The Experts below are selected from a list of 19335 Experts worldwide ranked by ideXlab platform
Emmanuel Mouche - One of the best experts on this subject based on the ideXlab platform.
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1-D steady state runoff production in light of Queuing Theory: Heterogeneity, connectivity, and scale
Water Resources Research, 2013Co-Authors: Marie-alice Harel, Emmanuel MoucheAbstract:[1] We used the frameworks of Queuing Theory and connectivity to study the runoff generated under constant rainfall on a one-dimensional slope with randomly distributed infiltrability. The equivalence between the stationary runoff-runon equation and the customers waiting time in a single server queue provides a theoretical link between the statistical description of infiltrability and that of runoff flow rate. Five distributions of infiltrability, representing soil heterogeneities at different scales, are considered: four uncorrelated (exponential, bimodal, lognormal, uniform) and one autocorrelated (lognormal, with or without a nugget). The existing theoretical results are adapted to the hydrological framework for the exponential case, and new theoretical developments are proposed for the bimodal law. Numerical simulations validate these results and improve our understanding of runoff-runon for all of the distributions. The quantities describing runoff generation (runoff one-point statistics) and its organization into patterns (patterns statistics and connectivity) are studied as functions of rainfall rate. The variables describing the wet areas are also compared to those describing the rainfall excess areas, i.e., the areas where rainfall exceeds infiltrability. Preliminary results concerning the structural and functional connectivity functions are provided, as well as a discussion about the origin of scale effects in such a system. We suggest that the upslope no-flow boundary condition may be responsible for the dependence of the runoff coefficient on the scale of observation. Queuing Theory appears to be a promising framework for runoff-runon modeling and hydrological connectivity problems. Citation: Harel, M.-A., and E. Mouche (2013), 1-D steady state runoff production in light of Queuing Theory: Heterogeneity, connectivity, and scale, Water Resour. Res., 49, 7973-7991,
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1 d steady state runoff production in light of Queuing Theory heterogeneity connectivity and scale
Water Resources Research, 2013Co-Authors: Marie-alice Harel, Emmanuel MoucheAbstract:[1] We used the frameworks of Queuing Theory and connectivity to study the runoff generated under constant rainfall on a one-dimensional slope with randomly distributed infiltrability. The equivalence between the stationary runoff-runon equation and the customers waiting time in a single server queue provides a theoretical link between the statistical description of infiltrability and that of runoff flow rate. Five distributions of infiltrability, representing soil heterogeneities at different scales, are considered: four uncorrelated (exponential, bimodal, lognormal, uniform) and one autocorrelated (lognormal, with or without a nugget). The existing theoretical results are adapted to the hydrological framework for the exponential case, and new theoretical developments are proposed for the bimodal law. Numerical simulations validate these results and improve our understanding of runoff-runon for all of the distributions. The quantities describing runoff generation (runoff one-point statistics) and its organization into patterns (patterns statistics and connectivity) are studied as functions of rainfall rate. The variables describing the wet areas are also compared to those describing the rainfall excess areas, i.e., the areas where rainfall exceeds infiltrability. Preliminary results concerning the structural and functional connectivity functions are provided, as well as a discussion about the origin of scale effects in such a system. We suggest that the upslope no-flow boundary condition may be responsible for the dependence of the runoff coefficient on the scale of observation. Queuing Theory appears to be a promising framework for runoff-runon modeling and hydrological connectivity problems.
Laura Mazzoldi - One of the best experts on this subject based on the ideXlab platform.
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energy demand in production systems a Queuing Theory perspective
International Journal of Production Economics, 2015Co-Authors: Lucio Zavanella, Simone Zanoni, Ivan Ferretti, Laura MazzoldiAbstract:Abstract Production systems are usually organised in departments consisting of machines, each one characterised by specific patterns of energy demand over time. This work proposes an analytical approach, based on the application of Queuing Theory, to model the power request and the consequent energy use in a production system. Despite the industrial context addressed, the model may be easily applied to small units (e.g., civil buildings) and other energy sources (e.g., thermal energy), thus giving more relevance to the approach proposed. The model can efficiently support green-field cases, particularly avoiding or integrating the traditional assumptions, such as load and coincidence factors (usually employed to determine the contractual electrical power), which provide a static view of the power needs of the system. In fact, the proposed Queuing model considers the arrivals as the statistical distribution of the switch-on of machines and service completions as the statistical distribution of the processing times at the machines themselves, thus offering a dynamic view of the power loads. Therefore, the model may be helpful while assessing the contract with the energy supplier or planning the production schedule of plants with significant energy-related constraints, including plant services. A numerical example shows the application of the proposed approach and its results are compared to those determined by the traditional design methodology.
Marie-alice Harel - One of the best experts on this subject based on the ideXlab platform.
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1-D steady state runoff production in light of Queuing Theory: Heterogeneity, connectivity, and scale
Water Resources Research, 2013Co-Authors: Marie-alice Harel, Emmanuel MoucheAbstract:[1] We used the frameworks of Queuing Theory and connectivity to study the runoff generated under constant rainfall on a one-dimensional slope with randomly distributed infiltrability. The equivalence between the stationary runoff-runon equation and the customers waiting time in a single server queue provides a theoretical link between the statistical description of infiltrability and that of runoff flow rate. Five distributions of infiltrability, representing soil heterogeneities at different scales, are considered: four uncorrelated (exponential, bimodal, lognormal, uniform) and one autocorrelated (lognormal, with or without a nugget). The existing theoretical results are adapted to the hydrological framework for the exponential case, and new theoretical developments are proposed for the bimodal law. Numerical simulations validate these results and improve our understanding of runoff-runon for all of the distributions. The quantities describing runoff generation (runoff one-point statistics) and its organization into patterns (patterns statistics and connectivity) are studied as functions of rainfall rate. The variables describing the wet areas are also compared to those describing the rainfall excess areas, i.e., the areas where rainfall exceeds infiltrability. Preliminary results concerning the structural and functional connectivity functions are provided, as well as a discussion about the origin of scale effects in such a system. We suggest that the upslope no-flow boundary condition may be responsible for the dependence of the runoff coefficient on the scale of observation. Queuing Theory appears to be a promising framework for runoff-runon modeling and hydrological connectivity problems. Citation: Harel, M.-A., and E. Mouche (2013), 1-D steady state runoff production in light of Queuing Theory: Heterogeneity, connectivity, and scale, Water Resour. Res., 49, 7973-7991,
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1 d steady state runoff production in light of Queuing Theory heterogeneity connectivity and scale
Water Resources Research, 2013Co-Authors: Marie-alice Harel, Emmanuel MoucheAbstract:[1] We used the frameworks of Queuing Theory and connectivity to study the runoff generated under constant rainfall on a one-dimensional slope with randomly distributed infiltrability. The equivalence between the stationary runoff-runon equation and the customers waiting time in a single server queue provides a theoretical link between the statistical description of infiltrability and that of runoff flow rate. Five distributions of infiltrability, representing soil heterogeneities at different scales, are considered: four uncorrelated (exponential, bimodal, lognormal, uniform) and one autocorrelated (lognormal, with or without a nugget). The existing theoretical results are adapted to the hydrological framework for the exponential case, and new theoretical developments are proposed for the bimodal law. Numerical simulations validate these results and improve our understanding of runoff-runon for all of the distributions. The quantities describing runoff generation (runoff one-point statistics) and its organization into patterns (patterns statistics and connectivity) are studied as functions of rainfall rate. The variables describing the wet areas are also compared to those describing the rainfall excess areas, i.e., the areas where rainfall exceeds infiltrability. Preliminary results concerning the structural and functional connectivity functions are provided, as well as a discussion about the origin of scale effects in such a system. We suggest that the upslope no-flow boundary condition may be responsible for the dependence of the runoff coefficient on the scale of observation. Queuing Theory appears to be a promising framework for runoff-runon modeling and hydrological connectivity problems.
Kannan R Mutharasan - One of the best experts on this subject based on the ideXlab platform.
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buffer or suffer redesigning heart failure postdischarge clinic using Queuing Theory
Circulation-cardiovascular Quality and Outcomes, 2018Co-Authors: Kannan R Mutharasan, Faraz S Ahmad, Itai Gurvich, Hannah Alphs Jackson, Jan A Van Mieghem, Clyde W YancyAbstract:Timely follow-up in clinic after heart failure hospitalization represents an evidence-based intervention associated with reduced rehospitalization.1 Major cardiovascular societies endorse a 7-day follow-up visit as an appropriate target for quality.2 Yet the rate of scheduled follow-up visits remains relatively low, at ≈65% in 2012 by registry data.3 Even more striking is the rate of arrived follow-up visits: 30% in 7 days.3 This represents a substantial missed opportunity to address and a likely explanation for ongoing avoidable readmissions. We took an unconventional approach to improving clinic scheduling policies by collaborating with our colleagues at the Northwestern University Kellogg School of Management to implement Queuing Theory as a novel approach to address a previously unyielding problem. In 2015, as part of a multidisciplinary intervention to improve outcomes for hospitalized heart failure patients, we systematically identified all patients within our hospital at risk for heart failure-related readmissions through a daily enterprise data warehouse screen4; developed a multidisciplinary bridge and transition team to engage patients during the index hospitalization; and then deployed Queuing Theory to first assess and then improve clinic follow-up. Queuing Theory is the mathematical study of waiting times.5,6 With roots in the telecommunications field, it has widespread applications in several processes such as understanding supermarket lines and managing factory inventory. A particularly powerful insight arising from Queuing Theory is the notion that extra capacity, or a capacity buffer, is necessary to ensure system performance when variable demand arises, such as for a hospital discharge clinic. We opted to use Queuing Theory to analyze hospital discharge load and understand the capacity needed in clinic to reduce wait times and improve access. Here, we provide our mathematical analysis based on real-world practice; the results of our intervention; and an online calculator (http://www.hfresearch.org) for other …
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abstract 161 heart failure care transitions a Queuing Theory approach to match variable hospital discharge rate with outpatient clinic capacity
Circulation-cardiovascular Quality and Outcomes, 2016Co-Authors: Kannan R Mutharasan, Itai Gurvich, Hannah Alphs Jackson, Preeti Kansal, Michael Abecassis, Allen S Anderson, Corrine Benacka, Jillian Berry A Jaeker, Charles J Davidson, Daniel NavarroAbstract:Background: Heart failure (HF) readmissions remain a major driver of cost and health care utilization. Timely follow-up of patients post-discharge represents an evidence-based intervention proven to reduce readmission rates. A previously unexplored characteristic of hospital discharges is variability in discharge caseload. This variability thwarts the timeliness of follow-up, negates the benefit of transition care planning and may lead to a higher risk of HF readmissions. Queuing Theory is the mathematical study of waiting times. We opted to use Queuing Theory to determine if caseload can be determined more precisely in a manner that sufficiently accommodates HF discharge variability. Objective: To analyze the impact of hospital discharge rate variability on outpatient clinic capacity needs using HF hospitalization discharge data and operations management approaches. Methods: Higher risk hospitalizations requiring active transitional care heart failure management were detected using an enterprise data warehouse-supported process over the study period. Queuing Theory approaches were used to model the impact of HF discharge clinic capacity on wait time to an appointment. Discharge clinic was modeled as a single 7-day follow-up appointment, with an acceptable scheduling window of 5 to 9 days post-discharge. Results: During the study period of 100 days, 566 HF discharges were made, for a median of 5.66 discharges daily, or 39.6 discharges weekly. The distribution of daily discharges was skewed rightward (mode = 3, range = 0 to 18, standard deviation = 3.3, coefficient of variation = 0.58). Current clinic design: Providing one discharge slot for every hospital discharge (100% utilization) leads to an average wait of 18.3 days prior to an appointment, with only 31.9% of appointments scheduled within 7 days, and 38.9% of appointments scheduled within 9 days. Clinic re-design (Queuing Theory): Providing five extra discharge appointment slots per week (88% utilization or 13.6% excess capacity) reduces the expected waiting period to 1.1 days, with 99.8% of patients seen within 7 days, and virtually all patients seen within 9 days of discharge. Conclusions: Deployment of Queuing Theory allows for a more precise quantification of needed clinical capacity to accomplish appropriate HF follow-up with a reasonable degree of certainty. Our simplified model demonstrates that variability in hospital discharge rates leads to excessive clinic wait times in the absence of a modest capacity buffer and consequently exposes patients to a higher risk of HF readmission. We show using single center HF discharge data that a 10-15% increase in capacity is needed to ensure an adequate follow-up service level. Ongoing process of care work will demonstrate if optimization of clinic load yields a significant reduction in HF readmissions.
Bruce Gurd - One of the best experts on this subject based on the ideXlab platform.
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quantity of material handling equipment a Queuing Theory based approach
Robotics and Computer-integrated Manufacturing, 2009Co-Authors: Dhamodharan Raman, Sev V Nagalingam, Bruce GurdAbstract:In this paper, we discuss the development of a two step analytical approach to determine the quantity of material handling equipment (MHE) required for effective handling of products among facilities. In the first step, a preliminary solution is obtained by considering the time required for loading and unloading of products, loaded travelling, empty travelling and breakdown of MHE. A detailed model, which integrates both operational and cost performance factors such as utilisation of MHE, work-in-process at the MHS and life-cycle cost, is then utilised to rank alternatives that are generated from the preliminary solution. The stochastic nature of a manufacturing system, which is not adequately addressed in the literature, is best modelled using Queuing Theory. An illustrative problem is given, and it is shown that for all the considered problems our approach outperforms the existing methods. The influence of various other factors including the operational characteristics of processing facilities, layout design, maintenance function, MHE speed and batch size in selection of the quantity of MHE is also demonstrated. Thus we show the significance of our proposed approach and its capability to support an integrated decision making process.
