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A new methodology to support group decision-making for IoT-based emergency response systems

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Abstract

An emergency response system (ERS) can assist a municipality or government in improving its capabilities to respond urgent and severe events. The responsiveness and effectiveness of an ERS relies greatly on its data acquisition and processing system, which has been evolved with information technology (IT). With the rapid development of sensor networks and cloud computing, the emerging Internet of things (IoT) tends to play an increasing role in ERSs; the networks of sensors, public services, and experts are able to interact with each other and make scientific decisions to the emergencies based on real-time data. When group decision making is required in an ERS, one critical challenge is to obtain the good understanding of massive and diversified data and make consensus group decisions under a high-level stress and strict time constraint. Due to the nature of unorganized data and system complexity, an ERS depends on the perceptions and judgments of experts from different domains; it is challenging to assess the consensus of understanding on the collected data and response plans before appropriate decisions can be reached for emergencies. In this paper, the group decision-making to emergency situations is formulated as a multiple attribute group decision making (MAGDM) problem, the consensus among experts is modeled, and a new methodology is proposed to reach the understanding of emergency response plans with the maximized consensus in course of decision-making. In the implementation, the proposed methodology in integrated with computer programs and encapsulated as a service on the server. The objectives of the new methodology are (i) to enhance the comprehensive group cognizance on emergent scenarios and response plans and (ii) to accelerate the consensus for decision making with an intelligent clustering algorithm, (iii) to adjust the experts’ opinions without affecting the reliability of the decision when the consensus cannot be reached from the preliminary decision-making steps. Partitioning Around Medoids (PAM) has been applied as the clustering algorithm, Particle Swarm Optimization (PSO) is deployed to adjust evaluation values automatically. The methodology is applied in a case study to illustrate its effectiveness in converging group opinions and promoting the consensus of understanding on emergencies.

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References

  • Akyilidiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor network: a survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  • Anandaligam, G. (1989). A multiagent multiattribute approach for conflict resolution in acid rain impact mitigation. IEEE Transactions on Systems, Man, and Cybernetics, 19(5), 1142–1153.

    Article  Google Scholar 

  • Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: a survey. Computer Networks, 54, 2787–2805.

    Article  Google Scholar 

  • Bai, X., & Yan, H. (2011). Study and design of the safe HIS on the Internet of things. Proceedings of 2011 Fourth International Symposium on Computational Intelligence and Design.

  • Baumgart, L., & Bass, E. (2008). Emergency management decision making during severe weather. Weather and Forecasting, 23, 1268–1279.

    Article  Google Scholar 

  • Belanger, B. (2001). The human side of decision-making. www.au.af.mil/au/awc/awcgate/readings/human.htm.

  • Ben-Arieh, D., & Chen, Z. (2006). Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations. IEEE Transactions on Systems, Man, and Cybernetics - Part A, 36, 558–568.

    Article  Google Scholar 

  • Ben-Arieh, D., Easton, T., & Evans, B. (2009). Minimum cost consensus with quadratic cost functions. IEEE Transactions on Systems, Man, and Cybernetics - Part A, 39, 210–217.

    Article  Google Scholar 

  • Bi, Z., & Kang, B. (2010). Enhancement of adaptability of parallel kinematic machines with an adjustable platform. ASME Journal of Manufacturing Science and Engineering, 132(9). doi:10.1115/1.4003120.

  • Bi, Z., & Kang, B. (2011). Sensing and responding to the changes of geometric surfaces in reconfigurable manufacturing and assembly. Enterprise Information Systems. doi:10.1080/17517575.2012.654826.

  • Bi, Z., Lang, S., & Shen, W. (2008). Reconfigurable manufacturing systems: the state of the art. International Journal of Production Research, 46(4), 967–992.

    Article  Google Scholar 

  • Bordogna, G., Fedrizzi, M., & Pasi, G. (1997). A linguistic modeling of consensus in group decision making based on OWA operators. IEEE Transactions on Systems, Man, and Cybernetics - Part A, 27, 126–132.

    Article  Google Scholar 

  • Cabrerizo, F., Moreno, J., Perez, I., & Herrera-Viedma, E. (2010). Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks. Soft Computing, 14, 451–463.

    Article  Google Scholar 

  • Cabrerizo, F., Perez, I., & Herrera-Viedma, E. (2010). Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information. Knowledge-Based Systems, 23, 169–181.

    Article  Google Scholar 

  • Chen, Z. (2011). Quasi real-time evaluation system for seismic disaster based on Internet of things. Proceedings of 2011 IEEE International Conferences on Internet of Things, and Cyber, Physical and Social Computing, pp. 520–524, doi:10.1109/iThings/CPSCom.2011.112.

  • Chen, X., & Fan, Z. (2006). Study on the assessment level of experts based on linguistic assessment matrices. Journal of Systems Engineering, 24(1), 111–115.

    Google Scholar 

  • Chen, X., & Fan, Z. (2007). Study on assessment level of experts based on difference preference information. Systems Engineering - Theory & Practice, 27(2), 27–35.

    Article  Google Scholar 

  • Chen, X., He, Y., & Huang, H. (2011). An approach to automatic development of interlocking logic based on statechart. Enterprise Information Systems, 5(3), 273–286.

    Article  Google Scholar 

  • Chiang, D., Lin, C., & Chen, M. (2011). The adaptive approach for storage assignment by6 mining data of warehouse management system for distribution centres. Enterprise Information Systems, 5(2), 219–234.

    Article  Google Scholar 

  • Chiclana, F., Mata, F., Martinez, L., Herrera-Viedma, E., & Alonso, S. (2008). Integration of a consistency control module with in a consensus decision making model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16, 35–53.

    Article  Google Scholar 

  • Dong, Y., Zhang, G., Hong, W., & Xu, Y. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49, 281–289.

    Article  Google Scholar 

  • Duan, L., & Xu, L. (2012). Business intelligence for enterprise systems: a survey. IEEE Transactions on Industrial Informatics, 8(3), 679–687.

    Article  Google Scholar 

  • Duan, L., Xu, L., Guo, F., Lee, J., & Yan, B. (2007). A local-density based spatial clustering algorithm with noise. Information Systems, 32, 978–986.

    Article  Google Scholar 

  • Duan, L., Street, W., & Xu, L. (2011). Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterprise Information Systems, 5(2), 169–181.

    Article  Google Scholar 

  • Eberhart, R., & Kennedy, J. (1995). A New optimizer using particle swarm theory. Procceedings 6th International Symposium on Micro Machine and Human Science, pp. 39–43, Nagoya, Japan, 1995.

  • Fan, Z., & Liu, Y. (2010). A method for group decision-making based on multi-granularity uncertain linguistic information. Expert Systems with Applications, 37, 4000–4008.

    Article  Google Scholar 

  • Fritzsche, M., Kittel, K., Blankenburg, A., & Vajna, S. (2012). Multidisciplinary design optimisation of a recurve bow based on applications of the autogenetic design theory and distributed computing. Enterprise Information Systems, 6(3), 329–343.

    Article  Google Scholar 

  • Fu, C., & Yang, S. (2010). The group consensus based evidential reasoning approach for multiple attributive group decision analysis. European Journal of Operational Research, 206, 601–608.

    Article  Google Scholar 

  • Fu, C., Zhang, G., Yang, J., & Liu, X. (2011). Study on the contract characteristics of Internet architecture. Enterprise Information Systems, 5(4), 495–513.

    Article  Google Scholar 

  • Gao, H. (2008). Constructing national index system for evaluation of emergency response plans. Business Economy, 009-6043, 11-0039-02.

    Google Scholar 

  • Ghezzi, C., & Pacifici, F. (2008). Evolution of software composition mechanisms: a survey. In D. Lucia, F. Ferrucci, G. Tortora, & M. Tucci (Eds.), Emerging methods, technologies, and process management in software engineering (pp. 3–19). New York: J. Wiley and Sons.

    Google Scholar 

  • Gluhak, A., & van Kranenburg, R. (2012). New instruments of governance for our societies. Proceedings of the 1st International IEEE Workshop on Hot Topics in Pervasive Computing, Lugano.

  • Guo, J., Xu, L., Xiao, G., & Gong, Z. (2012). Improving multilingual semantic interoperation in cross-organizational enterprise systems through concept disambiguation. IEEE Transactions on Industrial Informatics, 8(3), 647–658.

    Article  Google Scholar 

  • Hahn, E. (2010). Judgmental consistency and consensus in stochastic multicriteria decision making. Expert Systems with Applications, 37, 3784–3791.

    Article  Google Scholar 

  • Herrera, F., & Herrera-Viedma, E. (2000). Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets and Systems, 115, 67–82.

    Article  Google Scholar 

  • Herrera, F., Herrera-Viedma, E., & Verdegay, J. (1996). Direct approach processes in group decision making using linguistic OWA operators. Fuzzy Sets and Systems, 79, 175–190.

    Article  Google Scholar 

  • Herrera, F., Herrera-Viedma, E., & Verdegay, J. (1997). A rational consensus model in group decision making using linguistic assessments. Fuzzy Sets and Systems, 88, 31–49.

    Article  Google Scholar 

  • Herrera-Viedma, E., Martinez, L., Mata, F., & Chiclana, F. (2005). A consensus support systems model for group decision making problems with multigranular linguistic preference relations. IEEE Transactions on Fuzzy Systems, 13, 644–658.

    Article  Google Scholar 

  • Herrera-Viedma, E., Alonso, S., Chiclana, F., & Herrera, F. (2007). A consensus model for group decision making with incomplete fuzzy preference elations. IEEE Transactions on Fuzzy Systems, 15, 863–877.

    Article  Google Scholar 

  • Hsu, L., Horng, S., Fan, P., Khan, M., Wang, Y., & Run, R. (2011). MTPSO algorithm for solving planer graph coloring problem. Expert Systems with Applications, 38, 5525–5531.

    Article  Google Scholar 

  • Ingvaldsen, J., & Gulla, J. (2012). Industrial application of semantic process mining. Enterprise Information Systems, 6(2), 139–163.

    Article  Google Scholar 

  • Jiang, Y., Xu, L., Wang, H., & Wang, H. (2009). Influencing factors for predicting financial performance based on genetic algorithms. Systems Research and Behavioral Science, 26(6), 661–673.

    Article  Google Scholar 

  • Kacprzyk, J., Fedrizzi, M., & Nurmi, H. (1992). Group decision making and consensus under fuzzy preferences and fuzzy majority. Fuzzy Sets and Systems, 49, 21–31.

    Article  Google Scholar 

  • Kaufman, L., & Rousseeuw, R. (1987). Clustering by means of medoids. In Y. Dodge (Ed.), Statistical data analysis based on the L, norm (pp. 405–416). North-Holland: Elsevier.

    Google Scholar 

  • Kaufman, L., & Rousseeuw, P. (1990). Finding groups in data: an introduction to cluster analysis. New York: John Wiley & Sons. 0471878766.

    Book  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948.

  • Kennedy, J., Eberhart, R., & Shi, Y. (2001). The particle swarm (pp. 287–325). San Francisco: Swarm Intelligence.

    Google Scholar 

  • Kim, S., & Ahn, B. (1997). Group decision making procedure considering preference strength under incomplete information. Computers and Operations Research, 24, 1101–1112.

    Article  Google Scholar 

  • Kim, S., Choi, S., & Kim, J. (1999). An interactive procedure for multiple attribute group decision making with incomplete information: range-based approach. European Journal of Operational Research, 118, 139–152.

    Article  Google Scholar 

  • Kowalski, K. (1995). A human component to consider in your emergency management plans: the critical incident stress factor. Safety Science, 20, 115–123.

    Article  Google Scholar 

  • Kowalski, K., & Podlesny, A. (2000). The effects of disaster on workers: a study of burnout in investigators of serious accidents and fatalities in the US mining industry. http://stacks.cdc.gov/view/cdc/9430/.

  • Kowalski, K., & Trakofler, C. (2003). Judgment and decision making under stress: an overview for emergency managers. International Journal of Emergency Management, 1(3), 278–289.

    Article  Google Scholar 

  • Kumar, S., Kadow, B., & Lamkin, M. (2011). Challenges with the introduction of radio-frequency identification systems into a manufacturer’s supply chain—a pilot study. Enterprise Information Systems, 5(2), 235–253.

    Article  Google Scholar 

  • Li, L. (2011). Introduction: advanves in e-business engineering. Information Technology and Management, 12(2), 49–50.

    Article  Google Scholar 

  • Li, L., & Liu, J. (2012). An efficient and flexible web services-based multidisciplinary design optimisation framework for complex engineering systems. Enterprise Information Systems, 6(3), 345–371.

    Article  Google Scholar 

  • Li, L., Xu, L., Jeng, H., Naik, D., Allen, T., & Frontini, M. (2008). Creation of environmental health information system for public health service: a pilot study. Information Systems Frontiers, 10(5), 531–543.

    Article  Google Scholar 

  • Li, D., Huang, Z., & Chen, G. (2010). A systematic approach to heterogeneous multiattribute group decision making. Computers and Industrial Engineering, 59, 561–572.

    Article  Google Scholar 

  • Li, F., Xu, L., Jin, C., & Wang, H. (2011a). Intelligent bionic genetic algorithm (IB-GA) and its convergence. Expert Systems with Applications, 38(7), 8804–8811.

    Article  Google Scholar 

  • Li, F., Xu, L., Jin, C., & Wang, H. (2011b). Structure of multi-stage composite genetic algorithm (MSC-GA) and its performance. Expert Systems with Applications, 38(7), 8929–8937.

    Article  Google Scholar 

  • Li, L., Ge, R., Zhou, S., & Valerdi, R. (2012). Guest editorial integrated healthcare information systems. IEEE Transactions on Information Technology in Biomedicine, 16(4), 515–517.

    Article  Google Scholar 

  • Li, N., Yi, W., Bi, Z., Kong, H., & Gong, G. (2012). An optimization method for complex product design. Enterprise Information Systems. doi:10.1080/17517575.2012.743682.

  • Li, S., Xu, L., Wang, X., & Wang, J. (2012). Integration of hrbrid wireless networks in cloud services oriented enterprise information systems. Enterprise Information Systems, 6(2), 165–187.

    Article  Google Scholar 

  • Lin, S., Horng, S., Kao, T., Huang, D., Fahn, C., & Lai, J. (2010). An efficient bi-objective personal assignment based on a hybrid particle swarm optimization model. Expert Systems with Applications, 37, 7825–7830.

    Article  Google Scholar 

  • Liu, Q., & Wang, C. (2012). Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation. Enterprise Information Systems, 6(3), 315–327.

    Article  Google Scholar 

  • Liu, B., Cao, S., & He, W. (2011). Distributed data mining for e-business. Information Technology and Management, 12(2), 67–79.

    Article  Google Scholar 

  • Luo, J., Xu, L., Jamont, J., Zeng, L., & Shi, Z. (2007). A flood decision support system on agent grid: method and implementation. Enterprise Information Systems, 1(1), 49–68.

    Article  Google Scholar 

  • Michahelles, F., Thiesse, F., Schmidt, A., & Williams, J. (2007). Pervasive RFID and near field communication technology. IEEE Pervasive Computing, 6(3), 94–96.

    Article  Google Scholar 

  • Park, K., & Kim, S. (1997). Tools for interactive multi-attribute decision making with incompletely identified information. European Journal of Operational Research, 98, 111–123.

    Article  Google Scholar 

  • Parreiras, R., Ekel, P., Martini, S., & Palhares, R. (2010). A flexible consensus scheme for multicriteria group decision making under linguistic assessments. Information Sciences, 180, 1075–1089.

    Article  Google Scholar 

  • Pasley, J. (2005). How BPEL and SOA are changing web services development. IEEE Internet Computing, 9(3), 60–67.

    Article  Google Scholar 

  • Ramanathan, R., & Ganesh, L. (1994). Group preference aggregation methods employed in AHP: an evaluation and an intrinsic process for deriving members’ weightages. European Journal of Operational Research, 79, 249–265.

    Article  Google Scholar 

  • Ren, F., & Quan, C. (2012). Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing. Information Technology and Management, 13(4), 321–332.

    Article  Google Scholar 

  • Ren, L., Zhang, L., Tao, F., Zhang, X., Luo, Y., & Zhang, Y. (2012). A methodology towards virtualisation-based high performance simulation platform supporting multidisciplinary design of complex products. Enterprise Information Systems, 6(3), 267–290.

    Article  Google Scholar 

  • Robati, A., & Barani, G. (2012). Balanced fuzzy particle swarm optimization. Applied Mathematical Modelling, 36, 2169–2177.

    Article  Google Scholar 

  • Rosello, L., Prats, F., Agell, N., & Sanchez, M. (2010). Measuring consensus in group decisions by means of qualitative reasoning. International Journal of Approximate Reasoning, 51, 441–452.

    Article  Google Scholar 

  • Rousseeuw, P. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65.

    Article  Google Scholar 

  • Saaty, T. (1994). A ratio scale metric and compatibility of ratio scales: the possibility of Arrow’s impossibility theorem. Applied Mathematics Letters, 7, 51–57.

    Article  Google Scholar 

  • Salo, A. (1995). Interactive decision aiding for group decision support. European Journal of Operational Research, 84, 134–149.

    Article  Google Scholar 

  • Shan, S., Wang, L., & Li, L. (2012a). Modeling of emergency response decision-making process using stochastic Petri net: an e-service perspective. Information Technology and Management, 13(4), 363–376.

    Article  Google Scholar 

  • Shan, S., Wang, L., Li, L., & Chen, Y. (2012b). An emergency response decision support system framework for application in e-government. Information Technology and Management, 13(4), 411–427.

    Article  Google Scholar 

  • Sinha, R. (2005). Impact of experience on decision making in emergency situation. Lulea University of Technology, http://epubl.ltu.se/1402-1781/2005/15/LTU-CDUPP-0515-SE.pdf.

  • Streufert, S. (2005). Emergency decision making and metacomplexity. Proceedings of the 2nd International ISCRAM Conference, Brussels, Belgium, 2005.

  • Tang, Z., & Bagchi, K. (2010). Globally convergent particle swarm optimization via ranch-and-bound. Computer and Information Science, 3(4).

  • Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition. Third Edition, Elsevier.

  • van der Laan, M., Pollardy, K., & Bryan, J. (2002). New Partitioning Around Medoids Algorithm. UC Berkeley Division of Biostatistics Working Paper Series, pp. 105–116.

  • van Sinderen, M., & Almeida, J. (2011). Editorial empowering enterprises through next generation enterprise computing. Enterprise Information Systems, 5(1), 1–8.

    Article  Google Scholar 

  • Vaught, C. (2000). Behavioural and organizational dimensions of underground mine fires. http://stacks.cdc.gov/view/cdc/6398/.

  • Vaught, C., Mallett, L., Kowalski, K., & Brnich, M. (1997). Workers response to realistic evacuation training. http://stacks.cdc.gov/view/cdc/9283/.

  • Wang, C. (2012). Editorial advances in information integration infrastructures supporting multidisciplinery design optimisation. Enterprise Information Systems, 6(3), 265.

    Article  Google Scholar 

  • Wang, C., & Xu, L. (2008). Parameter mapping and data transformation for engineering application integration. Information Systems Frontiers, 10(5), 589–600.

    Article  Google Scholar 

  • Wang, X., & Xu, X. (2012). DIMP:an interoperable solution for software integration and product data exchange. Enterprise Information Systems, 6(3), 291–314.

    Article  Google Scholar 

  • Wang, P., Xu, L., Zhou, S., Fan, Z., Li, Y., & Feng, S. (2010). Novel Bayesian learning method for information aggregation in modular neural networks. Expert Systems with Applications, 37(2), 1071–1074.

    Article  Google Scholar 

  • Wang, L., Wang, J., Hao, L., & Hua, F. (2011). Research on e-government evaluation model based on the principal component analysis. Information Technology and Management, 12(2), 173–185.

    Article  Google Scholar 

  • Wang, L., Zeng, J., & Xu, L. (2011). A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization. Information Technology and Management, 12(2), 111–119.

    Article  Google Scholar 

  • Wetzstein, B., Leitner, P., Rosenberg, F., Dustdar, S., & Leymann, F. (2011). Identifying influential factors of business process performance using dependency analysis. Enterprise Information Systems, 5(1), 79–98.

    Article  Google Scholar 

  • Wickens, C. (1992). Engineering psychology and human performance. 2nd edition, Harper Collins Publication.

  • Wikipedia (2012). “July 2012 Beijing flood,” http://en.wikipedia.org/wiki/July_2012_Beijing_flood, 2012.

  • Xie, K., Chen, G., Wu, Q., Liu, Y., & Wang, P. (2011). Research on the group decision-making about emergency event based on network technology. Information Technology and Management, 12(2), 137–147.

    Article  Google Scholar 

  • Xie, K., Liu, J., Chen, G., Wang, P., & Chaudhry, S. (2012). Group decision-making in an unconventional emergency situation using agile Delphi approach. Information Technology and Management, 13(4), 351–361.

    Article  Google Scholar 

  • Xu, L. (1990). Linguistic approach to the multi-criteria ranking problem. International Journal of Systems Science, 21, 1773–1782.

    Article  Google Scholar 

  • Xu, Z. (2004). Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment. Information Sciences, 168, 171–184.

    Article  Google Scholar 

  • Xu, Z. (2005). Deviation measures of linguistic preference relations in group decision making. Omega, 33, 249–254.

    Article  Google Scholar 

  • Xu, Z. (2009). An automatic approach to reaching consensus in multiple attribute group decision making. Computers and Industrial Engineering, 56, 1369–1374.

    Article  Google Scholar 

  • Xu, L. (2011a). Enterprise systems: state-of-the-art and future trends. IEEE Transactions on Industrial Informatics, 7(4), 630–640.

    Article  Google Scholar 

  • Xu, L. (2011b). Information architecture for supply chain quality management. International Journal of Production Research, 49(1), 183–198.

    Article  Google Scholar 

  • Xu, Z., & Chen, J. (2007). An interactive method for fuzzy multiple attribute group decision making. Information Sciences, 177, 248–263.

    Article  Google Scholar 

  • Xu, J., & Wu, Z. (2011). A discrete consensus support model for multiple attribute group decision making. Knowledge-Based Systems, 24, 1196–1202.

    Article  Google Scholar 

  • Xu, S., & Xu, L. (2011). Management; a scientific discipline for humanity. Information Technology and Management, 12(2), 51–54.

    Article  Google Scholar 

  • Xu, L., Liang, N., & Gao, Q. (2008). An integrated approach for agricultural ecosystem management. IEEE Transactions on Systems, Man and Cybernetics - Part C, 38(4), 590–599.

    Article  Google Scholar 

  • Xu, L., Tan, W., Zhen, H., & Shen, W. (2008). An approach to enterprise process dynamic modeling supporting enterprise process evolution. Information Systems Frontiers, 10(5), 611–624.

    Article  Google Scholar 

  • Xu, L., Wang, C., Bi, Z., & Yu, J. (2012). AutoAssem: an automated assembly planning system for complex products. IEEE Transactions on Industrial Informatics, 8(3), 669–678.

    Article  Google Scholar 

  • Xu, S., Xu, L., & Basl, J. (2012). Introduction: advances in e-business engineering. Information Technology and Management, 13(4), 201–204.

    Article  Google Scholar 

  • Yin, Y., Fan, Y., & Xu, L. (2012). EMG & EPP-integrated human–machine interface between the paralyzed and rehabilitation exoskeleton. IEEE Transactions on Information Technology in Biomedicine, 16(4), 542–549.

    Article  Google Scholar 

  • Yue, Z. (2011). Developing a straightforward approach for group decision making based on determining weights of decision makers. Applied Mathematical Modelling. doi:10.1016/j.apm.2011.11.041.

  • Zeng, L., Li, L., & Duan, L. (2012a). Business intelligence in enterprise computing environment. Information Technology and Management, 13(4), 297–310.

    Article  Google Scholar 

  • Zeng, L., Li, L., Duan, L., Lu, K., Shi, Z., Wang, M., Wu, W., & Luo, P. (2012b). Distributed data mining: a survey. Information Technology and Management, 13(4), 403–409.

    Article  Google Scholar 

  • Zhou, Z., Valerdi, R., & Zhou, S. (2012). Guest editorial special section on enterprise systems. IEEE Transactions on Industrial Informatics, 8(3), 630.

    Article  Google Scholar 

  • Zhu, X., Wang, H., Xu, L., & Li, H. (2008). Predicting stock index increments by neural networks: the role of trading volume under different horizons. Expert Systems with Applications, 34(4), 3043–3054.

    Article  Google Scholar 

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Acknowledgment

This study has been sponsored by the National Nature Science Foundation of China under the grant entitled Study on Parallel Intelligent Optimization Simulation with Combination of Qualitative and Quantitative Method (grant no. 61004089). It has also been supported by Doctoral Fund of Chinese Ministry of Education under the grant entitled “Research of a Simulation Optimization Design Method for Complex Product” (grant no. 20091102120013).

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  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Ni Li, Minghui Sun, Zeya Su & Chao Wang

  2. Department of Engineering, Indiana University-Purdue University Fort Wayne, Fort Wayne, IN, 46805-1499, USA

    Zhuming Bi

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Li, N., Sun, M., Bi, Z. et al. A new methodology to support group decision-making for IoT-based emergency response systems. Inf Syst Front 16, 953–977 (2014). https://doi.org/10.1007/s10796-013-9407-z

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