Emergency scheduling for forest fires subject to limited rescue team resources and priority disaster areas

To enable immediate and efficient emergency scheduling during forest fires, we propose a novel emergency scheduling model for such fires subject to priority disaster areas and limited rescue team resources to minimize the total travel distance for rescue teams. Moreover, a hybrid intelligent algorithm integrating genetic algorithm (GA) and particle swarm optimization (PSO) is adopted to solve the proposed model. A case study is presented to illustrate the proposed model and the effectiveness of the proposed algorithm. The goal of this work is to analyze the emergency scheduling problem of forest fires subject to limited rescue teams and priority disaster areas. Both theoretical and simulation results demonstrate that the proposed model can perform effectively the quantitative analysis of an emergency involving forest fires. Such results can help decision makers to make better judgment when dealing with an emergency involving fires. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

[1]  Chaoyong Zhang,et al.  An improved genetic algorithm for robust permutation flowshop scheduling , 2011 .

[2]  Yong Deng,et al.  A new fuzzy dempster MCDM method and its application in supplier selection , 2011, Expert Syst. Appl..

[3]  Maria Paola Scaparra,et al.  A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics , 2014, Comput. Oper. Res..

[4]  Khaled Ghedira,et al.  Distributed genetic algorithm for disaster relief planning , 2013, Int. J. Comput. Commun. Control.

[5]  Linet Özdamar,et al.  Greedy Neighborhood Search for Disaster Relief and Evacuation Logistics , 2008, IEEE Intelligent Systems.

[6]  Peigen Li,et al.  A very fast TS/SA algorithm for the job shop scheduling problem , 2008, Comput. Oper. Res..

[7]  R. Pastor-Satorras,et al.  Steady-state dynamics of the forest fire model on complex networks , 2009, 0911.0569.

[8]  MengChu Zhou,et al.  A Chance Constrained Programming Approach to Determine the Optimal Disassembly Sequence , 2013, IEEE Transactions on Automation Science and Engineering.

[9]  Irina S. Dolinskaya,et al.  Disaster relief routing: Integrating research and practice , 2012 .

[10]  Linet Özdamar,et al.  Emergency Logistics Planning in Natural Disasters , 2004, Ann. Oper. Res..

[11]  Yong Deng,et al.  Route selection for emergency logistics management: A bio-inspired algorithm , 2013 .

[12]  Zhi-Hua Hu,et al.  A container multimodal transportation scheduling approach based on immune affinity model for emergency relief , 2011, Expert Syst. Appl..

[13]  MengChu Zhou,et al.  Probability Evaluation Models of Product Disassembly Cost Subject to Random Removal Time and Different Removal Labor Cost , 2012, IEEE Transactions on Automation Science and Engineering.

[14]  Linet Özdamar,et al.  A dynamic logistics coordination model for evacuation and support in disaster response activities , 2007, Eur. J. Oper. Res..

[15]  Wei Yi,et al.  Ant Colony Optimization for Disaster Relief Operations , 2007 .

[16]  Yuan Yuan,et al.  Path selection model and algorithm for emergency logistics management , 2009, Comput. Ind. Eng..

[17]  Zoran Kapelan,et al.  Decision Support System for emergency scheduling of raw water supply systems with multiple sources , 2013, Frontiers of Environmental Science & Engineering.

[18]  Tolga Bektas,et al.  Formulations and Branch-and-Cut Algorithms for the Generalized Vehicle Routing Problem , 2011, Transp. Sci..

[19]  M. Abramson,et al.  The effects of bushfire smoke on respiratory health , 2011, Respirology.

[20]  Linet Özdamar,et al.  A comparison of two mathematical models for earthquake relief logistics , 2011 .

[21]  Graham Coates,et al.  A multi-objective combinatorial model of casualty processing in major incident response , 2013, Eur. J. Oper. Res..

[22]  Jiuh-Biing Sheu,et al.  An emergency logistics distribution approach for quick response to urgent relief demand in disasters , 2007 .

[23]  O. Séro-Guillaume,et al.  Modelling forest fires. Part II: reduction to two-dimensional models and simulation of propagation , 2002 .

[24]  S A Ergonul.  A probabilistic approach for earthquake loss estimation , 2005 .

[25]  Peigen Li,et al.  A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem , 2007, Comput. Oper. Res..

[26]  T. Aditama,et al.  Impact of haze from forest fire to respiratory health: Indonesian experience , 2000, Respirology.

[27]  Javier Montero,et al.  A multi-criteria optimization model for humanitarian aid distribution , 2011, J. Glob. Optim..

[28]  Ebrahim Teimoury,et al.  Metaheuristic Algorithm for Solving Biobjective Possibility Planning Model of Location-Allocation in Disaster Relief Logistics , 2014, J. Appl. Math..

[29]  T. Bektaş.  The multiple traveling salesman problem: an overview of formulations and solution procedures , 2006 .

[30]  Jain-Shing Wu,et al.  Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling , 2014, Expert Syst. Appl..

[31]  Pedro M. Reyes.  Logistics networks: A game theory application for solving the transshipment problem , 2005, Appl. Math. Comput..

[32]  Gülay Barbarosoglu,et al.  A two-stage stochastic programming framework for transportation planning in disaster response , 2004, J. Oper. Res. Soc..

[33]  D. I. Iudin,et al.  Reprint of Infinity computations in cellular automaton forest-fire model , 2015, Commun. Nonlinear Sci. Numer. Simul..