3,014 research outputs found
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
Recommended from our members
Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Depannage and other maintenance strategies for transportation fleets
This paper addresses the problem of designing a depannage service network for freight transportation fleets in order to fill up an evident gap in Operations Management literature. As a matter of
fact, breakdowns have a significant impact on service level and an efficient and effective assistance service is needed in order to guarantee competitive advantage. Aiming at laying the foundations for further research on modelling and solving the considered problem, a literature review is performed for identifying analogies with other problems, already approached in the past, and stating the problem. Finally, parameters, decision variables and objective function that should be considered for modelling and solving the problem are proposed, trying to stimulate the discussion on a new research theme and, more specifically, on the integration of decisions at strategic, tactical and operational level into a systemic model
Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems
The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users' clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds.Peer ReviewedPostprint (author's final draft
COMMUNITY SERVICE MANAGEMENT AND PLANNING PROGRAMS IN OKLAHOMA: WHY, WHAT AND HOW DELIVERED
Community/Rural/Urban Development,
TDMP-Reliable Target Driven and Mobility Prediction based Routing Protocol in Complex VANET
Vehicle-to-everything (V2X) communication in the vehicular ad hoc network
(VANET), an infrastructure-free mechanism, has emerged as a crucial component
in the advanced Intelligent Transport System (ITS) for special information
transmission and inter-vehicular communications. One of the main research
challenges in VANET is the design and implementation of network routing
protocols which manage to trigger V2X communication with the reliable
end-to-end connectivity and efficient packet transmission. The organically
changing nature of road transport vehicles poses a significant threat to VANET
with respect to the accuracy and reliability of packet delivery. Therefore, a
position-based routing protocol tends to be the predominant method in VANET as
they overcome rapid changes in vehicle movements effectively. However, existing
routing protocols have some limitations such as (i) inaccurate in high dynamic
network topology, (ii) defective link-state estimation (iii) poor movement
prediction in heterogeneous road layouts. In this paper, a target-driven and
mobility prediction (TDMP) based routing protocol is therefore developed for
high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow
and diverse road layouts in VANET. The primary idea in TDMP is that the
destination target of a driver is included in the mobility prediction to assist
the implementation of the routing protocol. Compared to existing geographic
routing protocols which mainly greedily forward the packet to the next-hop
based on its current position and partial road layout, TDMP is developed to
enhance the packet transmission with the consideration of the estimation of
inter-vehicles link status, and the prediction of vehicle positions dynamically
in fluctuant mobility and global road layout.Comment: 35 pages,16 Figure
The Effect of Mapping Technology on Fire-Based EMS Response Times in Santa Clara County
The goal of this research is to determine whether map technology and signal preemption technology contribute to lowering fire-based EMS response times in Santa Clara County. The research uses benchmarking among fire departments in Santa Clara County, the Bay Area, and large Western urban fire departments to determine which factors contribute to the success in meeting contracted response times. Findings from this evaluation were used to create recommendations for fire service leadership regarding resource use along the continuum of fire department-based EMS response to 9-1-1 calls for medical service
Recommended from our members
Optimizing emergency preparedness and resource utilization in mass-casualty incidents
This paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. T he goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIP-based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times
- …