A Discrete Inspired Bat Algorithm for Firetruck Dispatch in Emergency Situations

Part of the Springer Tracts in Civil Engineering book series (SPRTRCIENG)


This research considers the case where a large fire has developed beyond the possibility of suppression and resources need to be deployed to reduce the risk to critical assets. Thus, to determine an optimal deployment of the firetrucks to multiple assets in a large area, a mathematical formulation is proposed, focusing on the maximization of the aggregated value of the protected assets that are critically selected, and on the minimization of the dispatch strategy cost. Moreover, the novelty of the presented formulation is the incorporation of the CO2 emissions of the firetrucks in the cost function, and, hence, the formulation of the Green-Prize Collecting Vehicle Routing Problem. Moreover, a hybrid Bat Algorithm (BA) is developed for the optimization of the aforementioned problem, namely the Discrete Inspired Bat Algorithm (DIBA). The effectiveness of the proposed algorithmic approach is demonstrated over computational experiments, in comparison with the results of a commercial exact solver.


Discrete bat algorithm Prize-collecting vehicle routing problem CO2 emissions 



This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKY).


  1. El Bouzekri, E. I. A., Elhassania, M., & Alaoui, A. E. H. (2013). A hybrid ant colony system for green capacitated vehicle routing problem in sustainable transport. Journal of Theoretical and Applied Information Technology, 54(2), 198–208.Google Scholar
  2. Jia, S. J., Yi, J., Yang, G. K., Du, B., & Zhu, J. (2013). A multi-objective optimisation algorithm for the hot rolling batch scheduling problem. International Journal of Production Research, 51(3), 667–681.CrossRefGoogle Scholar
  3. Li, K., & Tian, H. (2016). A two-level self-adaptive variable neighborhood search algorithm for the prize-collecting vehicle routing problem. Applied Soft Computing, 43, 469–479.CrossRefGoogle Scholar
  4. Long, J., Sun, Z., Pardalos, P. M., Hong, Y., Zhang, S., & Li, C. (2019). A hybrid multi-objective genetic local search algorithm for the prize-collecting vehicle routing problem. Information Sciences, 478, 40–61.MathSciNetCrossRefGoogle Scholar
  5. Martell, D. L. (2007). Fifty years of OR in forestry preface to the special forestry issue of INFOR. INFOR: Information Systems and Operational Research, 45(1), 5–7.Google Scholar
  6. Osaba, E., Carballedo, R., Yang, X. S., Fister Jr, I., Lopez-Garcia, P., & Del Ser, J. (2018). On efficiently solving the vehicle routing problem with time windows using the bat algorithm with random reinsertion operators. In Nature-Inspired Algorithms and Applied Optimization (pp. 69–89). Cham: Springer.Google Scholar
  7. Osaba, E., Yang, X. S., Fister, I., Jr., Del Ser, J., Lopez-Garcia, P., & Vazquez-Pardavila, A. J. (2019). A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm and Evolutionary Computation, 44, 273–286.CrossRefGoogle Scholar
  8. Osaba, E., Yang, X. S., Diaz, F., Lopez-Garcia, P., & Carballedo, R. (2016). An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Engineering Applications of Artificial Intelligence, 48, 59–71.CrossRefGoogle Scholar
  9. Roozbeh, I., Ozlen, M., & Hearne, J. W. (2018). An adaptive large neighbourhood search for asset protection during escaped wildfires. Computers & Operations Research, 97, 125–134.MathSciNetCrossRefGoogle Scholar
  10. Saji, Y., & Riffi, M. E. (2016). A novel discrete bat algorithm for solving the travelling salesman problem. Neural Computing and Applications, 27(7), 1853–1866.CrossRefGoogle Scholar
  11. Taha, A., Hachimi, M., & Moudden, A. (2015). Adapted bat algorithm for capacitated vehicle routing problem. International Review on Computers and Software (IRECOS), 10(6), 610–619.CrossRefGoogle Scholar
  12. Taha, A., Hachimi, M., & Moudden, A. (2017). A discrete Bat Algorithm for the vehicle routing problem with time windows. In 2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA) (pp. 65–70). IEEE.Google Scholar
  13. Tang, L., & Wang, X. (2006). Iterated local search algorithm based on very large-scale neighborhood for prize-collecting vehicle routing problem. The International Journal of Advanced Manufacturing Technology, 29(11–12), 1246–1258.CrossRefGoogle Scholar
  14. Tian, G., Ren, Y., & Zhou, M. (2016). Dual-objective scheduling of rescue vehicles to distinguish forest fires via differential evolution and particle swarm optimization combined algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3009–3021.CrossRefGoogle Scholar
  15. Tiwari, A., Chang, P. C., Elangovan, G., & Annadurai, S. P. (2015). A hybrid edge recombination approach to solve price collecting vehicle routing problem. In 2015 International Conference on Control, Automation and Robotics (ICCAR) (pp. 200–203). IEEE.Google Scholar
  16. Van Der Merwe, M., Minas, J., Ozlen, M., & Hearne, J. (2014). The cooperative orienteering problem with time windows. Scholar
  17. Van der Merwe, M., Minas, J. P., Ozlen, M., & Hearne, J. W. (2014b). A mixed integer programming approach for asset protection during escaped wildfires. Canadian Journal of Forest Research, 45(4), 444–451.CrossRefGoogle Scholar
  18. Wu, P., Cheng, J., & Feng, C. (2019). Resource-constrained emergency scheduling for forest fires with priority areas: An efficient integer-programming approach. IEEJ Transactions on Electrical and Electronic Engineering, 14(2), 261–270.CrossRefGoogle Scholar
  19. Wu, P., Chu, F., Che, A., & Zhou, M. (2017). Bi-objective scheduling of fire engines for fighting forest fires: New optimization approaches. IEEE Transactions on Intelligent Transportation Systems, 19(4), 1140–1151.CrossRefGoogle Scholar
  20. Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Berlin, Heidelberg: Springer.Google Scholar
  21. Zhang, T., Chaovalitwongse, W. A., Zhang, Y. J., & Pardalos, P. M. (2009). The hot-rolling batch scheduling method based on the prize collecting vehicle routing problem. Journal of Industrial and Management Optimization, 5(4), 749–765.MathSciNetCrossRefGoogle Scholar
  22. Zhou, Y., Luo, Q., Xie, J., & Zheng, H. (2016). A hybrid bat algorithm with path relinking for the capacitated vehicle routing problem. In Metaheuristics and Optimization in Civil Engineering (pp. 255–276). Cham: Springer.Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Production Engineering and ManagementTechnical University of CreteChaniaGreece

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