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We present a spatiotemporal data set of all out-of-hospital sudden cardiac arrests (OHCA) dispatches for the City of Virginia Beach. We also develop a modular toolkit that can be used to process the data and generate problem instances based on user-defined input. The data were collected from multiple sources, and our analysis process was validated by Virginia Beach officials. The data set consists of detailed information about each dispatch made in response to an OHCA; it includes the time the call for service arrived, the response time of the first unit on scene, the address, and the coordinates of each OHCA incident. It also contains detailed spatial information for all existing first-responder stations and both the great-circle and the road distances between all first-responder stations and OHCA incidents. The raw data files were very large in size and were processed using SAS®, MATLAB, and QGIS. In conjunction with the database, we provide a MATLAB code that allows generating multiple random test instances based on user-defined input. The library of problems can be used in healthcare emergency problems and also for facility location models, bilocation problems, and drone studies. The data set was organized such that it can be readily used by researchers in the field of healthcare operations research and those studying the spatiotemporal distribution of OHCAs. Given the difficulty to access OHCA data at the level of detail we provide, the data set will facilitate the implementation of data-driven models to design emergency medical response networks and to study the distribution of OHCAs. Additionally, the provision of data and the toolkit will be very useful in benchmarking algorithms and solvers, which is valuable to the data-driven optimization community in general. Summary of Contribution: The paper provides a data set of spatiotemporal information out-of-hospital cardiac arrests (OHCAs) for the City of Virginia Beach. The complete data set also includes spatial information about all fire, emergency medical services, and police stations in the city and both the road and haversine distances between each pair of stations and OHCA incident. Additionally, we provide a toolkit to generate random instances based on user input. To the best of our knowledge, it is the first time that an OHCA database is made publicly available in such level of detail, and there is no precedent of such in IJOC. OHCAs are a leading cause of death worldwide, and emergency medical services still encounter difficulties in providing care in a timely manner. Given the criticality of OHCAs, we believe that making this data set publicly available can help the implementation of data-driven models by researchers in the field of operations research.

In deregulated electricity markets, a Generation Company (Genco) has to optimally allocate their energy among different markets including spot, local and bilateral contract markets. Modern portfolio theory (MPT) allows a Genco to achieve their goal by maximizing their profit and decreasing their associated risk. Combining MPT with an adequate tool to forecast energy prices makes it possible for a Genco to vary the optimal allocation of their portfolio even on a daily basis. This paper proposes two MPT models, one applying the Mean Variance Criterion (MVC) and the other one the Conditional Value at Risk (CVaR). The MPT models are combined with a generalized autoregressive conditional heteroskedastic (GARCH) prediction technique for a Genco to optimally diversify their energy portfolio. The two models are applied to a real PJM electricity market showing not only their capabilities but also useful comparisons between them in order to help decision makers to use them as decision-aid tools.

A recent article by Zaghian et al. (2018) proposed reformulations, proposed reformulations of stochastic chance-constrained programming models for radiation therapy treatment planning. This note questions the validity of the proposed reformulations and shows that they are not equivalent to the original formulations. Two numerical examples illustrate that the approach proposed by Zaghian et al. (2018) provides approximation problems and not reformulations.

O gerenciamento da mudança de engenharia (ECM) é uma prática comum em empresas globalizadas, mas que carece de relatos da sua disseminação em setores mais tradicionais. O presente artigo descreve o desafio encontrado na “tropicalização” de uma tela de centrífuga utilizada na fabricação do açúcar, e os aprendizados daí decorrentes para a mudança de um projeto norte-americano e sua adaptação à realidade brasileira. O método utilizado é o da pesquisa-ação e a abordagem qualitativa. O trabalho contribui ao demonstrar uma aplicação em que o ECM foi adaptado se alinhando ao modelo de negócio da empresa. Os formulários previstos no ECM não burocratizaram o processo, mas passaram servir para formalizar as vendas, engenharia e manufatura do produto. Os resultados conseguidos com o ECM na tropicalização do produto fez com que a técnica passasse a ser utilizada em todas as linhas de produto da empresa no Brasil. Dado que a importação de tecnologias é comum na indústria brasileira, sugere-se que sistemáticas similares aqui apresentada possam ser utilizadas para tropicalizar tecnologias que aumentem sua competitividade.