing inventory problem with joint set-up costs. For the case of d -dimensional rectangle stabbing with soft capacities, we present a 3 d -approximation algorithm for the unweighted case. For d -dimensional rectangle stabbing problem with hard capacities, we present a bi-criteria algorithm that computes 4 d -approximate solutions that use at most two copies of every line. Finally, we present hardness results for rectangle stabbing when the dimension is part of the input and for a two-dimensional weighted version with hard capacities.

We consider two fundamental stochastic optimization problems that arise in the context of supply-chain models, the single-period newsvendor problem and its multiperiod extension with independent demands. These problems are among the most well-studied stochastic optimization problems in the Operations Research literature. Most commonly, these problems are studied from the perspective that the input probability distributions are given in terms of specific probability distribution functions that are computationally tractable; under this assumption, both problems can be solved efficiently. Unfortunately, this information is unlikely to be available in practice, and hence we make the more realistic assumption that the probability distribution is given by a "black box" from which independent samples can be drawn. We give the first fully polynomial randomized approximation schemes for these two problems in this sampling-based model.Our work provides new insights into the power of two of the most often-used approaches to solving stochastic optimization problems, the sample average approximation (SAA) and stochastic dynamic programming. For the newsvendor problem, we show that by taking a polynomial number of samples and then solving the newsvendor problem with respect to the resulting approximation to the true distribution, we obtain provably near-optimal solution. This significantly extends the class of problems for which the SAA is known to yield a scheme. Finally, we show how to adapt the framework of stochastic dynamic programming to yield an approximation scheme for the multiperiod newsvendor problem with independent demands. We believe that this is an interesting first step towards the goal of providing a mechanism for deriving efficient approximate stochastic dynamic programming methods for a wide range of multistage stochastic optimization problems.

We consider several classical models in deterministic inventory theory: the single-item lot-sizing problem, the joint replenishment problem, and the multi-stage assembly problem. These inventory models have been studied extensively, and play a fundamental role in broader planning issues, such as the management of supply chains. We shall give a novel primal-dual framework for designing algorithms for these models that significantly improve known results in several ways: the performance guarantees for the quality of the solutions improve on or match previously known results; the performance guarantees hold under much more general assumptions about the structure of the costs, and the algorithms and their analysis are significantly simpler than previous known results. Finally, our primal-dual framework departs from the structure of previously studied primal-dual approximation algorithms in significant ways, and we believe that our approach may find application in other settings.We provide 2-approximation algorithms for the joint replenishment problem and for the assembly problem, and solve the single-item lot-sizing problem to optimality. The results for the joint replenishment and the lot-sizing problems also hold for their generalizations with back orders allowed. As a byproduct of our work, we prove known and new upper bounds on the integrality gap of the LP relaxations for these problems.