About

Darin England joined the Industrial and Systems Engineering Department at the University of Minnesota as an Assistant Teaching Professor in 2015. He teaches courses covering Probability and Statistics, Simulation, Decision Analysis, and Analytics, and provides academic advising for undergraduate students. With over 20 years of experience across the energy, transportation, health care, and hospitality industries, he has worked as a field engineer and research scientist. His research interests lie at the intersection of Operations Research and Computer Science, where he has utilized mathematical programming, machine learning, simulation, and forecasting methodologies to assist companies in making better decisions. Dr. England earned his PhD in Computer Science from the University of Minnesota, and holds a B.S. in Industrial Engineering from Purdue University and an M.S. in Operations Research from Georgia Institute of Technology.

Research topics

• Computer Science
• Machine Learning
• Engineering
• Operations research
• Mathematics
• Nursing
• Medicine
• Medical education
• Transport engineering
• Family medicine

Selected publications

• Urban fire station location planning using predicted demand and service quality index

  International Journal of Data Science and Analytics · 2022 · 15 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Operations research
  PublisherDOI

• Urban Fire Station Location Planning using Predicted Demand and Service Quality Index

  arXiv (Cornell University) · 2021-09-05

  preprintOpen accessSenior author

  In this article, we propose a systematic approach for fire station location planning. We develop machine learning models, based on Random Forest and Extreme Gradient Boosting, for demand prediction and utilize the models further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We further develop a two-stage stochastic optimization model to characterize the confidence in our decision outcome. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 80% and false positive rate of 20% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.

  PublisherOA PDFDOI

• Urban Fire Station Location Planning: A Systematic Approach using Predicted Demand and Service Quality Index.

  arXiv (Cornell University) · 2021 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Operations research

  In this article, we propose a systematic approach for fire station location planning. We develop a machine learning model, based on Random Forest, for demand prediction and utilize the model further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 70% and false positive rate of 22% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.

  Publisher

• RESPECT: Radiology Employees Striving for Productive and Effective Communication

  Radiographics · 2020 · 4 citations

  • Medicine
  • Medical education
  • Family medicine

  RSNA, 2020.

  PublisherDOI

• Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence

  Medical Decision Making · 2011-04-29 · 42 citations

  article

  BACKGROUND: Statins are an important part of the treatment plan for patients with type 2 diabetes. However, patients who are prescribed statins often take less than the prescribed amount or stop taking the drug altogether. This suboptimal adherence may decrease the benefit of statin initiation. OBJECTIVE: To estimate the influence of adherence on the optimal timing of statin initiation for patients with type 2 diabetes. METHOD: The authors use a Markov decision process (MDP) model to optimize the treatment decision for patients with type 2 diabetes. Their model incorporates a Markov model linking adherence to treatment effectiveness and long-term health outcomes. They determine the optimal time of statin initiation that minimizes expected costs and maximizes expected quality-adjusted life years (QALYs). RESULTS: In the long run, approximately 25% of patients remain highly adherent to statins. Based on the MDP model, generic statins lower costs in men and result in a small increase in costs in women relative to no treatment. Patients are able to noticeably increase their expected QALYs by 0.5 to 2 years depending on the level of adherence. CONCLUSIONS: Adherence-improving interventions can increase expected QALYs by as much as 1.5 years. Given suboptimal adherence to statins, it is optimal to delay the start time for statins; however, changing the start time alone does not lead to significant changes in costs or QALYs.

  PublisherDOI

• A Robust Spanning Tree Topology for Data Collection and Dissemination in Distributed Environments

  IEEE Transactions on Parallel and Distributed Systems · 2007-04-30 · 55 citations

  article1st authorCorresponding

  Large-scale distributed applications are subject to frequent disruptions due to resource contention and failure. Such disruptions are inherently unpredictable and, therefore, robustness is a desirable property for the distributed operating environment. In this work, we describe and evaluate a robust topology for applications that operate on a spanning tree overlay network. Unlike previous work that is adaptive or reactive in nature, we take a proactive approach to robustness. The topology itself is able to simultaneously withstand disturbances and exhibit good performance. We present both centralized and distributed algorithms to construct the topology, and then demonstrate its effectiveness through analysis and simulation of two classes of distributed applications: Data collection in sensor networks and data dissemination in divisible load scheduling. The results show that our robust spanning trees achieve a desirable trade-off for two opposing metrics where traditional forms of spanning trees do not. In particular, the trees generated by our algorithms exhibit both resilience to data loss and low power consumption for sensor networks. When used as the overlay network for divisible load scheduling, they display both robustness to link congestion and low values for the makespan of the schedule

  PublisherDOI

• Robust design for distributed computing systems

  2006-01-01 · 2 citations

  articleSenior author

  Robust systems have the ability to maintain performance under a wide variety of operating conditions. Although the notion of robustness is very intuitive and is generally considered desirable, there exist no widely used quantitative metrics for this property. In the first part of this work we define robustness in terms of its impact on performance and present a new technique for measuring and characterizing the robustness of a system to a specific disturbance. Unlike previous work, our approach does not require the use of sophisticated mathematical models. To show its efficacy, the metric is applied to three different scheduling problems. In the second part of this work we apply the methodology of dynamic programming to effect robust policies for making resource management decisions in the face of uncertainty. We apply this methodology in a novel way to new problems that are posed by the emergence of on-demand computing. We view the problem from the perspective of a software service provider whose objective is to minimize the cost of leasing resources and maintain an adequate quality of service. By using this methodology, service providers can make good leasing decisions in the face of such uncertainties as random demand for the service and random execution times of service requests. The resulting policies reduce the cost of hosting a service and significantly reduce its variance, an indication of robustness. In the third part of this work we develop and evaluate a new robust network topology for applications that operate on a spanning tree overlay network. Unlike previous work that is adaptive or reactive in nature, we take a proactive approach: the topology itself is able to simultaneously withstand disturbances and exhibit good performance. We present both centralized and distributed tree construction algorithms and evaluate their effectiveness through analysis and simulation of two classes of distributed applications: data collection in sensor networks, and data dissemination in divisible load scheduling. The results show that our robust spanning trees achieve a desirable trade-off for opposing performance metrics where more commonly used forms of spanning trees do not.

  Publisher

• A Resource Leasing Policy for on-Demand Computing

  The International Journal of High Performance Computing Applications · 2006-02-01 · 2 citations

  article1st author

  Leasing computational resources for on-demand computing is now a viable option for providers of network services. Temporary spikes or lulls in demand for a service can be accommodated by flexible leasing arrangements. From the service provider's perspective, the problem is how many resources to lease and for how long. In this paper we formulate and solve the resource leasing problem for the case of a single service. The objective is to minimize the cost of leasing resources while still maintaining an adequate quality of service, which we measure by the average wait time of requests. Demand for the service and execution times of service requests are modeled as random variables. The problem is formulated as a continuous-time, infinite-horizon Markov decision problem. We use the dynamic programming method of value iteration for its solution and we characterize the resulting optimal cost function. We find that the cost of providing a service is convex-like in the number of resources leased and nondecreasing in the number of requests in the system. Close examination of the optimal cost function shows that the cost of providing a service is more sensitive to underdeployment than to overdeployment. Thus, when demand for the service is known to exist, but is unpredictable, it is better to lease more resources than fewer resources.

  PublisherDOI

• A Stochastic Control Model for Deployment of Dynamic Grid Services

  2005-04-06 · 12 citations

  article1st authorCorresponding

  We introduce a formal model for deployment and hosting of a dynamic grid service wherein the service provider must pay a resource provider for the use of computational resources. Our model produces policies that balance the number of required resources with the desire to keep the cost of hosting the service to a minimum. The two components of cost that we consider are the deployment cost and the cost to keep the service active, which we view as a lease. We cast the problem in a dynamic programming framework and we are able to show that the model makes good leasing decisions in the face of such uncertainties as random demand for the service and random execution times of service requests. The results show that the policies obtained from the model reduce the cost of hosting a service and significantly reduce the variance of that cost.

  PublisherDOI

• A new metric for robustness with application to job scheduling

  2005-10-24 · 37 citations

  article1st authorCorresponding

  Scheduling strategies for parallel and distributed computing have mostly been oriented toward performance, while striving to achieve some notion of fairness. With the increase in size, complexity, and heterogeneity of today's computing environments, we argue that, in addition to performance metrics, scheduling algorithms should be designed for robustness. That is, they should have the ability to maintain performance under a wide variety of operating conditions. Although robustness is easy to define, there are no widely used metrics for this property. To this end, we present a methodology for characterizing and measuring the robustness of a system to a specific disturbance. The methodology is easily applied to many types of computing systems and it does not require sophisticated mathematical models. To illustrate its use, we show three applications of our technique to job scheduling; one supporting a previous result with respect to backfilling, one examining overload control in a streaming video server, and one comparing two different scheduling strategies for a distributed network service. The last example also demonstrates how consideration of robustness leads to better system design as we were able to devise a new and effective scheduling heuristic.

  PublisherDOI

Frequent coauthors

• Jon Weissman

  University of Minnesota

  13 shared

• Murat Kurt

  9 shared

• Jennifer Mason

  Abu Dhabi National Oil (United Arab Emirates)

  9 shared

• Steven A. Smith

  American Political Science Association

  9 shared

• A.G. de Kok

  9 shared

• Nilay D. Shah

  9 shared

• Andrew Heger

  New York City Fire Department

  3 shared

• Arnab Dey

  Twin Cities Orthopedics

  3 shared

Awards & honors

• 2021 Russell J. Penrose Excellence in Teaching Award