2,807 research outputs found
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
Relief distribution networks : a systematic review
In the last 20 years, Emergency Management has received increasing attention from the scientific community. Meanwhile, the study of relief distribution networks has become one of the most popular topics within the Emergency Management field. In fact, the number and variety of contributions devoted to the design or the management of relief distribution networks has exploded in the recent years, motivating the need for a structured and systematic analysis of the works on this specific topic. To this end, this paper presents a systematic review of contributions on relief distribution networks in response to disasters. Through a systematic and scientific methodology, it gathers and consolidates the published research works in a transparent and objective way. It pursues three goals. First, to conduct an up-to-date survey of the research in relief distribution networks focusing on the logistics aspects of the problem, which despite the number of previous reviews has been overlooked in the past. Second, to highlight the trends and the most promising challenges in the modeling and resolution approaches and, finally, to identify future research perspectives that need to be explored
Online optimization of casualty processing in major incident response: An experimental analysis
When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility
Balancing the arrival times of users in a two-stage location problem
There has been a number of facility location problems dealing with the introduction of the equity issue in the travel distances distribution. In this paper we analyze a new aspect of equity concerning the distribution of the arrival times of customers. Given a depot and a set of demand points generating flow which also represent potential locations, we consider a discrete two-stage location problem whose aim is to locate a given number of facilities and to allocate the demand points to a facility. We assume as objective the maximization of the minimum difference between two consecutive arrival times of flows to the depot through the patronized facility. This particular equity measure is introduced in order to reduce risks of congestion in the dynamic of flow arrivals at the common destination. The problem is described through two Integer Programming formulations. Computational results for solution methods based on both formulations are then shown and analyzed
Facility location optimization model for emergency humanitarian logistics
Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations
A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems
The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable.
The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk.
Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible.
The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints.
An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response.
The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool
AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS
Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems
ОПТИМАЛЬНАЯ МАРШРУТИЗАЦИЯ ВОЗДУШНЫХ СУДОВ И МАШИН СКОРОЙ ПОМОЩИ В ЛОГИСТИКЕ ПРИ СТИХИЙНЫХ БЕДСТВИЯХ
One of the most vital aspects of emergency management studies is the development and examination of post-disaster search and rescue activities and treatment facilities. One of such issues to be considered while performing these operations is to reach the disaster victims within minimum time and to plan disaster logistics in the most efficient manner possible. In this study, the problem of planning debris scanning activities with Unmanned Aerial Vehicles after an earthquake and transporting the injured people to the hospitals by ambulances within minimum time was discussed, and mathematical models were developed to solve the problem. The ambulance routing problem and the mathematical model to be used in the solution to the problem are discussed for the first time in the literature. The developed model was tested on the problem sets created by taking into account the data of the province under investigation.Одним из наиболее важных аспектов исследований по управлению рисками и чрезвычайными ситуациями является разработка и изучение поисково-спасательных мероприятий и очистных сооружений после стихийных бедствий. Одним из вопросов, которые необходимо учитывать при выполнении этих операций, является обеспечение доступа к жертвам стихийных бедствий в минимальные сроки и планирование логистики в случае стихийных бедствий наиболее эффективным способом. В данном исследовании рассматривается проблема планирования работ по спасению с помощью беспилотных летательных аппаратов после землетрясения и транспортировки пострадавших людей в больницы на машинах скорой помощи за минимальное время. Для решения этой проблемы были разработаны и предложены математические модели. Впервые рассматривается задача маршрутизации скорой помощи и математическая модель, которая будет использоваться для решения этой задачи. Разработанная модель была протестирована на множествах задач, созданных с учетом реальных данных исследуемой провинции Турции
Optimization of Location-Routing for the Waste Household Appliances Recycling Logistics under the Uncertain Condition
Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. In recent years, due to the poor management of waste household appliances recycling logistics system, safety accidents occur frequently, which seriously harm the health and life safety of the society. This paper studies the risk management of recycling waste household appliances under uncertain conditions and establishes a risk measurement model under fuzzy population density. Considering the multi-stage and classification diversity of waste household appliances recycling logistics, the multi-objective location routing model and location - routing model are established respectively. Based on the model complexity analysis, the solution method of multi-objective model is designed. Finally, the validity of the model and algorithm is verified by examples and tests
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