Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Using Boundary Objects to Stimulate Transformational Thinking: Storm Resilience for the Port of Providence, Rhode Island (USA)

Like many coastal ports around the world, Rhode Island’s Port of Providence in USA is at risk for climate-related natural hazards, such as catastrophic storm surges and significant sea level rise (0.5–2.0 m), over the next century. To combat such events, communities may eventually adopt so-called “transformational adaptation” strategies, like the construction of major new infrastructure, the reorganization of vulnerable systems, or changes in their locations. Such strategies can take decades or more to plan, design, find consensus around, fund, and ultimately implement. Before any meaningful decisions can be made, however, a shared understanding of risks, consequences, and options must be generated and allowed to percolate through the decision-making systems. This paper presents results from a pre-planning exercise that utilized “boundary objects” to engage the Port of Providence\u27s stakeholders in an early dialogue about the transformational approaches to hazard–risk mitigation. The research team piloted the following three boundary objects as a means to initiate meaningful dialogue about long-term storm resilience challenges amongst key stakeholders of this exposed seaport system: (1) a storm scenario with local-scale visualizations, (2) three long-term transformational resilience concepts, and (3) a decision support tool called Wecision. The team tested these boundary objects in a workshop setting with 30 port business owners and policy makers, and found them to be an effective catalyst to generate a robust dialogue around a very challenging topic

Multi-Level Multi-Objective Programming and Optimization for Integrated Air Defense System Disruption

The U.S. military\u27s ability to project military force is being challenged. This research develops and demonstrates the application of three respective sensor location, relocation, and network intrusion models to provide the mathematical basis for the strategic engagement of emerging technologically advanced, highly-mobile, Integrated Air Defense Systems. First, we propose a bilevel mathematical programming model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder\u27s penetration path through a defended region. Next, we formulate a multi-objective, bilevel optimization model to relocate surviving sensors to maximize an intruder\u27s minimal expected exposure to traverse a defended border region, minimize the maximum sensor relocation time, and minimize the total number of sensors requiring relocation. Lastly, we present a trilevel, attacker-defender-attacker formulation for the heterogeneous sensor network intrusion problem to optimally incapacitate a subset of the defender\u27s sensors and degrade a subset of the defender\u27s network to ultimately determine the attacker\u27s optimal penetration path through a defended network

Efficiency of different spatial and temporal strategies for reducing vertebrate pest populations

Understanding effectiveness of control strategies of pest species is fundamental for planning efficient and cost-effective management programs. In addition to culling rates, there are many potential factors that can determine efficiency of different management strategies, including demographic processes such as immigration rates, birth dynamics, and spatial ecology. We developed a stochastic, data-based simulation model of feral swine population dynamics which accounted for social dynamics in space. We tested the impacts of different spatio-temporal management strategies (i.e., culling rates, timing of culling during the year, spatial pattern of culling and strength of a barrier to immigration) on population response and efficiency. The spatial culling strategy dramatically impacted efficiency of control – using zonation required removal of fewer pigs (up to 46% less) to achieve similar reductions compared with other spatial strategies. Also, our spatially-explicit model predicted that lower culling intensities could be used to achieve population reductions when zonation was applied relative to predictions from harvesting theory based on simple logistic models. As culling intensity increased (≥50% of target population annually) and the target population reached low density (\u3c5% of original density), effects of spatial strategy became less pronounced relative to immigration barrier effects. Lastly, for the same level of moderate culling effort, prioritization of culling during the low-birthing period generally resulted in faster population reduction to near zero abundance relative to prioritization during the high-birthing period, or spreading the work over a year period, but the significance of this effect depended on the spatial culling strategy and culling intensity. Our results imply that continually updating knowledge of current abundance during management may not only be important for determining culling quotas, but also for updating and optimizing management strategies. When the management goal is maximum population control, consideration of birth and spatial dynamics can increase return on management effort and bring to light management inefficiencies

The need of adding a safety barrier to water cooled nuclear reactors

The present paper deals with the proposal of an additional safety barrier for the class of large (1000 MWe or more) Light Water Reactors (LWR) now in operation, in construction, or under design. Emphasis is given to the motivations or the needs for the barrier. Two main parts of the paper can be distinguished. The following topics are discussed in the former part: (a) the weakness of the barrier constituted by the current design of nuclear fuel; (b) the continuously increasing complexity of the system, with main reference to the Instrumentation and Control (I&C); (c) the role that the Large Break Loss of Coolant Accident (LBLOCA) had for arriving at the current layout of the Reactor Coolant System (RCS). Furthermore avoiding the severe accidents in 1979, 1987 and 2011, is at the basis of the proposal. In the latter part, the elements of the proposed technological safety barrier are discussed: the As-Low-As-Reasonably-Achievable (ALARA) principle, the Best Estimate Plus Uncertainty (BEPU) approach, the Extended Safety Margin Detection (E-SMD) hardware, the Emergency Rescue Team (ERT) strategy (or a virtual entity for the reactor) and the Independent Assessment (IA) concept. The additional safety barrier, although not demonstrated in the paper, is expected to reduce for a factor in the range 10-1000 the probability of core melt and to have a cost in the order of 1% the cost of a nuclear reactor unit

The technological challenge for current generation nuclear reactors

The present paper deals with the proposal of an additional safety barrier for the class of large (1000 MWe or more) Light Water Reactors (LWR) now in operation, in construction, or under design. Emphasis is given to the motivations or the needs for the barrier. Two main parts of the paper can be distinguished. The following topics are discussed in the former part (section 2): (a) the weakness of the barrier constituted by the current design of nuclear fuel; (b) the continuously increasing complexity of the system, with main reference to the Instrumentation and Control (I&C); (c) the role that the Large Break Loss of Coolant Accident (LBLOCA) had for arriving at the current layout of the Reactor Coolant System (RCS). Furthermore avoiding the severe accidents in 1979, 1987 and 2011, is at the basis of the proposal. In the latter part (sections 3 and 4), the elements of the proposed technological safety barrier are discussed: the As-Low-As-Reasonably-Achievable (ALARA) principle, the Best Estimate Plus Uncertainty (BEPU) approach, the Extended Safety Margin Detection (E-SMD) hardware, the Emergency Rescue Team (ERT) strategy (or a virtual entity for the reactor) and the Independent Assessment (IA) concept. The additional safety barrier, although not demonstrated in the paper, is expected to reduce for a factor in the range 10–1000 the probability of core melt and to have a cost in the order of 1% the cost of a nuclear reactor unit

A two-level facility location and sizing problem for maximal coverage

This paper presents a two-stage hierarchical location problem for systems where the lower level facilities act as the first points contact for the customers while the upper level facilities act as suppliers of the lower level facilities that either serve them or provide advanced services to customers. Furthermore, more recent and realistic coverage constructs such as gradual and cooperative covering are included in our setting. Although our problem can be applicable in various settings, the most fitting application is in wireless telecommunication networks to determine the location of base stations and mobile switching centers. We have developed two competing formulations for the problem, each of which involve nonlinear components that are difficult to deal with. We then develop their respective linearizations and tested their performances. These formulations are solved by commercial optimizers for a set of reasonably large problem instances and it is found that majority of the problems can be solved within a maximum of 10% optimality gap within a short time