OR/MS research perspectives in disaster operations management: a literature review

The unpredictability of natural disasters makes handling their impacts on the population, the environment and the economic resources a challenging decision-making process that must be wisely performed in a very short period of time. An adequate management of operations to disaster response is challenging for decision makers and it has become a topic of significant relevance on a worldwide basis. As a result, academics and practitioners in the field of OR/MS have increased their interest in developing tools to support the decision-making processes on a disaster scenario. This paper surveys the OR/MS literature to identify new trends of increasing interest in disaster operations management (DOM) that have emerged in the last years. A discussion on the gaps that have been successfully addressed in the last years and those that remain opened is also presented. Among the main findings, the recent use of methodologies based on data analysis, such as machine learning and data mining, to address DOM problems was identified. Moreover, a significant increase in the study of operations in the recovery phase and the inclusion of humanitarian objectives in mathematical models was established

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

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature

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 ALNS para el VRPD en la distribución de última milla

Los vehículos aéreos no tripulados más conocidos como drones han despertado gran interés en los últimos años, teniendo aplicaciones en operaciones militares y civiles, recientemente se ha investigado acerca de las ventajas de su uso en la distribución de paquetes. En esta investigación se aborda un Problema de Ruteo de Vehículos con Drones (VRPD) enfocado a la distribución de última milla, en el cual los drones y camiones pueden trabajan de manera simultánea, considerando un límite de capacidad para el camión y el dron, así como restricciones asociadas al tiempo de la ruta. Dentro de la formulación matemática se propone una ecuación de velocidad de vuelo del dron y se restringen las rutas a un porcentaje límite de batería disponible para evitar mal funcionamiento en el aire. Para resolver esta formulación se usa el algoritmo Adaptive Large Neighborhood Search (ALNS), que es validado usando instancias propuestas en la literatura. Además, se verifica cómo varía la función objetivo de la solución inicial mediante la utilización de las heurísticas de destrucción y reparación, finalmente se realiza un análisis de sensibilidad para algunos parámetros del algoritmo y características de los drones; realizando conclusiones de los resultados arrojados y efectuando recomendaciones para futuras investigaciones.Unmanned aerial vehicles, better known as drones, have aroused great interest in recent years, having applications in military and civil operations. Research has recently been carried out on the advantages of their use in parcel distribution. This research addresses a Vehicle Routing Problem with Drones (VRPD) focused on last mile distribution, in which drones and trucks can work simultaneously, considering a capacity limit for the truck and the drone, as well as restrictions associated with the time of the route. Within the mathematical formulation, a drone flight speed equation is proposed, and the routes are restricted to a limit percentage of available battery to avoid malfunctions in the air. To solve this formulation, the Adaptive Large Neighborhood Search (ALNS) algorithm is used, which is validated using instances proposed in the literature. In addition, it is verified how the objective function of the initial solution varies by using the destruction and repair heuristics, finally a sensitivity analysis is performed for some parameters of the algorithm and characteristics of the drones; making conclusions from the results obtained and making recommendations for future research

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, 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

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature

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, 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