The median routing problem for simultaneous planning of emergency response and non-emergency jobs

This paper studies a setting in emergency logistics where emergency responders must also perform a set of known, non-emergency jobs in the network when there are no active emergencies going on. These jobs typically have a preventive function, and allow the responders to use their idle time much more productively than in the current standard. When an emergency occurs, the nearest responder must abandon whatever job he or she is doing and go to the emergency. This leads to the optimisation problem of timetabling jobs and moving responders over a discrete network such that the expected emergency response time remains minimal. Our model, the Median Routing Problem, addresses this complex problem by minimising the expected response time to the next emergency and allowing for re-solving after this. We describe a mixed-integer linear program and a number of increasingly refined heuristics for this problem. We created a large set of benchmark instances, both from real-life case study data and from a generator. On the real-life case study instances, the best performing heuristic finds on average a solution only 3.4% away from optimal in a few seconds. We propose an explanation for the success of this heuristic, with the most pivotal conclusion being the importance of solving the underlying p-Medians Problem

The enriched median routing problem and its usefulness in practice

Emergency response fleets often have to simultaneously perform two types of tasks: (1) urgent tasks requiring immediate action, and (2) non-urgent preventive maintenance tasks that can be scheduled upfront. In Huizing et al. (2020), Huizing et al. proposed the Median Routing Problem (MRP) to optimally schedule agents to a given set of non-urgent tasks, such that the response time for urgent tasks remains minimal. They proposed both an exact MILP-solution and a fast, scalable and accurate heuristic. However, when implementing the MRP-solution in a real-life pilot with a Dutch railway provider, we found that the model needed to be extended by including additional practical objectives and constraints. Therefore, in this paper, we extend the MRP to the so-called Enriched Median Routing Problem (E-MRP), making the model much better aligned with considerations from practice. Accordingly, we extend the MRP-based solutions to the E-MRP. This allows us to compare the performance of our proposed E-MRP solutions to performance obtained in the current operational practice of our partnering railway infrastructure company. We conclude that the E-MRP solution leads to a strong reduction in emergency response times compared to current practice by smartly scheduling the same volumes of non-urgent tasks

EMERGENCY VEHICLES ALLOCATION MODEL FOR URBAN CITY

The Emergency Medical Service (EMS) plays a vital role in any community. The accessibility, distribution, and utilization of the emergency health care services have a great impact on the effectiveness and the efficiency of the EMS. In this study, an algorithm has been devised to optimize choosing the appropriate station location, allocating the right number of emergency vehicles to a station, and directing the critical and non-critical patients to the most suitable hospitals. XPRESS-MP solver engine was used in this study to derive a deterministic mathematical model using mixed integer linear programming. This model is customized for Beirut city. The aim of the study is to design a system that ensures that an emergency vehicle should be available at the time of the incident and the travelling time required must be less than the standard travelling time. The outcome of the study optimizes the number and the location of the EMS stations as well as the number of emergency vehicles allocated to each station. In addition, it optimizes addressing and readdressing the patients to the most suitable hospitals taking into consideration deterioration of the patients’ health conditions while being driven to the hospital. Furthermore, the travel time presented in this study shows the shortest travel distance between any two zones

Online Optimisation of Casualty Processing in Major Incident Response

Recent emergency response operations to Mass Casualty Incidents (MCIs) have been criticised for a lack of coordination, implying that there is clear potential for response operations to be improved and for corresponding benefits in terms of the health and well-being of those affected by such incidents. In this thesis, the use of mathematical modelling, and in particular optimisation, is considered as a means with which to help improve the coordination of MCI response. Upon reviewing the nature of decision making in MCIs and other disaster response operations in practice, this work demonstrates through an in-depth review of the available academic literature that an important problem has yet to be modelled and solved using an optimisation methodology. This thesis involves the development of such a model, identifying an appropriate task scheduling formulation of the decision problem and a number of objective functions corresponding to the goals of the MCI response decision makers. Efficient solution methodologies are developed to allow for solutions to the model, and therefore to the MCI response operation, to be found in a timely manner. Following on from the development of the optimisation model, the dynamic and uncertain nature of the MCI response environment is considered in detail. Highlighting the lack of relevant research considering this important aspect of the problem, the optimisation model is extended to allow for its use in real-time. In order to allow for the utility of the model to be thoroughly examined, a complementary simulation is developed and an interface allowing for its communication with the optimisation model specified. Extensive computational experiments are reported, demonstrating both the danger of developing and applying optimisation models under a set of unrealistic assumptions, and the potential for the model developed in this work to deliver improvements in MCI response operations

Separate and concentrate: accounting for patient complexity in general hospitals

Scholars have recently suggested the reorganization of general hospitals into organizationally separate divisions for routine and non-routine services to overcome operational misalignments between the two types of services. We provide empirical evidence for this proposal from a quality perspective, using over 250,000 patient discharge records from 60 German hospitals across 39 high-mortality disease segments, and focusing on in-hospital mortality as outcome. Disentangling the effects of high absolute and relative hospital volumes in a disease group, our analysis suggests that both routine and complex patients would benefit from a hospital organization with a multi-specialty hub for emergency and non-routine elective services at its core, complemented by organizationally separate disease-focused hospitals-within-hospitals for routine services. We also provide evidence that the hub hospital can further improve service quality for complex patients by adopting a disease-based rather than medical specialty-based departmental routing strategy for newly arriving patients. A counterfactual analysis, based on a simultaneous equations probit model that controls simultaneously for endogeneity of volume, focus, and routing strategy, suggests that the proposed reorganization could have reduced mortality in the sample by 13.43% (95% CI [6.87%; 18.95%]) for routine patients and by 11.67% (95% CI [6.13%; 16.86%]) for the most complex patients

Human factors aspects of air traffic control

An overview of human factors problems associated with the operation of present and future air traffic control systems is presented. A description is included of those activities and tasks performed by air traffic controllers at each operational position within the present system. Judgemental data obtained from controllers concerning psychological dimensions related to these tasks and activities are also presented. The analysis includes consideration of psychophysiological dimensions of human performance. The role of the human controller in present air traffic control systems and his predicted role in future systems is described, particularly as that role changes as the result of the system's evolution towards a more automated configuration. Special attention is directed towards problems of staffing, training, and system operation. A series of ten specific research and development projects are recommended and suggested work plans for their implementation are included

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster