7 research outputs found

    Assessing strategies for achieving environmentally sustainable food systems using robust optimization

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    [EMBARGOED UNTIL 5/1/2024] With growing populations and affluence, many organizations predict that food demand will increase, which presents considerable challenges to achieving economic, environmental, and social sustainability 1. At the same time, more people are living in urban environments. In 2018 in the United States (U.S.) 82 percent ( percent) of the population lived in urban areas, with an anticipated increase to 89 percent by 2050 1. Increasing food production within urban areas could alleviate pressure to increase conventionally-grown agricultural commodities and the foods produced from them. Further, many entities promote localizing or regionalizing food systems in support of economic, social, and environmental sustainability 2. For these reasons, it is important to determine the extent to which localized food systems can be realized, including urban agricultural activities, changing food diets, and agricultural conservation practices given the nutritional needs of the population as well as the corresponding land available. In this research, we first used non-robust (average yield) and robust (varying yield) optimization techniques to find the minimum radius required from the center of Chicago, Illinois, accounting for differences in land area by type, to meet the population's nutritional needs given yield data for conventional and urban agricultural products. Then, we extended the optimization models to find if shifting from the current American diet to other diets would help with localizing food systems: What would be the benefits and costs in terms of nutrient adequacy and environmental footprints associated with each diet, and to what extent would potential future climate change impact on crop yields change the results? Finally, we assessed the impacts of an agricultural conservation practice called prairie strips in which we allocate some percent of corn and soybean fields to prairie strips to determine how this strategy would change the ability to localize food system among all diets/scenarios and what would be additional environmental changes associated with this practice.Includes bibliographical references

    An optimization model to address overcrowding in emergency departments using patient transfer : [data]

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    Supplementary material and data for the article: Zeynab Oveysi, Ronald G. McGarvey, Kangwon Seo, "An Optimization Model to Address Overcrowding in Emergency Departments Using Patient Transfer", Advances in Operations Research, vol. 2021, Article ID 7120291, 11 pages, 2021. https://doi.org/10.1155/2021/712029

    An optimization model to address overcrowding in emergency departments using patient transfer

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    Overcrowding of emergency departments (EDs) is a problem that affected many hospitals especially during the response to emergency situations such as pandemics or disasters. Transferring nonemergency patients is one approach that can be utilized to address ED overcrowding. We propose a novel mixed-integer nonlinear programming (MINLP) model that explicitly considers queueing effects to address overcrowding in a network of EDs, via a combination of two decisions: modifying service capacity to EDs and transferring patients between EDs. Computational testing is performed using a Design of Experiments to determine the sensitivity of the MINLP solutions to changes in the various input parameters. Additional computational testing examines the effect of ED size on the number of transfers occurring in the system, identifying an efficient frontier for the tradeoff between system cost (measured as a function of the service capacity and the number of patient transfers) and the systemwide average expected waiting time. Taken together, these results suggest that our optimization model can identify a range of efficient alternatives for healthcare systems designing a network of EDs across multiple hospitals

    A Novel Best-Worst-Method two-stage DEA model considering decision makers' preferences: An application in bank branches evaluation

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    Data Envelopment Analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the " black box " approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, Best Worst Method (BWM) is used for incorporating decision maker (DM) preferences in two-stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on decision maker's (DM) judgment. Second, generated weights are imposed on two-stage DEA model as additional constraints and a novel bi-objective BWM-two stage DEA model is introduced. Finally, the proposed bi-objective BWM-two stage DEA model is solved using min-max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages " production " and " profitability " and efficiency of branches are calculated in each stage. According to the efficiencies of each sub-stage, branches are divided to four groups and recommendations are made for each group

    A Mixed Integer Network DEA with Shared Inputs and Undesirable Outputs for Performance Evaluation: Efficiency Measurement of Bank Branches

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    Conventional DEA performs like a " black box " and provides no information about sub-processes. In some cases, such as banks, providing services is made up of interactive and interdependent processes. Also, in real world applications, inputs could be shared among these sub-processes. Moreover, due to the characteristics of some variables, such as number of employees, only integer values could be assigned to them. Hence, to address these shortcomings, in this study, a mixed integer network DEA (MI-NDEA) with shared inputs and undesirable outputs has been proposed to evaluate the efficiency of decision making units. The proposed model considers integer values for some of the input variables. Also, it assumes that some inputs are shared among different stages of the production process. To illustrate the capability of the model, the efficiency of " Internet banking " , " profitability " , " production " and " overall " performance of a set of bank branches have been evaluated and results are discussed. The results indicate that the mean of overall efficiency for all branches is high. However, some branches are not efficient enough in the " Production " stage or " Profitability " stage. To identify the source of inefficiency in such branches, projection values have been calculated and recommendations have been made for policy makers