Simulation and optimization for an experimental environment to wildfire resource management and planning: Firefight project modelling and architecture

Jové, Jaume Figueras; Petit, Antonio Guasch; Casas, Pau Fonseca i; Garcia, Josep Casanovas

doi:10.1109/wsc.2013.6721574

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fire simulation data model proposed in (Nader et al 2011, Figueras et al 2013 has been extended to include the fire suppression entities, such as aircrafts, air crew and air operations planning including in formation about the aircraft type and regulation in force and the list of tasks a crew member has to per form, among others. Figure 1 shows a brief schema of the elements modelled, the most important is the air operation planning because it is the link between the simulator and the optimizer.…”

Section: Model Definitionmentioning

confidence: 99%

Web simulation training environment for aircraft resource planning in wildfire events

Jové¹,

Petit²,

Garcia³

2015

2015 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

This poster presents a simulation tool developed in cooperation with the Catalonia firefighting authority to provide a training environment for firefighter air operations commanders in wildfire events. In case of a wildfire event multiple aircrafts are deployed, including a commandment aircraft, by the main operation center. Aircrafts tasks, deployments and schedules can be assigned by both the commandment aircraft and the main operation center. This center is also in charge of controlling the different simultaneous wildfire aircrafts being able to re-assign, land or re-schedule aircrafts from one wildfire to another. An on-line multi-user environment has been developed to manage and optimize the aircraft operations. The aim of this environment is to increase operations security and to relief the operators from errors and repetitive tasks. On top of the optimization environment a multi-user web based simulation tool has been developed in order to provide a training framework for firefighters air controllers.

show abstract

Section: Model Definitionmentioning

confidence: 99%

Web simulation training environment for aircraft resource planning in wildfire events

Jové¹,

Petit²,

Garcia³

2015

2015 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

show abstract

“…Fried et al [8] have proposed an integrated model for allocating firefighter resources and evaluating dispatching rules. Other researchers [5,14,27] have also integrated fire behavior simulation and optimization to help allocate firefighting resources. Hu and Ntaimo [13] have developed an agent-based discrete event simulation model that simulates fire suppression based on dispatching plans from a stochastic optimization model.…”

Section: Related Workmentioning

confidence: 99%

Allocation and Scheduling of Firefighting Units in Large Petrochemical Complexes

Alutaibi¹,

Alsubaie²,

Martí³

2015

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Fire incidents in large petrochemical complexes such as oil refineries cause heavy losses. Due to the strong interdependencies that exist among units in these industrial complexes, planning an efficient response is a challenging task for firefighters. The task is even more challenging during multiple-fire incidents. This chapter describes a firefighting decision support system that helps conduct efficient responses to fire incidents. The decision support system optimizes the allocation of firefighting units in multiple-fire incidents with the objective of minimizing the economic impact. In particular, the system considers infrastructure interdependencies in estimating the damage associated with a given fire scenario, calculates the resulting economic losses and determines the optimal assignment of available firefighters. The decision support system can be used before an incident for training and planning, during an incident for decision support or after an incident for evaluating suppression strategies.

show abstract

A Simulation-Based Real-Time Deep Reinforcement Learning Approach for Fighting Wildfires

Tupayachi,

Ferguson,

2024

2024 Annual Modeling and Simulation Conference (ANNSIM)

View full text Add to dashboard Cite

Simulation and optimization for an experimental environment to wildfire resource management and planning: Firefight project modelling and architecture

Cited by 4 publications

References 2 publications

Web simulation training environment for aircraft resource planning in wildfire events

Web simulation training environment for aircraft resource planning in wildfire events

Allocation and Scheduling of Firefighting Units in Large Petrochemical Complexes

A Simulation-Based Real-Time Deep Reinforcement Learning Approach for Fighting Wildfires

Contact Info

Product

Resources

About