Study on Task Allocation Model of Forest Fire Fighting

2012 ◽  
Vol 457-458 ◽  
pp. 1129-1136
Author(s):  
Fa Lin Liu ◽  
Li Juan Wang
Keyword(s):  
Forest Fire ◽  
Information Entropy ◽  
Task Allocation ◽  
Ant Colony Algorithm ◽  
Optimal Allocation ◽  
Entropy Method ◽  
Fire Fighting ◽  
Allocation Scheme ◽  
Allocation Model ◽  
The Core

Firefighting task allocation is the core of the forest fire fighting programs. Making use of information entropy method, combined with the fire brigades, fire fighting capability, difficulty and other factors, an analysis in the priority under the conditions of multiple fire sites and fire lines is made, so as to build up fire fighting mission efficient matrix and determine the optimal allocation scheme by using ant colony algorithm. The practice shows that this task allocation model for forest fire fighting under multiple fire sites situation is feasible and practical.

The new generation of remote sensing services for operational forest fire-fighting within GMES

2006 ◽  
Author(s):  
Fernando Valcarce ◽  
Jesús Gonzalo ◽  
Joaquín Ramírez ◽  
Abel Calle Montes ◽  
Emilio Chuvieco
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Fire Fighting ◽  
New Generation

scholarly journals Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 5057
Author(s):  
Wan-Yu Yu ◽  
Xiao-Qiang Huang ◽  
Hung-Yi Luo ◽  
Von-Wun Soo ◽  
Yung-Lung Lee
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Task ◽  
The Core ◽  
Dynamic Task Allocation ◽  
Vehicle Technology ◽  
Future Work ◽  
Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

New technologies for effective forest fire fighting

2007 ◽  
Vol 64 (2) ◽  
pp. 243-251 ◽  
Author(s):  
João C. M. Bordado ◽  
João F. P. Gomes
Keyword(s):  
Forest Fire ◽  
New Technologies ◽  
Fire Fighting

Task Allocation Scheme for Cyber Physical Social Systems

2020 ◽  
Vol 7 (2) ◽  
pp. 832-842 ◽  
Author(s):  
Zhou Su ◽  
Minghui Dai ◽  
Qifan Qi ◽  
Yuntao Wang ◽  
Qichao Xu ◽  
...  
Keyword(s):  
Task Allocation ◽  
Social Systems ◽  
Allocation Scheme

Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

2018 ◽  
Vol 94 (1) ◽  
pp. 265-282 ◽  
Author(s):  
Somaiyeh MahmoudZadeh ◽  
David M. W. Powers ◽  
Karl Sammut ◽  
Amir Mehdi Yazdani ◽  
Adham Atyabi
Keyword(s):  
Motion Planning ◽  
Task Allocation ◽  
Allocation Model ◽  
Planning Task ◽  
Ocean Environment ◽  
Hybrid Motion

Mathematical Models for Predicting and Managing Water Resources — The Case of China in 2025

2013 ◽  
Vol 448-453 ◽  
pp. 995-1001
Author(s):  
Ning Na Wang ◽  
Qin Lin Zhou
Keyword(s):  
Water Supply ◽  
Water Demand ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Decision Makers ◽  
Grey System ◽  
Allocation Model ◽  
Water Supply Management ◽  
Generate Solution ◽  
Major Factors

An effective management of water supply is critically significant to a countrys water utilities, and accurate prediction of water supply and demand is of key importance for water supply management. The objectives of this paper are to use Grey System Model (GSM) and Linear Regression Model to forecast the water demand and water supply respectively in China 2025, and then propose a new Optimal Allocation Model (OAM) to generate solution so that analysts and decision makers can gain insight and understanding. The two predictive models take into account four major factors including domestic development, agriculture, industries and eco-environment, calculating a deficit between water demand and water supply in China 2025. Then the OAM, which considers desalinization, irrigation saving and urban recycling, provides a feasible solution to fill the gap and an effectual management of water supply.

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