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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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.
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.
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.
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)
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)
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)
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)
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)
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)
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.
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)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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)
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.
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.
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.
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)
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.
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.
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.
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.
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.
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)
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)
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)
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.
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.
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.
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)
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.
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.
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)
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.
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)
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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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DOI: https://doi.org/10.1007/s11633-018-1133-z