Optimal location of medical emergencies in the road network: a combined model approach of agent-based simulation and a metaheuristic algorithm

Background: The ability of ambulance centers to respond to emergency calls is an important factor in the recovery of patients' health. This study aimed to provide a model for the establishment of emergency relief in the road network in 2020 in East Azerbaijan province. Methods: This applied-descriptive and experimental research with an explanatory modelling approach used the comments of 70 experts to run a model, which was based on the use of a metaheuristic (genetic) algorithm ,Simulation for the number of ambulances and the composition of the monitoring list simultaneously , objective and subjective data combined ,the  agent and environmental variables, were determined and modelled through a meta-hybrid approach during the agent-based simulation and the metaheuristic algorithm. Results: To travel the initial structure for 40 dangerous points and five stations, the initial time was equal to 7860 Minutes, which reached a number between 2700 and 4000 Minutes after genetic optimization, production of a new list, and the mutation of ambulances from one station to another. Conclusion: This type of optimization can be used to accelerate activities and reduce costs. Due to the dissimilar traffic of the areas, the ambulance does not arrive at dangerous points at equal times. The travel time of all dangerous points can be reduced by changing the location of points, moving forward or backwards depending on the conditions, customizing the features of ambulances and dangerous points, and combining the list of areas to find the best location for emergencies according to the interaction between agents, environmental constraints, and different behavioral features

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

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

Infrastructure planning for electrified transportation

Due to the climate crisis, the importance of reducing greenhouse gas (GHG) has been recognized by governments, private companies and the general public alike. Yet carbon capturing-based approaches are difficult to integrate with transportation, which is one of the largest GHG producing sectors, Therefore, electrification is the only viable approach to reduce emissions from transportation, by greatly increasing the market share of electric vehicles (EVs). However, the mass adoption of either (or both) of battery EVs (BEVs) and fuel cell EVs (FCEVs) require a large amount of supporting infrastructures, particularly the construction of EV charging stations (EVCSs) for BEVs and hydrogen refuelling stations (HRSs) for FCEVs. The goal of this study is to provide effective approaches for the sizing and sitting of EVCSs and HRSs to facilitate the deployment of BEVs and FCEVs. The background and an overview of the thesis are provided in Chapter 1, where the gaps in the current research are pointed out and the objectives of the thesis are formulated. Chapter 2 reviewed the current state of technologies regarding the hydrogen life cycle as well as the popular planning models for EVCSs and HRSs. In Chapter 3, to achieve a competitive strategy from the perspective of private companies, a market-based framework is proposed for the problem of EVCS planning by leveraging Graph Convolutional Network (GCN) and game theory. In Chapter 4, a multi-objective planning model is developed for EVCSs and the expansion of distribution network with significant renewable components while considering uncertainties in EV charging behaviour. Additionally, in Chapter 5, a planning model of HRS maximises the long-term profit while considering different practical constraints. The HRS planning model also addresses short-term demand uncertainty via redistribution. The models that are developed in this study are validated using either synthetic or real-world case studies, and the simulation results showed the effectiveness of the proposed models. Finally Chapter 6 summarises the major achievements of the thesis and provides directions for further research

Measuring and optimizing accessibility to emergency medical services

Emergency medical services (EMSs) undertake the responsibility of providing rapid medical care to patients suffering from unexpected illnesses or injuries and transferring them to definitive care facilities. This research concerns several research gaps that are associated with different EMS trips, real-time traffic conditions, improving EMS efficiency and equalities. This research aims to develop GIS-based spatial optimization methods to improve service efficiency and equality in EMS systems. Specifically, the research intends to achieve the following goals: (1) to measure spatiotemporal accessibility to EMS; (2) to improve EMS efficiency and provision through spatial optimization approaches; (3) to reduce urban-rural inequalities in EMS accessibility and coverage using spatial optimization approaches. The proposed approaches are applied in three empirical studies in Wuhan, China. To achieve the first objective, the proximity and the enhanced two-step floating catchment method (E-2SFCA) are adopted to evaluate spatiotemporal accessibility. First, the EMS travel time is estimated for the two related trips as an overall EMS journey: one is from the nearest EMS station to the scene (Trip 1), and the other is from the scene to the nearest emergency hospital (Trip 2). Then, the E-2SFCA method is employed to calculate the accessibility score that integrates both geographic accessibility and availability of EMS. Travel time is estimated by using both static road network with standard speed limits and online map service considering real-time traffic. To achieve the second objective, two facility location models are proposed to improve EMS service coverages for two-related trips (Trips 1 and 2). The first model maximizes the amount of demand covered by both ambulance coverage (EMS station – demand) and hospital coverage (demand – hospital). The second model maximizes the amount of demand that can be served by both ambulance coverage and overall coverage (EMS station – demand – hospital). To achieve the third objective, two bi-objective optimization models are developed. The two models have the same primary objective to maximize the total covered demand by ambulance. The second objective is to minimize one of the two inequality measures: one focuses on accessibility of uncovered rural people, and the other concerns the urban-rural inequality in service coverage. For the first empirical study with respect to spatiotemporal access to EMS, different spatial patterns are found for the three trips (two partial trips and the overall trip). Good accessibility to one trip cannot guarantee good accessibility to another trip. In addition, urban-rural inequalities in EMS accessibility and coverage are observed. Finally, it is observed that real-time traffic conditions greatly affect EMS accessibility, particularly in urban districts. Specifically, the accessibility of EMS becomes poor during the morning (7-9 am) and evening peak periods (5-7 pm). For the second empirical study in relation to EMS optimization involving two related trips, the results find that the first proposed model can guarantee that more demand to be covered by both ambulance and hospital coverages than the Maximum Coverage Location Problem (MCLP). The second proposed model can ensure that as many people as possible to be served by both ambulance and overall coverage than the work by ReVelle et al. (1976). For the third empirical study attempting to reduce urban-rural inequality in EMS, the results show that the first bi-objective model can improve EMS accessibility of uncovered rural demand, and the second model can reduce EMS service coverages between urban and rural areas. However, the improvement EMS inequalities between urban and rural areas leads to a cost of a decrease in the total covered population, especially in urban areas. Regarding policy implications, this research suggests that different EMS trips and traffic conditions should be considered when measuring spatial accessibility to EMS. Spatial optimization research can help improving service efficiency and reduce regional equalities in EMS systems. The work presented in this thesis can aid the planning practice of public services like EMS and provide decision support for policymakers

Integrating artificial neural networks, simulation and optimisation techniques in improving public emergency ambulance preparedness for heterogeneous regions under stochastic environments.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The Bulawayo Emergency Medical Services (BEMS) department continues to rely on judgemental methods with limited use of historical data for future predictions, strategic, tactical and operational level decision making. The rural to urban migration trend has seen the sprouting of new residential areas, and this has put pressure to the limited health, housing and education resources. It is expected that as population increases, there is subsequent increase in demand for public emergency services. However, public emergency ambulance demand trends has been decreasing in Bulawayo over the years. This trend is a sign of limited capacity of the service rather than demand itself. The situation demanded for consolidated efforts across all sectors including research, to restore confidence among residents, reduce health risk and loss of lives. The key objective was to develop a framework that would assist in integrating forecasting, simulation and optimisation techniques for ambulance deployment to predefined locations with heterogeneous demand patterns under stochastic environments, using multiple performance indicators. Secondary data from the Bulawayo Municipality archives from 2010 to 2018 was used for model building and validation. A combination of methods based on mathematics, statistics, operations research and computer science were used for data analysis, model building, sensitivity analysis and numerical experiments. Results indicate that feed forward neural network (FFNN) models are superior to traditional SARIMA models in predicting ambulance demand, over a short-term forecasting horizon. The FFNN model is more inclined to value estimation as compared to SARIMA model, which is directional as depicted by the linear pattern over time. An ANN model with a 7-(4)-1 architecture was selected to forecast 2019 public emergency ambulance demand (PEAD). Peak PEAD is expected in January, March, September and December whilst lower demand is expected for April, June and July 2019. Simulation models developed mimicked the prevailing levels of service for BEMS with six(6) operational ambulances. However. the average response times were well above 15 minutes, with significantly high average queuing times and number of ambulances queuing for service. These performance outcomes were highly undesirable as they pose a great threat to human based outcomes of safety and satisfaction with regards to service delivery. Optimisation for simulation was conducted by simultaneously minimising the average response time and average queuing time, while maximising throughput ratios. Increasing the number of ambulances influenced the average response time below a certain threshold, beyond this threshold, the average response time remained constant rather than decreasing gradually. Ambulance utilisation inversely varied to increase in the feet size. Numerical experiments revealed that reducing the response time results in the reduction in number of ambulances required for optimal ambulance deployment. It is imperative to simultaneously consider multiple performance indicators in ambulance deployment as it balances resource allocation and capacity utilisation, while avoiding idleness of essential equipment and human resources. Management should lobby for de-congestion and resurfacing of old and dilapidated roads to increase access and speed when responding to emergency calls. Future research should investigate the influence of varying service time on optimum deployment plans and consider operational costs, wages and other budgetary constraints that influence the allocation of critical but scarce resources such as personnel, equipment and emergency ambulance response vehicles

Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro

Stochastic planning for active distribution networks hosting fast charging stations

With the advent of electric vehicles (EVs), charging infrastructure needs to become more available and electricity providers must build additional power generation capacity to support the grid. In siting and sizing of fast charging stations (FCSs), both the distribution network constraints, as well as the traffic network limitations, must be considered because FCSs exist on both levels. Moreover, the siting and sizing of wind-powered distributed generation (WPDG) is a solution to gradually decarbonizing the grid; therefore, reducing our carbon footprint. In addition to providing capacity, they also have other benefits in the distribution network such as reducing transmission losses. In this thesis, a new framework is proposed which successfully implements a novel scoring technique to rate the attractiveness of FCS candidate locations thus, determining the expected FCS demand in each candidate location and uses WPDGs to support that load. A study has been conducted to compare the suitability of industrial-scale turbines versus micro-wind turbines in an urban area. A method for selecting candidate locations for the later has been developed. A stochastic program is proposed to account for the non-deterministic elements of the problem including generic loads, residential electric vehicle loads, FCS loads, and wind speed where they are accounted for collectively using a method called convolution. This comes hand-in-hand with a mixed-integer non-linear programming model that sites and sizes both FCSs and WPDGs with an objective of maximizing profits to incentivize investments. A list of novel constraints has been introduced that connect the traffic network to the power network. The problem is modeled from the perspective of electric utilities but also considers the perspectives of the urban planners and potential investors. A case study was implemented showing how the scoring technique works and the results show that the math model considered all the parameters and respected all the constraints delivering a holistic set of decisions to site and size both FCSs and micro WPDGs in an urban area