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Pareto set as a model for dispatching resources in emergency Centres

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Abstract

The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. Understanding which are the best policies of dispatching as well as workload balancing are very important in the context FOG computing, which aims to distribute in part workload and services on fog devices (such as hardened routers, switches, IP video cameras, etc.). Fog-computing systems produce an ever-increasing service request impacting, for instance, to the power consumption on cloud servers. On the other hand, it is equally crucial to guarantee the quality of service (e.g., latency requirements) of end users. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

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References

  1. Birks DJ, Donkin S, and Wellsmith MJ (2008). Synthesis over analysis: Towards an ontology of volume crime simulation. In Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, eds. Lin Liu and John E. Eck. Hershey, PA: Idea Group

  2. Abounacer R, Rekik M, Renaud J (2014) An exact solution approach for multi-objective location-transportation problem for disaster response. Computer Operation Research 41:83–93

    Article  MathSciNet  MATH  Google Scholar 

  3. Guedes R, Furtado V. and Pequeno T (2015). Multi-objective Evolutionary Algorithms and Multiagent Models for Optimizing Police Dispatch. International Conference on Intelligence and Security Informatics (ISI)

  4. Huang K, Smilowitz BB (2012) Models for relief routing: equity, efficiency and efficacy transport. Res Part E: Logist Transport Rev 48:2–18

    Article  Google Scholar 

  5. Huang K, Jiang Y, Yuan Y, Zhao L (2015). modelling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E

  6. Deng R, Lu R, Lai C, Luan T and Liang H (2015) Optimal Workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet of Things Journal 3(6):1171–1181

  7. Guo X, Singh R, Zhao NT. (2016). An index based task assignment policy for achieving optimal power-delay tradeoff in edge cloud systems, (ICC) 2016 IEEE International Conference on communications, pp. 1–7

  8. Rao R, Liu X, Xie L, Liu F (2012) Coordinated energy cost management of distributed internet data centers in smart grid. IEEE Transactions on Smart Grid 3(1):50–58

    Article  Google Scholar 

  9. Zhao, T., Zhou, S, Guo, X, Niu, Z. (2017). Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing, 2017 IEEE International Conference on Communications, pp. 1–7

  10. Nabi, M, Benkoczi, R, Abdelhamid, S, Hassanein, H. S. (2017) Resource assignment in vehicular clouds, Communications (ICC) 2017 IEEE International Conference on, pp. 1–6

  11. Rao R, Liu X, Ilic M, Liu J (2012) Distributed coordination of internet data centers under multiregional electricity markets. Proc IEEE 100(1):269–282

    Article  Google Scholar 

  12. Souza V, Ramírez W, Masip-Bruin X, Marín-Tordera E, Ren G, Tashakor G. (2016). Handling service allocation in combined Fog-cloud scenarios, 2016 IEEE International Conference on Communications, pp. 1–5

  13. Kraemer F, Braten AE, Tamkittikhun N, Palma D (2017) Fog computing in healthcare – a review and discussion. Access IEEE 5:9206–9222

    Article  Google Scholar 

  14. Sacks S, Larson R, Richard C, Schaack C (1993) Minimizing the cost of dispatch delays by holding patrol cars in reserve. J Quant Criminol 9(2):203–224

    Article  Google Scholar 

  15. Almeida A, Ramalho GL, Santana HP, Tedesco P, Menezes TR, Corruble V, Chevaleyre Y (2004) Recent Advances on Multi-Agent Patrolling. Proc. 17th Brazilian Symposium on Artificial Intelligence

  16. Guedes R, Furtado V, Pequeno T (2014) Multiagent models for police resource allocation and dispatch. Intelligence and Security Informatics Conference (JISIC) 3(4):288–291

    Google Scholar 

  17. Hale CD (1980) Police patrol, operations and management. Prentice Hall, Upper Saddle River

    Google Scholar 

  18. Ianonnim AP, Chiyosi F, Morabito R (2015) A spatially distributed queuing model considering dispatching policies with server reservation. Transportation Research Part E: Logistics and Transportation Review 75:49–66

    Article  Google Scholar 

  19. Brantingham P, Brantingham P (2004) Computer simulation as a tool for environmental criminology. Secur J 17(1):21–30

    Article  Google Scholar 

  20. Liu L, Eck JE (2008) Artificial crime analysis systems: using computer simulations and geographic information systems. Idea Group, Hershey

    Book  Google Scholar 

  21. Zhang Y, Brown DE (2013) Police patrol districting method and simulation evaluation using agent-based model & GIS. Security Informatics 2:7

    Article  Google Scholar 

  22. Furtado V, Vasconcelos E (2006) A multiagent simulator to teach police allocation. AI Mag 27(3):63

    Google Scholar 

  23. Ahmaldi A, Seyedia P, Syam S (2017) A survey of healthcare facility location. Comput Oper Res 79:223–263

    Article  MathSciNet  Google Scholar 

  24. Wang Y, Luangkesom K, Schuman L. (2012). Modeling emergency medical response to a mass casualty incident using agent based simulation. Socio-Economic planning sciences, Elsevier

  25. Wang J, Han L, Shi A, Cui N (2016) A new partial coverage locating model for cooperative fire services. Information Science 373(10):527–538

    Article  Google Scholar 

  26. Najafi N, Eshghi K, Dullaert W (2013) A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transport Res Part E: Logist Transport Rev 49:217–249

    Article  Google Scholar 

  27. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4):257–271

    Article  Google Scholar 

  28. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

  29. Zitzler E., Laumanns, and Bleuler S. (2004). A Tutorial on Evolutionary Multiobjective Optimization. In: Metaheuristics for Multiobjective Optimization. Lecture Notes in Economics and Mathematical Systems. Volume 535

  30. Cansado T (2005) Alocação e Despacho de Recursos para Combate à Criminalidade. Master Dissertation, UFMG

  31. Melo A, Furtado V, Coelho A, Menezes R (2009) A bio-inspired crime simulation model. Decis Support Syst

  32. Wilensky U (1999). NetLogo. http://ccl.northwestern.edu/netlogo/ . Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL

  33. Yu L, Jiang T, Cao Y, Qi Q (2014) Carbon-aware energy cost minimization for distributed internet data centers in smart microgrids. IEEE Internet of Things Journal 1(3):255–264

    Article  Google Scholar 

Download references

Acknowledgements

This work has been partially supported by National Funding from the FCT - Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2013 Project; by Finep, with resources from Funttel, Grant No. 01.14.0231.00, under the Centro de Referência em Radiocomunicações - CRR project of the Instituto Nacional de Telecomunicações (Inatel), Brazil; by Brazilian National Council for Research and Development (CNPq) via Grant No. 309335/2017-5.

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Authors and Affiliations

  1. University of Fortaleza (UNIFOR), Fortaleza, CE, Brazil

    Ricardo Guedes, Vasco Furtado, Tarcísio Pequeno & Joel J. P. C. Rodrigues

  2. National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí, MG, Brazil

    Joel J. P. C. Rodrigues

  3. Instituto de Telecomunicações, Lisbon, Portugal

    Joel J. P. C. Rodrigues

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  1. Ricardo Guedes
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  2. Vasco Furtado
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  3. Tarcísio Pequeno
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  4. Joel J. P. C. Rodrigues
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Correspondence to Vasco Furtado.

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Guedes, R., Furtado, V., Pequeno, T. et al. Pareto set as a model for dispatching resources in emergency Centres. Peer-to-Peer Netw. Appl. 12, 865–880 (2019). https://doi.org/10.1007/s12083-018-0690-9

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