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

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Abstract

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.

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References

  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. 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: https://doi.org/10.1016/j.jclepro.2008.04.020.

    Article  Google Scholar 

  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: https://doi.org/10.1016/j.ijpe.2009.06.026.

    Article  Google Scholar 

  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)

    MathSciNet  Google Scholar 

  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: https://doi.org/10.3969/j.issn.1001-4098.2007.07.009. (in Chinese)

    Google Scholar 

  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. 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. 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. 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. 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: https://doi.org/10.1108/JHLSCM-05-2014-0019.

    Article  Google Scholar 

  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)

    MATH  Google Scholar 

  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: https://doi.org/10.1007/s00500-012-0968-4.

    Article  Google Scholar 

  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: https://doi.org/10.1016/j.asoc.2014.09.041.

    Article  Google Scholar 

  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: https://doi.org/10.1016/j.ejor.2016.04.041.

    Article  MathSciNet  MATH  Google Scholar 

  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: https://doi.org/10.1007/s11633-017-1107-6.

  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: https://doi.org/10.1007/s11633-017-1086-7.

    Article  Google Scholar 

  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: https://doi.org/10.1007/s11633-016-1027-x.

    Article  Google Scholar 

  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: https://doi.org/10.1007/s11633-016-1042-y.

    Article  Google Scholar 

  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: https://doi.org/10.1016/j.ejor.2004.04.028.

    Article  MathSciNet  MATH  Google Scholar 

  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: https://doi.org/10.1007/s11633-012-0686-5.

    Article  Google Scholar 

  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: https://doi.org/10.1016/j.ijrmms.2012.03.006.

    Article  Google Scholar 

  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: https://doi.org/10.1109/72.159069.

    Article  Google Scholar 

  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: https://doi.org/10.1007/s11633-016-0986-2.

    Article  Google Scholar 

  24. M. Kryszkiewicz. Rough set approach to incomplete information systems. Information Sciences, vol. 112, no.1, pp. 39–49, 1998. DOI: https://doi.org/10.1016/S0020-0255(98)10019-1.

    Article  MathSciNet  MATH  Google Scholar 

  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: https://doi.org/10.1002/int.20006.

    Article  MATH  Google Scholar 

  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: https://doi.org/10.1007/s11633-006-0029-5.

    Article  Google Scholar 

  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: https://doi.org/10.1109/IEEM.2016.7798124.

    Google Scholar 

  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: https://doi.org/10.13546/j.cnki.tjyjc.2017.03.045. (in Chinese)

    Google Scholar 

  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.

    Article  Google Scholar 

  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: https://doi.org/10.1007/s11633-016-0958-6.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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: https://doi.org/10.13546/j.cnki.tjyjc.2015.11.047. (in Chinese)

    MathSciNet  Google Scholar 

  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: https://doi.org/10.1007/s11633-014-0837-y.

    Article  Google Scholar 

  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: https://doi.org/10.1007/s11633-011-0571-7.

    Article  Google Scholar 

  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: https://doi.org/10.1016/j.omega.2017.07.005.

    Article  Google Scholar 

  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: https://doi.org/10.1108/09600030910985811.

    Article  Google Scholar 

  37. Z. Pawlak. Rough sets. International Journal of Computer & Information Sciences, vol. 11, no. 5, pp. 341–356, 1982. DOI: https://doi.org/10.1007/BF01001956.

    Article  MathSciNet  MATH  Google Scholar 

  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: https://doi.org/10.3724/SP.J.1016.2009.01229. (in Chinese)

    Article  MathSciNet  Google Scholar 

  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: https://doi.org/10.3969/j.issn.1000-7024.2006.11.040. (in Chinese)

    Google Scholar 

  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. 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: https://doi.org/10.1007/s11633-007-0217-y.

    Article  Google Scholar 

  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: https://doi.org/10.1007/s11633-008-0090-3.

    Article  Google Scholar 

  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: https://doi.org/10.1111/j.1745-493X.1994.tb00271.x.

    Article  Google Scholar 

  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. 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: https://doi.org/10.1016/j.ins.2015.01.002.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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: https://doi.org/10.1080/00207543.2016.1174344.

    Article  Google Scholar 

  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 

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Correspondence to Zhi Li.

Additional information

Recommended by Associate Editor Dong-Ling Xu

Yuan He is a master student in logistics engineering at Sichuan University, China.

Her research interests include supply chain management, specically the emergency supply chain management, supply chain process optimization and logistics system operation management.

Xue-Dong Liang received the Ph. D. degree in mechanical engineering from Chongqing University, China in 2009. He is an associate professor of industrial engineering and engineering management department at Sichuan University, China. He worked at Purdue University as a visiting scholar in from 2007 to 2008. He has published about 50 refereed journal and conference papers.

His research interests include supply chain management, project management, logistics and collaborative design.

Fu-Min Deng received the Ph. D. degree in management science and engineering from Sichuan University, China in 2008. He is a professor of Industrial Engineering and Engineering Management Department at Sichuan University, China. He has published about 30 refereed journal and conference papers. He received Sichuan Province Science and Technology Progress Award and Sichuan sixteenth outstanding achievements in Social Science Award.

His research interests include supply chain management, emergency management and technical economics and management.

Zhi Li received the Ph. D. degree in supply chain management at the Hongkong Polytechnic University, China in 2014. He is a lecturer of Industrial Engineering and Engineering Management Department at Sichuan University, China. He has published about 10 refereed journal and conference papers.

His research interests include supply chain management and logistic.

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He, Y., Liang, XD., Deng, FM. et al. Emergency Supply Chain Management Based on Rough Set – House of Quality. Int. J. Autom. Comput. 16, 297–309 (2019). https://doi.org/10.1007/s11633-018-1133-z

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