An emergency vehicles allocation model for major industrial disasters

Fancello, Gianfranco; Mancini, Susanna; Pani, Claudia; Fadda, Paolo

doi:10.1016/j.trpro.2017.05.134

Cited by 12 publications

(9 citation statements)

References 23 publications

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“…one with a higher one, whichever is the value of the secondary objective. This is a standard technique commonly used in multi-objective optimization dealing with hierarchical objective functions [31], [32], [33].…”

Section: E a Mixed-integer Linear Programming Formulationmentioning

confidence: 99%

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

2022

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In this paper, we consider the problem of completely flying over an area just hit by an earthquake with a fleet of Unmanned Aerial Vehicles (UAVs) to opportunely direct rescue teams. The cooperation between UAVs ensures that the search for possible survivors can be faster and more effective than the solutions currently implemented by civil protection.To study this scenario, we introduce the Cover by Multitrips with Priorities (CMP) problem, which tries to keep into account all the main real-life issues connected to the flight and coordination of the UAVs. We conduct a theoretical study to estimate the best number of UAVs and additional batteries, to give indications to the organization that leads the rescue teams to be able to guarantee rapid and effective rescue. Finally, based on some theoretical considerations, we propose some heuristics that tackle the problem of flying over the whole area with a fleet of UAVs in the shortest possible time. Simulations show that they work efficiently in both the proposed scenarios and provide better performance than previous solutions once they are arranged to work in our scenarios. The main advantages of our approach w.r.t. the current drone-based solutions used by the civil defense are that UAVs do not need drivers so the time of all available rescue workers can be invested in doing something else. In our model, we take into account that some sites (e.g. buildings with a high fire risk or schools and hospitals) have a higher priority and must be inspected first, and the possibility that UAVs can make a decision based on what they detect. Finally, our approach allows UAVs to collaborate so that the same sites will be flown over exactly once in order to speed up the rescue mission.

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Section: E a Mixed-integer Linear Programming Formulationmentioning

confidence: 99%

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

2022

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“…However, the study does not consider the casualty transportation between the locations. Fancello et al [28] studied the location and allocation problem of vehicles participating in relief operations in large-scale industrial disasters. The aim is to maximize the number of injured patients treated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (26) ensures that the number of casualties to be transported to the relevant hospital is less than the capacity of that hospital. Constraints (27) and (28) are the constraints that dynamically update the hospital capacities in each period. Constraints (29)-(34) refer to variable values before the disaster.…”

Section: First Stage Constraintsmentioning

confidence: 99%

Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Caglayan

Satoğlu

2021

Mathematics

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Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points.

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“…Each evacuation process or step is limited by the disaster environment and available resources, so evacuation must be simulated under these constraints to find an approximate best solution that can be performed. To achieve this task for an outpatient chemotherapy area with consideration of the five items mentioned above, this study proposes a constraint-based simulation [ 17 , 18 , 19 , 20 ] system with the capability for visual representation. The proposed system comprises three main processes: the definition of spatial and activity constraints, the modeling of agents, and the optimization of additional resource allocations.…”

Section: Related Workmentioning

confidence: 99%

Constructing Constraint-Based Simulation System for Creating Emergency Evacuation Plans: A Case of an Outpatient Chemotherapy Area at a Cancer Medical Center

Lin

Yien³

et al. 2020

Healthcare

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Making emergency evacuation plans for disaster prevention is always a high priority for hospital administrators to ensure the safety of patients and employees. This study employs the outpatient chemotherapy area of a cancer medical center as an example, and its area involves professional medical care and relatively complex human group behaviors. Hence, it is necessary to simulate evacuations in advance to formulate a special evacuation plan. To achieve this task, a constraint-based simulation system is developed with three major processes: defining spatial and activity constraints, agent-based modeling, and optimizing resource allocation. The spatial boundaries are converted from a three-dimensional model in the Building Information Modeling (BIM) to conduct a visualized simulation. Based on the spatial boundaries, the activities of the agents are set to obey the process specified by work studies. Finally, the Monte Carlo method is employed to simulate the stochastic rescue behaviors of nurses during disasters to determine the fittest resource allocation with the shortest evacuation time for different numbers of patients. The results reveal that the proposed system can output a suggested list of resource allocations and visualized results for administrators when making evacuation plans such that all the people in the area can be safely evacuated.

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An emergency vehicles allocation model for major industrial disasters

Cited by 12 publications

References 23 publications

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Constructing Constraint-Based Simulation System for Creating Emergency Evacuation Plans: A Case of an Outpatient Chemotherapy Area at a Cancer Medical Center

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