Emergency Supply Chain Management Based on Rough Set – House of Quality

  • Yuan He
  • Xue-Dong Liang
  • Fu-Min Deng
  • Zhi LiEmail author
Research Article


Due to the frequent occurrence of various emergencies in recent years, people have put forward higher requirements on the emergency supply chain management. It is of great significance to explore the key management indicators of emergency supply chain for its management and efficient operation. In order to reveal the essence of emergency supply chain management, production, procurement, distribution, storage, use, recycling and other emergencies, supply chain links are considered to establish an emergency supply chain management index system to identify the key influencing factors in the emergency supply chain. The emergency supply chain involves many management elements and the traditional qualitative analysis and comprehensive evaluation methods have their shortcomings in practice. In order to get a more suitable method, a novel evaluation model is proposed, based on Rough set–house of quality method. In this paper, Rough set is used to filter the indexes, eliminate redundant indicators, and simplify many management indicators of the emergency supply chain system to a few core indicators. Then, the house of quality is used to analyze and sort the core index to get the key management index of emergency supply chain. The effectiveness of the proposed evaluation model is validated through a series of numerical experiments. The experimental results also show that the proposed evaluation model can assist decision makers in optimizing the emergency supply chain procedure and improving the efficiency of accident rescue.


Emergency supply chain Rough set House of quality management indicators attribute reduction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    S. R. Dash, U. S. Mishra, P. Mishra. Emerging issues and opportunities in disaster response supply chain management. International Journal of Supply Chain Management, vol. 2, no. 1, pp. 55–61, 2013.Google Scholar
  2. [2]
    S. Seuring, M. Müller. From a literature review t o a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, vol. 16, no. 15, pp. 1699–1710, 2008. DOI: 10.1016/j.jclepro.2008.04.020.CrossRefGoogle Scholar
  3. [3]
    J. P. Xu, B. Li, D. S. Wu. Rough data envelopment analysis and its application t o supply chain performance evaluation. International Journal of Production Economics, vol. 122, no. 2, pp. 628–638, 2009. DOI: 10.1016/j.ijpe.2009. 06.026.CrossRefGoogle Scholar
  4. [4]
    C. D. Shi, J. H. Chen, F. L. Guo. The application research of rough sets and BP neural network in supply chain performance evaluation. Soft Science, vol. 22, no. 3, pp. 9–13, 2008. (in Chinese)MathSciNetGoogle Scholar
  5. [5]
    M. Guo, J. F. Zhu. The performance evaluation in logistics service supply chain based on fuzzy-rough sets. Systems Engineering, vol. 25, no. 7, pp. 48–52, 2007. DOI: 10.3969/j.issn.1001-4098.2007.07.009. (in Chinese)Google Scholar
  6. [6]
    S. Z. Bai, T. T. Liu. Based on the Q F D transport logistics enterprise logistics service quality improvement analysis. Logistics Engineering and Management, vol. 34, no. 9, pp. 7–10, 2012. (in Chinese)Google Scholar
  7. [7]
    H. B. Ma, J. H. Ji, B. He. Research on supply chain management for emergencies. Modern Management Science, no. 10, pp. 76–77, 80, 2009. (in Chinese)Google Scholar
  8. [8]
    Z. Y. Chen. On synergy management in emergency supply chain dealing with unconventional emergencies. Journal of Beijing Institute of Technology (Social Sciences Edition), vol. 15, no. 3, pp. 95–99, 2013. DOI: 10.15918/j.jbitss1009-3370.2013.03.010. (in Chinese)Google Scholar
  9. [9]
    Z. Y. Xu, S. K. Ren, X. S. Guo, Z. P. Yuan. Evaluation of emergency supply chain reliability under uncertain information. Operations Research and Management Science, vol. 24, no. 3, pp. 35–44, 2015. (in Chinese)Google Scholar
  10. [10]
    J. D. Hong, K. Y. Jeong, K. L. Feng. Emergency relief supply chain design and trade-off analysis. Journal of Humanitarian Logistics and Supply Chain Management, vol. 5, no. 2, pp. 162–187, 2015. DOI: 10.1108/JHLSCM-05-2014-0019.CrossRefGoogle Scholar
  11. [11]
    X. H. He, W. F. Hu, M. Xiao. Coordination optional contract mechanism of service supply chain for emergencies. Journal of Shandong University (Natural Science), vol. 50, no. 11, pp. 81–90, 2015. DOI: 10.6040/j.issn.1671-9352. 0.2014.520. (in Chinese)zbMATHGoogle Scholar
  12. [12]
    Y. J. Zheng, H. F. Ling. Emergency transportation planning in disaster relief supply chain management: A cooperative fuzzy optimization approach. Soft Computing, vol. 17, no. 7, pp. 1301–1314, 2013. DOI: 10.1007/s00500-012-0968-4.CrossRefGoogle Scholar
  13. [13]
    Y. J. Zheng, S. Y. Chen, H. F. Ling. Evolutionary optimization for disaster relief operations: A survey. Applied Soft Computing, vol. 27, pp. 553–566, 2015. DOI: 10.1016/j. asoc.2014.09.041.CrossRefGoogle Scholar
  14. [14]
    D. Alem, A. Clark, A. Moreno. Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, vol. 255, no. 1, pp. 187–206, 2016. DOI: 10.1016/j.ejor.2016.04.041.MathSciNetCrossRefzbMATHGoogle Scholar
  15. [15]
    D. J. Li, Y. Y. Li, J. X. Li, Y. Fu. Gesture recognition based on B P neural network improved by chaotic genetic algorithm. International Journal of Automation and Computing, to be published. DOI: 10.1007/s11633-017-1107-6.Google Scholar
  16. [16]
    S. P. Mishra, P. K. Dash. Short term wind speed prediction using multiple kernel pseudo inverse neural network. International Journal of Automation and Computing, vol. 15, no. 1, pp. 66–83, 2018. DOI: 10.1007/s11633-017-1086-7.CrossRefGoogle Scholar
  17. [17]
    H. Zermane, H. Mouss. Development of an internet and fuzzy based control system of manufacturing process. International Journal of Automation and Computing, vol. 14, no. 6, pp. 706–718, 2017. DOI: 10.1007/s11633-016-1027-x.CrossRefGoogle Scholar
  18. [18]
    A. M. Rao, K. Ramji, B. S. K. S. S. Rao, V. Vasu, C. Puneeth. Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm. International Journal of Automation and Computing, vol. 14, no. 3, pp. 285–294, 2017. DOI: 10.1007/s11633-016-1042-y.CrossRefGoogle Scholar
  19. [19]
    O. S. Vaidya, S. Kumar. Analytic hierarchy process: An overview of applications. European Journal of Operational Research, vol. 169, no. 1, pp. 1–29, 2006. DOI: 10.1016/j. ejor.2004.04.028.MathSciNetCrossRefzbMATHGoogle Scholar
  20. [20]
    Z. Aliakbarpoor, M. Izadikhah. Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis. International Journal of Automation and Computing, vol. 9, no. 6, pp. 609–615, 2012. DOI: 10.1007/s11633-012-0686-5.CrossRefGoogle Scholar
  21. [21]
    Y. Wang, W. F. Yang, M. Li, X. Liu. Risk assessment of floor water inrush in coal mines based on secondary fuzzy comprehensive evaluation. International Journal of Rock Mechanics and Mining Sciences, vol. 52, pp. 50–55, 2012. DOI: 10.1016/j.ijrmms.2012.03.006.CrossRefGoogle Scholar
  22. [22]
    S. I. Horikawa, T. Furuhashi, Y. Uchikawa. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 801–806, 1992. DOI: 10.1109/72.159069.CrossRefGoogle Scholar
  23. [23]
    S. R. Devi, P. Arulmozhivarman, C. Venkatesh, P. Agarwal. Performance comparison of artificial neural network models for daily rainfall prediction. International Journalof Automation and Computing, vol. 13, no. 5, pp. 417–427, 2016. DOI: 10.1007/s11633-016-0986-2.CrossRefGoogle Scholar
  24. [24]
    M. Kryszkiewicz. Rough set approach to incomplete information systems. Information Sciences, vol. 112, no.1, pp. 39–49, 1998. DOI: 10.1016/S0020-0255(98)10019-1.MathSciNetCrossRefzbMATHGoogle Scholar
  25. [25]
    G. Büyüközkan, T. Ertay, C. Kahraman, D. Ruan. Determining the importance weights for the design requirements in t he house of quality using t he fuzzy analytic network approach. International Journal of Intelligent Systems, vol. 19, no. 5, pp. 443–461, 2004. DOI: 10.1002/int. 20006.CrossRefzbMATHGoogle Scholar
  26. [26]
    C. D. Wu, Y. Zhang, M. X. Li, Y. Yue. A rough set GAbased hybrid method for robot path planning. International Journal of Automation and Computing, vol. 3, no. 1, pp. 29–34, 2006. DOI: 10.1007/s11633-006-0029-5.CrossRefGoogle Scholar
  27. [27]
    F. M. Deng, X. Y. Zhang, X. D. Liang, Z. X. Guo, C. Bao. Earthquake disaster emergency supply chain performance evaluation based on triangular fuzzy numbers. In Proceedings of International Conference on Industrial Engineering and Engineering Management, IEEE, Bali, Indonesia, pp. 1483–1487, 2016. DOI: 10.1109/IEEM.2016.7798124.Google Scholar
  28. [28]
    Y. F. Li, L. L. Xin. The construction of performance evaluation index system for intelligent supply chain. Statistics & Decision, no. 3, pp. 183–185, 2017. DOI: 10.13546/j. cnki.tjyjc.2017.03.045. (in Chinese)Google Scholar
  29. [29]
    M. Kim, R. Sharman, C. P. Cook-Cottone, H. R. Rao, S. J. Upadhyaya. Assessing roles of people, technology and structure in emergency management systems: A public sector perspective. Behaviour & Information Technology, vol. 31, no. 12, pp. 1147–1160, 2012. D O I: 10.1080/0144929X. 2010.510209.CrossRefGoogle Scholar
  30. [30]
    Z. C. Song, Y. Z. Ge, H. Duan, X. G. Qiu. Agent-based simulation systems for emergency management. International Journal of Automation and Computing, vol. 13, no. 2, pp. 89–98, 2016. DOI: 10.1007/s11633-016-0958-6.CrossRefGoogle Scholar
  31. [31]
    Y. Z. Jin, H. Zhou, H. J. Yang, S. J. Zhang, J. D. Ge. An approach t o locating delayed activities in software processes. International Journal of Automation and Computing, vol. 15, no. 1, pp. 115–124, 2018.CrossRefGoogle Scholar
  32. [32]
    G. F. Qiu, J. Y. Wang. Green construction project evaluation model based on Rough set. Statistics & Decision, no. 11, pp. 178–181, 2015. DOI: 10.13546/j.cnki.tjyjc. 2015.11.047. (in Chinese)MathSciNetGoogle Scholar
  33. [33]
    C. X. Dou, T. Gui, Y. F. Bi, J. Z. Yang, X. G. Li. Assessment of power quality based on D-S evidence theory. International Journal of Automation and Computing, vol. 11, no. 6, pp. 635–643, 2014. DOI: 10.1007/s11633-014-0837-y.CrossRefGoogle Scholar
  34. [34]
    A. T. Yang, L. D. Zhao. Supply chain network equilibrium with revenue sharing contract under demand disruptions. International Journal of Automation and Computing, vol. 8, no. 2, pp. 177–184, 2011. DOI: 10.1007/s11633-011-0571-7.CrossRefGoogle Scholar
  35. [35]
    G. Behzadi, M. J. O'Sullivan, T. L. Olsen, A. Zhang. Agribusiness supply chain risk management: A review of quantitative decision models. Omega, vol. 79, pp. 21–42, 2018. DOI: 10.1016/ Scholar
  36. [36]
    S. Pettit, A. Beresford. Critical success factors in the context of humanitarian aid supply chains. International Journal of Physical Distribution & Logistics Management, vol. 39, no. 6, pp. 450–468, 2009. DOI: 10.1108/09600030910 985811.CrossRefGoogle Scholar
  37. [37]
    Z. Pawlak. Rough sets. International Journal of Computer & Information Sciences, vol. 11, no. 5, pp. 341–356, 1982. DOI: 10.1007/BF01001956.MathSciNetCrossRefzbMATHGoogle Scholar
  38. [38]
    G. Y. Wang, Y. Y. Yao, H. Yu. A survey on rough set theory and applications. Chinese Journal of Computers, vol. 32, no. 7, pp. 1229–1246, 2009. DOI: 10.3724/SP.J.1016. 2009.01229. (in Chinese)MathSciNetCrossRefGoogle Scholar
  39. [39]
    X. R. Yin. Discrete method of continuous attributes based on Rough set. Computer Engineering and Design, vol. 27, no. 11, pp. 2038–2040, 2006. DOI: 10.3969/j.issn.1000-7024.2006.11.040. (in Chinese)Google Scholar
  40. [40]
    X. M. Zhang. Study on evaluation index weight of equipment manufacturing enterprises innovation capability based on Rough set and AHM. China Soft Science, no. 6, pp. 151–158, 2014. (in Chinese)Google Scholar
  41. [41]
    Q. Shen, R. Jensen. Rough sets, their extensions and applications. International Journal of Automation and Computing, vol. 4, no. 3, pp. 217–228, 2007. DOI: 10.1007/s11633-007-0217-y.CrossRefGoogle Scholar
  42. [42]
    C. Bean, C. Kambhampati. Autonomous clustering using rough set theory. International Journal of Automation and Computing, vol. 5, no. 1, pp. 90–102, 2008. DOI: 10.1007/s11633-008-0090-3.CrossRefGoogle Scholar
  43. [43]
    A. Ansari, B. Modarress. Quality function deployment: The role of suppliers. International Journal of Purchasing and Materials Management, vol. 30, no. 3, pp. 27–35, 1994. DOI: 10.1111/j.1745-493X.1994.tb00271.x.CrossRefGoogle Scholar
  44. [44]
    Y. Z. Chen, J. F. Tang, R. T. Hou, L. Y. Ren. Productprogramming model based on QFD. Journal of Northeastern University (Natural Science), vol. 23, no. 8, pp. 809–812, 2002. DOI: 10.3321/j.issn:1005-3026.2002.08. 027. (in Chinese)Google Scholar
  45. [45]
    J. H. Ruan, P. Shi, C. C. Lim, X. P. Wang. Relief supplies allocation and optimization by interval and fuzzy number approaches. Information Sciences, vol. 303, pp. 15–32, 2015. DOI: 10.1016/j.ins.2015.01.002.MathSciNetCrossRefzbMATHGoogle Scholar
  46. [46]
    T. Park, K. J. Kim. Determination of an optimal set of design requirements using house of quality. Journal of Operations Management, vol. 16, no. 5, pp. 569–581, 1998. DOI: 10.1016/S0272-6963(97)00029-6.CrossRefGoogle Scholar
  47. [47]
    X. Liu. Construction of disaster relief materials reserve system, Sichuan walk in the forefront of the country. Sichuan Daily, 2012-06-15(002). (in Chinese)Google Scholar
  48. [48]
    J. H. Ruan, X. P. Wang, F. T. S. Chan, Y. Shi. Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. International Journal of Production Research, vol. 54, no. 14, pp. 4368–4386, 2016. DOI: 10.1080/00207543.2016.1174344.CrossRefGoogle Scholar
  49. [49]
    X. H. Wang, F. Li, L. Liang. The deconstruction of a relief material supply network and corresponding structure optimization model. Chinese Journal of Management Science, vol. 25, no. 1, pp. 139–150, 2017. DOI: 10.16381/.cnki.issn1003-207x.2017.01.015. (in Chinese)Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business SchoolSichuan UniversityChengduChina

Personalised recommendations