Unmanned aerial vehicles (UAVs) are expected to make groundbreaking changes in the logistics industry. Leading logistics companies have been developing and testing their usage of UAVs recently as an environmentally friendly and cost-effective option. In this paper, we investigate how much the UAV delivery service is environmentally friendly compared to the traditional ground vehicle (GV) delivery service. Since there are fuel (battery) and loadable weight restrictions in the UAV delivery, multi-hopping of UAV is necessary, which may cause a large consumption of electrical energy. We present a two-phase approach. In Phase I, a new vehicle routing model to obtain optimal delivery schedules for both UAV-alone and GV-alone delivery systems is proposed, which considers each system's restrictions, such as the max loadable weight and fuel replenishment. In Phase II, CO 2 emissions are computed as a sustainability measure based on the travelling distance of the optimal route obtained from Phase I, along with various GV travel-speeds. A case study finds that the UAV-alone delivery system is much more CO 2 efficient in all ranges of the GV speeds investigated.

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Probability-based models are developed using information from a variety of datasets to predict daily surgical volumes weeks in advance. The quest was motivated by the need to make real-time adjustments to staff capacity and reallocation of the operating room block time based on predicted future demand. We test the notion that more data always leads to better predictions. Four probabilistic prediction models are presented, each parameterized based on real data and information from different sources. We hypothesize that the accuracy of the prediction improves by incorporating additional information. Models are tested for a surgical service at a large hospital using data of 20 months (January 19, 2015-August 31, 2016. We find that incorporating additional information may not improve prediction accuracy if that information is prone to data errors. However, deploying analytical data treatment to ameliorate these errors leads to better predictions. We also compare the predictive ability of the probability-based models to neural network-based models and find that the neural network models do not outperform simpler models. Managers should critically review the accuracy of the data used in decision-making. While a greater amount of inherently error-free information is the best, analytics can enhance the utility of error-prone data. [

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Utilizing unmanned aerial vehicles for delivery service has been drawing attention in the logistics industry. Since commercial unmanned aerial vehicles have fundamental limitations on payloads and battery capacities, hybrid ground vehicle and unmanned aerial vehicle models have been actively investigated as practical solutions. However, these studies have focused on linehaul (delivery) demands, excluding a large number of backhaul (pickup) demands. If we consider both demands at the same time, an empty unmanned aerial vehicle that finished linehaul service can be immediately used to serve a backhaul customer. In this study, we investigate the differences that arise by considering backhauls as an additional element of the routing problem. A mixed integer linear programming model is developed, and a heuristic is constructed to solve large-scale problems. To demonstrate the effectiveness of our model, we compare it to existing models using a real-world example. Our solution is also evaluated based on experiments employing a large number of randomly generated datasets.INDEX TERMS Drone, flying sidekick traveling salesman problem, heuristic, mixed integer linear programming, unmanned aerial vehicle, vehicle routing problem with backhauls.

This paper describes a single-machine scheduling problem of maximizing total job value with a machine availability constraint. The value of each job decreases over time in a stepwise fashion. Several solution properties of the problem are developed. Based on the properties, a branch-and-bound algorithm and a heuristic algorithm are derived. These algorithms are evaluated in the computational study, and the results show that the heuristic algorithm provides effective solutions within short computation times.

Overcrowding in emergency departments (EDs) has long been a problem worldwide and has serious consequences for patient satisfaction and safety. Typically, overcrowding is caused by delays in the boarding time of ED patients waiting for inpatient beds. If the hospitalization of patients is predicted early enough in EDs, inpatient beds can be prepared in advance and the boarding time can be reduced. We design machine learning-based hospitalization predictive models using data on 27,747 patients and compare the experimental results. Five predictive models are designed: 1) logistic regression, 2) XGBoost, 3) NGBoost, 4) support vector machine, and 5) decision tree models. Based on the predictive results, we estimate the quantitative effects of hospitalization predictions on EDs and wards. Using the data from the ED of a general hospital in South Korea, our experiments show that the ED length of stay of a patient can be reduced by 12.3 minutes on average and the ED can reduce the total length of stay by 340,147 minutes for a year.INDEX TERMS Emergency department, machine learning, hospitalization prediction, estimation of quantitative effects.

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