A memetic algorithm for location-routing problem with time windows for the attention of seismic disasters a case study from Bucaramanga, Colombia

Introduction− In recent years, a great part of the population has been affected by natural and man-caused disasters. Hence, evacua-tion planning has an important role in the reduction of the number of victims during a natural disaster. Objective−In order to contribute to current studies of operations research in disaster management, this paper addresses evacuation planning of urban areas by using buses to pick up affected people after an earthquake.Methodology−The situation is modeled using Location-Routing Problem with Time Windows (LRPTW) to locate emergency shelters and identify evacuation routes that meet attention time constraints. To solve the LRPTW problem, a memetic algorithm (MA) is de-signed to minimize the total response time during an evacuation. The algorithm is not only validated using instances of literature, but also with the assessment of a case study of a seismic event in Bucaramanga, Colombia.Results and conclusions− The main contribution of this article is the development of a memetic algorithm for the solution of the proposed model that allows to solve real-size instances. The hybrid initialization of the MA prevents an early convergence by combin-ing randomness and a heuristic technique. Computational results indicate that the MA is a viable approach for the LRPTW solution. Likewise, a case study is presented for the city of Bucaramanga in order to validate the proposed model. Two scenarios are simulated showing that the management of the time windows (homogeneous or random) directly influences the solution and affects the objec-tive function. From a practical perspective, the location-routing problem must consider other criteria such as the cost of evacua-tion, including the attention delay cost, and the cost of opening shelters and routing.Introducción− En años recientes gran parte de la población ha sido afectada por desastres tanto naturales como antrópicos. Por esto, la planificación de la evacuación juega un papel importante en la reduc-ción del número de víctimas ante un desastre natural. Objetivo− Con el propósito de contribuir a los estudios actuales desde la investigación de operaciones en gestión de desastres, esta inves-tigación aborda la planificación de la evacuación de áreas urbanas usando buses para recoger afectados.Metodología− El problema se modela mediante un problema de localización-ruteo con ventanas de tiempo (LRPTW) para determinar el número y la ubicación de los albergues las y rutas de recolección para evacuación, cumpliendo restricciones en tiempo de atención. Para solucionar el LRPTW, se diseña un algoritmo memético (MA) que minimiza el tiempo total de respuesta en la evacuación. El algo-ritmo es validado en instancias de la literatura y mediante un caso de estudio de un evento sísmico en Bucaramanga (Colombia).Resultados y conclusiones− La contribución principal de este ar-tículo es el desarrollo de un MA para solucionar el modelo propuesto, que permite resolver instancias de tamaño real. La inicialización híbrida del MA evita una convergencia temprana, combinando alea-toriedad con una técnica heurística. Los resultados computacionales indican que el MA es un enfoque viable para solucionar el LRPTW. Así mismo, se presenta un caso de estudio en Bucaramanga para validar el modelo propuesto. Se plantean dos escenarios de desastre, evidenciando que el tratamiento que se da a las ventanas de tiempo (homogénea o aleatoria) influye directamente en la solución y afec-ta la función objetivo. Desde un enfoque práctico, el problema debe considerar otros criterios que pueden influir en la planificación de la evacuación, como el costo de la evacuación, costo de la demora en la atención, costo de apertura y de ruteo

Un algoritmo memético para el problema de localización-ruteo con ventanas de tiempo para la atención de desastres sísmicos: un caso de estudio de Bucaramanga, Colombia

Introduction: In recent years, a great part of the population has been affected by natural and man-caused disasters. Hence, evacuation planning has an important role in the reduction of the number of victims during a natural disaster. Objective: In order to contribute to current studies of operations research in disaster management, this paper addresses evacuation planning of urban areas by using buses to pick up affected people after an earthquake. Methodology: The situation is modeled using Location-Routing Problem with Time Windows (LRPTW) to locate emergency shelters and identify evacuation routes that meet attention time constraints. To solve the LRPTW problem, a memetic algorithm (MA) is designed to minimize the total response time during an evacuation. The algorithm is not only validated using instances of literature but also with the assessment of a case study of a seismic event in Bucaramanga, Colombia. Results and conclusions: The main contribution of this article is the development of a memetic algorithm for the solution of the proposed model that allows to solve real-size instances. The hybrid initialization of the MA prevents an early convergence by combining randomness and a heuristic technique. Computational results indicate that the MA is a viable approach for the LRPTW solution. Likewise, a case study is presented for the city of Bucaramanga in order to validate the proposed model. Two scenarios are simulated showing that the management of the time windows (homogeneous or random) directly influences the solution and affects the objective function. From a practical perspective, the location-routing problem must consider other criteria such as the cost of evacuation, including the attention delay cost, and the cost of opening shelters and routing.Introducción: En años recientes gran parte de la población ha sido afectada por desastres tanto naturales como antrópicos. Por esto, la planificación de la evacuación juega un papel importante en la reducción del número de víctimas ante un desastre natural. Objetivo: Con el propósito de contribuir a los estudios actuales desde la investigación de operaciones en gestión de desastres, esta investigación aborda la planificación de la evacuación de áreas urbanas usando buses para recoger afectados. Metodología: El problema se modela mediante un problema de localización-ruteo con ventanas de tiempo (LRPTW) para determinar el número y la ubicación de los albergues las y rutas de recolección para evacuación, cumpliendo restricciones en tiempo de atención. Para solucionar el LRPTW, se diseña un algoritmo memético (MA) que minimiza el tiempo total de respuesta en la evacuación. El algoritmo es validado en instancias de la literatura y mediante un caso de estudio de un evento sísmico en Bucaramanga (Colombia). Resultados y conclusiones: La contribución principal de este artículo es el desarrollo de un MA para solucionar el modelo propuesto, que permite resolver instancias de tamaño real. La inicialización híbrida del MA evita una convergencia temprana, combinando aleatoriedad con una técnica heurística. Los resultados computacionales indican que el MA es un enfoque viable para solucionar el LRPTW. Así mismo, se presenta un caso de estudio en Bucaramanga para validar el modelo propuesto. Se plantean dos escenarios de desastre, evidenciando que el tratamiento que se da a las ventanas de tiempo (homogénea o aleatoria) influye directamente en la solución y afecta la función objetivo. Desde un enfoque práctico, el problema debe considerar otros criterios que pueden influir en la planificación de la evacuación, como el costo de la evacuación, costo de la demora en la atención, costo de apertura y de ruteo

Evacuation planning with flood inundation as inputs

Recent flooding events happening in our city demonstrate frequency and severity of floods in the UK, highlighting the need to plan and prepare, and efficiently defend. Different from the numerous evacuation model and optimization algorithms, this paper aims to address flood evacuation planning with flood inundation as inputs. A dynamic flooding model and prediction to estimate the development of both surface water and flooding from rivers and watercourses has been fed into evacuation planning at various levels. A three-step approach is proposed. The first step is to identify assembly point designation. The second step is to find the candidate shortest path from each assembly point to all safe areas for all evacuees with consideration of possible inundation. The last step is to determine the optimal safe area for evacuees in the inundation area. The work presented in this paper has emphasized timing issue in evacuation planning. A case study is given to illustrate the use of the approach

Groupwise evacuation with genetic algorithms

In a crisis situation on board a ship, it can be of the utmost importance to have the passengers safely evacuate to the lifeboats in an efficient manner. Existing methods such as marked escape routes, maps and so on are not optimal as pre-planned escape routes may become heavily congested by passengers. The closest lifeboat is not always feasible as lifeboat capacity can be exceeded. Considering that some evacuees are strongly affiliated and would like to evacuate together as a group, it all becomes a very difficult problem to solve. Sub-problems have been modelled, but no existing model combines all of these aspects into account. We proceed by modelling the area to be evacuated as a time-expanded graph, assuming that future development in hazard severity is known in the form of a survivability percentage for each node. Then we apply a multi-objective genetic algorithm with five different fitness functions that use heuristics to maximize overall survivability and reduce the total egress time if possible. A method has been developed to pick the best evacuation plan out of the pool of potential solutions returned by the genetic algorithm. The solution is compared with Dijkstra’s algorithm and randomly generated paths. Experiments are conducted using these algorithms for both predefined and randomly generated graphs using different parameters. In the tested random graph, the genetic algorithm gives on average 24% better survivability and 3 times better grouping Random algorithms. A fixed network with a known solution was solved 100%. This genetic algorithm can be used to generate better routing plans that utilizes multiple evacuation routes and lifeboats while taking into account groups, resulting in smoother evacuations which can save more lives

Compromising system and user interests in shelter location and evacuation planning

Cataloged from PDF version of article.Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority’s point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the flow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the importance of the number and locations of shelter sites and the trade-off between efficiency and fairness. 2014 Elsevier Ltd. All rights reserved

A heuristic approach to flood evacuation planning

Flood evacuation planning models are an important tool used in preparation for flooding events. Authorities use the plans generated by flood evacuation models to evacuate the population as quickly as possible. Contemporary models consider the whole solution space and use a stochastic search to explore and produce solutions. The one issue with stochastic approaches is that they cannot guarantee the optimality of the solution and it is important that the plans be of a high quality. We present a heuristically driven flood evacuation planning model; the proposed heuristic is deterministic, which allows the model to avoid this problem. The determinism of the model means that the optimality of solutions found can be readily verified

An operational research-based integrated approach for mass evacuation planning of a city

Large-scale disasters are constantly occurring around the world, and in many cases evacuation of regions of city is needed. ‘Operational Research/Management Science’ (OR/MS) has been widely used in emergency planning for over five decades. Warning dissemination, evacuee transportation and shelter management are three ‘Evacuation Support Functions’ (ESF) generic to many hazards. This thesis has adopted a case study approach to illustrate the importance of integrated approach of evacuation planning and particularly the role of OR/MS models. In the warning dissemination phase, uncertainty in the household’s behaviour as ‘warning informants’ has been investigated along with uncertainties in the warning system. An agentbased model (ABM) was developed for ESF-1 with households as agents and ‘warning informants’ behaviour as the agent behaviour. The model was used to study warning dissemination effectiveness under various conditions of the official channel. In the transportation phase, uncertainties in the household’s behaviour such as departure time (a function of ESF-1), means of transport and destination have been. Households could evacuate as pedestrians, using car or evacuation buses. An ABM was developed to study the evacuation performance (measured in evacuation travel time). In this thesis, a holistic approach for planning the public evacuation shelters called ‘Shelter Information Management System’ (SIMS) has been developed. A generic allocation framework of was developed to available shelter capacity to the shelter demand by considering the evacuation travel time. This was formulated using integer programming. In the sheltering phase, the uncertainty in household shelter choices (either nearest/allocated/convenient) has been studied for its impact on allocation policies using sensitivity analyses. Using analyses from the models and detailed examination of household states from ‘warning to safety’, it was found that the three ESFs though sequential in time, however have lot of interdependencies from the perspective of evacuation planning. This thesis has illustrated an OR/MS based integrated approach including and beyond single ESF preparedness. The developed approach will help in understanding the inter-linkages of the three evacuation phases and preparing a multi-agency-based evacuation planning evacuatio

Multi-objective decision analytics for short-notice bushfire evacuation: An Australian case study

This paper develops a multi-objective optimisation model to compute resource allocation,shelter assignment and routing options to evacuate late evacuees from affected areas to shelters.Three bushfire scenarios are analysed to incorporate constraints of restricted time-window and potential road disruptions.Capacity and number of rescue vehicles and shelters are other constraints that are identical in all scenarios.The proposed mathematical model is solved by ?-constraint approach.Objective functions are simultaneously optimised to maximise the total number of evacuees and assigned rescue vehicles and shelters.We argue that this model provides a scenario-based decision-making platform to aid minimise resource utilisation and maximise coverage of late evacuees