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A Compromise Decision-Making Model to Recover Emergency Logistics Network

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 15))

Abstract

Quick recovery of emergency service facilities (ESFs) in the aftermath of large-scale disasters has emerged as a hot topic in the field of emergency logistics. This paper focuses on the ESFs recovery problem under the constraints of scare emergency resource and recovery time. In this study, a compromise programming model is proposed as an integrated decision support tool to obtain an optimal compromise solution with regard to two objectives: minimize the consumption of recovery resources and maximize the resilience capacity through selecting different recovery strategies. Then a genetic algorithm is proposed to solve the developed mathematical model, and a numerical example is followed to illustrate the effectiveness and usefulness of proposed model.

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References

  1. Zhao, L.D., Jiang, Y.P.: A replenishment model for emergency rescue systems. In: Proc. of The Third International Conference on Innovative Computing, Information and Control (2008)

    Google Scholar 

  2. McDaniels, T., Chang, S., Cole, D., Mikawoz, J., Longstaff, H.: Fostering resilience to extreme events within infrastructure systems: Characterizing decision contexts for mitigation and adaptation. Global Environ. Chang. 18(2), 310–318 (2008)

    Article  Google Scholar 

  3. Bruneau, M., Chang, S.E., Eguchi, R.T., et al.: A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra. 19(4), 733–752 (2003)

    Article  Google Scholar 

  4. Berkes, F., Colding, J.F., Folke, C.: Navigating social-ecological systems: building resilience for complexity and change. Cambridge University Press, UK (2003)

    Google Scholar 

  5. Losada, C., Scaparra, M.P., O’Hanley, J.R.: Optimizing system resilience: a facility protection model with recovery time. Eur. J. Oper. Res. 217(3), 519–530 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  6. Zobel, C.W., Khansa, L.: Characterizing multi-event disaster resilience. Comput. Oper. Res. (2011) (in press)

    Google Scholar 

  7. Rose, A., Liao, S.Y.: Modeling regional economic resilience to disasters: a computable general equilibrium model of water service disruptions. J. Regional. Sci. 48(1), 75–112 (2005)

    Article  Google Scholar 

  8. Nojima, N., Ishikawa, Y., Okumura, T., Sugito, M.: Empirical estimation of lifeline outage time in seismic disaster. In: Proc. of U.S.—Japan Joint Workshop and Third Grantee Meeting, U.S.—Japan Cooperative Research on Urban Earthquake Disaster Mitigation, Seattle, WA, USA, pp. 516–517 (August 2001)

    Google Scholar 

  9. Chang, S.E., Rose, A., Shinozuka, M., Svekla, W., Tierney, K.J.: Modeling earthquake impact on urban lifeline systems: advances and integration. In: Research Progress and Accomplishments 1999-2000, MCEER (2000)

    Google Scholar 

  10. Bryson, K.M., Millar, H., Joseph, A., Mobolurin, A.: Using formal MS/OR modeling to support disaster recovery planning. Eur. J. Oper. Res. 141(3), 679–688 (2002)

    Article  MATH  Google Scholar 

  11. Altay, N., Green III, W.G.: OR/MS research in disaster operations management. Eur. J. Oper. Res. 175(1), 475–493 (2006)

    Article  MATH  Google Scholar 

  12. Sheffi, Y.: Building a resilient supply chain. Harvard Business Review 1(8), 1–4 (2005)

    Google Scholar 

  13. Yu, P.L.: A class of solutions for group decision problems. Manage. Sci. 19(8), 936–946 (1973)

    Article  MATH  Google Scholar 

  14. Zelelny, M.: Compromise programming. In: Cochrane, J.L., Zeleny, M. (eds.) Multiple-Criteria Decision Making, pp. 262–301. University of South Carolina Press, Columbia (1973)

    Google Scholar 

  15. Zelelny, M.: A concept of compromise solutions and the method of the displaced ideal. Comput. Oper. Res. 1, 479–496 (1974)

    Article  Google Scholar 

  16. Freimer, M., Yu, P.L.: Some new results on compromise solutions for group decision problems. Manage. Sci. 22(6), 688–693 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  17. Goldberg, D.E.: Genetic algorithms in search optimization and machine learning. Addison Wesley, MA (1989)

    MATH  Google Scholar 

  18. Vose, M.D.: Modelling simple genetic algorithm. In: Foundations of Genetic Algorithms II, pp. 63–73. Morgan Kaufmann Publishers (1993)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Systems Engineering, Southeast University, Nanjing, China, 210096

    Yiping Jiang & Lindu Zhao

Authors
  1. Yiping Jiang
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  2. Lindu Zhao
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Corresponding author

Correspondence to Yiping Jiang .

Editor information

Editors and Affiliations

  1. Graduate School of Information,, Production & Systems (IPS), Waseda University, Hibikino 2-7, Kitakyushu, 808-0135, Japan

    Junzo Watada

  2. Graduate School of Information Science, Nagoya University, Furo-cho, Nagoya, 464-8601, Japan

    Toyohide Watanabe

  3. Sellinger School of Business and, Management, Loyola University Maryland, Charles Street 4501 N, Baltimore, 21210, USA

    Gloria Phillips-Wren

  4. KES International, PO Box 2115, Shoreham-by-Sea, BN43 9AF, United Kingdom

    Robert J. Howlett

  5. , School of Electrical and Information, University of South Australia, 5095, Adelaide, Australia

    Lakhmi C. Jain

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© 2012 Springer-Verlag Berlin Heidelberg

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Jiang, Y., Zhao, L. (2012). A Compromise Decision-Making Model to Recover Emergency Logistics Network. In: Watada, J., Watanabe, T., Phillips-Wren, G., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29977-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-29977-3_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29976-6

  • Online ISBN: 978-3-642-29977-3

  • eBook Packages: EngineeringEngineering (R0)

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