簡易檢索 / 詳目顯示

研究生: Alyssa Marinelle S. Espiritu
Espiritu
論文名稱: An Integrated Fuzzy Entropy and Failure Mode Effect and Analysis Weighing Method in Assessing Supply Chain Risk Factors and Applied Intuitionistic Fuzzy TOPSIS and VIKOR in Assessing Impact on Company Metrics
An Integrated Fuzzy Entropy and Failure Mode Effect and Analysis Weighing Method in Assessing Supply Chain Risk Factors and Applied Intuitionistic Fuzzy TOPSIS and VIKOR in Assessing Impact on Company Metrics
指導教授: 曾世賢
Shih-Hsien Tseng
口試委員: 賴正育
Cheng-Yu Lai
陳基祥
Chi-Hsiang Chen
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 96
外文關鍵詞: Supply Chain Risks, Intuitionistic Fuzzy, Entropy Weighing Method, Technique for Order of Preference by Similarity to Ideal Solution, Višekriterijumsko Kompromisno Rangiranjeis
相關次數: 點閱:179下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

  • The unanticipated events and recent disruptions in supply chain cause a huge damage
    on businesses. Due to increasing number of supply chain risks to consider, predicting the impact of these risks are getting more difficult. In this study several techniques are combined and utilized to investigate the impact of supply chain risks on company measures. An Intuitionistic Fuzzy Entropy Weighing Method and Failure Mode and Effect Analysis method were combined to rank the identified supply chain risks while Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution and Intuititionistic Fuzzy Višekriterijumsko Kompromisno Rangiranjeis are used in assessing the impact of these risks in company metrics. It is concluded that market change risk should be given the highest importance, followed by inaccurate information sharing and bullwhip effect while high crime rates and pollution were given the least priority. The top priority risks were found out to havea negative impact on the quality and expense measure of the company. The finding of this research also shows that equal weights in each supply chain risk or prioritizing the governmental and natural risk will impact the quality and flexibility metric while operational, demand and information technology risk will impact the quality and expense metric of the company. This study also proves that quality metric should be given the utmost priority as it will have an impact the most in business in any scenario. With this research, management will be able to properly plan their risk mitigation and allocate their resources accordingly.

    ABSTRACT …………………………………………………………………………………. i ACKNOWLEDGEMENT ………………………………………………………………....... ii LIST OF ABBREVIATIONS ………………………………………………………………. vi LIST OF FIGURES ………………………………………………………………………… viii LIST OF TABLES ………………………………………………………………………….... ix 1. Introduction ………………………………………………………………………………… 1 1.1 Supply Chain ……………………………………………………………………… 1 1.2 Motivation of this Study …………………………………………………………. 2 1.3 Research Objectives ………………………………………………………………. 3 1.4 Structure of Research ……………………………………………………………... 3 2. Review of Related Literature ………………………………………………………………. 4 2.1 Supply Chain Risk Disruption ……………………………………………………. 4 2.2 Application of MCDM in SCRM ………………………………………………… 6 2.3 Research Gap ……………………………………………………………………... 8 2.4 Supply Chain Risks Criterions and Sub Criterions ……………………………… 10 2.4.1 Operational Risk ………………………………………………………. 10 2.4.2 Natural Risk …………………………………………………………… 10 2.4.3 Social Risk ……………………………………………………………. 11 2.4.4 Demand Risk ………………………………………………………….. 11 2.4.5 Government Risk …………………………………………………….... 11 2.4.6 Information Technology Risk …………………………………………. 12 2.5 Supply Chain Management Performance Metric ……………………………… 12 2.5.1 Competitive Performance Metric ……………………………………. 12 2.5.2 Cost and Expense Metric ……………………………………………. 12 2.5.3 Flexibility Metric ……………………………………………………. 13 2.5.4 Time Metric …………………………………………………………. 13 2.5.5 Quality Metric ………………………………………………………. 13 3. Methodology …………………………………………………………………………….18 3.1 Data Collection ……………………………………………………………….. 18 3.2 Intuitionistic Fuzzy Set ……………………………………………………….. 19 3.2.1 Derivation of IF Set from Experts’ Opinion ………………………. 20 3.3 Intuitionistic Fuzzy Entropy Weighing ………………………………………. 22 3.4 Failure Mode and Effect Analysis ……………………………………………. 23 3.5 Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution …………………………………………………………………………… 24 3.6 Intuitionistic Fuzzy Višekriterijumsko Kompromisno Rangiranje …………… 29 4. Results and Discussion …………………………………………………………………. 32 4.1 Derivation of IFS Parameters …………………………………………………. 32 4.1.1 Weights of Experts ………………………………………………….. 32 4.1.2 Aggregated Result of IFS Parameters ……………………………….. 33 4.2. Ranking of Supply Chain Risks ………………………………………………. 34 4.2.1 Computed Weights of Decision Makers …………………………….. 34 4.2.2 Computed Weights of Sub-Criteria’s Using IF-EWM ………………. 35 4.2.3 Computed Weights of Sub-Criteria’s Using FMEA ………………… 36 4.2.4 Final Ranking and Weighted Aggregated Result of Sub-criteria’s …… 37 4.3 Ranking of Supply Chain Metrics ……………………………………………….. 38 4.3.1 Ranking of Alternatives Using IF-TOPSIS …………………………… 38 4.3.2 Ranking of Alternatives Using IF-VIKOR …………………………… 39 4.3.3 Final Ranking of Alternatives using combined IF-TOPSIS and IF-VIKOR ………………………………………………………………………………. 39 4.4 Discussion ………………………………………………………………………. 40 4.5 Sensitivity Analysis …………………………………………………………….. 42 5. Conclusion ……………………………………………………………………………….. 46 5.1 Discussion of Research Objectives …………………………………………….. 46 5.2 Contribution …………………………………………………………………….. 47 5.3 Limitation and Scope of the Study ……………………………………………… 48 5.4 Future Research …………………………………………………………………. 48 5.5 Importance of This Research ……………………………………………………. 48 REFERENCES ……………………………………………………………………………… 49 APPENDICES ………………………………………………………………………………. 58

    Abdel-Basset, M., & Mohamed, R. (2019). A novel plithogenic TOPSIS- CRITIC model for
    sustainable supply chain risk management. Journal of Cleaner Production, Vol. 247, 20
    February 2020, 119586. doi:10.1016/j.jclepro.2019.119586
    Alkafaas, S., Fattouh, M., Masoud R., & Nada, O. (2020). Intuitionistic fuzzy VIKOR method for facility location selection problem. International Journal of Engineering Research
    & Technology (IJERT), 9(8), 719-724.
    Alshehri, S., Jun, W., Shah, S., & Solangi, Y. (2021). Analysis of core risk factors and potential policy options for sustainable supply chain: an MCDM analysis of Saudi
    Arabia’s manufacturing industry. Environ Sci Pollut Res (2022) 29:25360–25390. doi:
    10.1007/s11356-021-17558-4
    Amin, F., Dong, Q., Grzybowska, K., Ahmed, Z., & Yan, B. (2022). A novel fuzzy-based
    VIKOR–CRITIC Soft Computing method for evaluation of sustainable supply chain
    risk management. Sustainability 2022, 14, 2827. doi: 10.3390/su14052827
    Atanassov, K.T. (1999). Intuitionistic Fuzzy Sets. Springer-Verlag Berlin Heildelberg.
    Atanassov, K.T. (2012) On Intuitionistic Fuzzy Sets Theory. Springer-Verlag Berlin
    Heidelberg.
    Baghalian, A., Rezapour, S., & Zanjirani Farahani, R. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case.
    European Journal of Operational Research, 227(1), 199-215.
    doi:10.1016/j.ejor.2012.12.017
    Bakshi, N., & Kleindorfer, P. R. (2009). Co-opetition and investment for supply-chain
    resilience. Production and Operations Management, 18(6), 583-603.
    Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202. doi:
    10.1080/00207543.2018.1530476
    Bier, T., Lange, A., & Glock, C. H. (2019). Methods for mitigating disruptions in complex supply chain structures: a systematic literature review. International Journal of Production Research, 57(6), 1835-1856. doi:10.1080/00207543.2019.1687954
    Blackhurst, J., Craighead, C. W., Elkins, D., & Handfield, R. B. (2005). An empirically
    derived agenda of critical research issues for managing supply-chain disruptions.
    International Journal of Production Research, 43(19), 4067-4081.
    Bode, C., & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36,215–228. doi:10.1016/j.jom.2014.12.004
    Boran, F.E., Genç, Ş., Kurt, M., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing, 21(2), 105-120. doi:10.1002/hfm.20252.
    Chand, M., Raj, T., Shankar, R., & Agarwal, A. (2017). Select the best supply chain by risk analysis for Indian industries environment using MCDM approaches. Benchmarking:
    An International Journal, Vol. 24 Issue: 5, doi: 10.1108/BIJ-09-2015-0090
    Chatterjee, K., & Kar, S. (2016). Multi-criteria analysis of supply chain risk management
    using interval valued fuzzy TOPSIS. OPSEARCH, 53, 474-499. doi: 10.1007/s12597-
    015-0241-6
    Chen, J., Sohal, A. S., & Prajogo, D. I. (2013). Supply chain operational risk mitigation: a collaborative approach. International Journal of Production Research, 51(7), 2186-2199. doi: 10.1080/00207543.2012.727490
    Christopher, M. (2005). Logistics and supply chain management, creating value-adding
    networks (3rd ed.). Harlow: Financial Times Prentice Hall
    Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The
    severity of supply chain disruptions: Design characteristics and mitigation capabilities.Decision Sciences, 38(1), 131-156.
    Cunha, L., Ceryno, P., & Leiras, A. (2019). Social supply chain risk management: A
    taxonomy, a framework and a research agenda. Journal of Cleaner Production, 220,
    1101-1110. doi:10.1016/j.jclepro.2019.02.183
    Datapine. (n.d). Logistics key performance indicators and metrics. Retrieved March 10 2023,from https://www.datapine.com/kpi-examples-andtemplates/logistics?fbclid=IwAR16jTxjdFpvmFT4WnoZTu6kvrZ8ullNtstduktmrCMS
    kvFXOLmmYcnLeTE
    Davila, T. (2005). Measuring the benefits of product standardization and postponement of configuration in a supply chain. In Harrison, T., Lee, H., & Neale, J. (Eds.), The
    practice of supply chain management (pp.225). 2004 Springer Science&Business
    Media, Inc.
    Dharmarajan, R. (2017). An intuitionistic fuzzy TOPSIS DSS model with weight determining methods. International Journal of Engineering and Computer Science, 6(2), 20354-20361.
    El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the
    disruption impacts on supply chains’ resilience and robustness? Evidence from an
    empirical survey in a COVID-19 outbreak era. International Journal of Production
    Economics, 233, 107972. doi:10.1016/j.ijpe.2020.107972
    Elrod, C., Murray, S., Bande, S. (2013). A review of performance metrics for supply chain management. Engineering Management Journal Vol. 25 Issue: 3.
    doi:10.1080/10429247.2013.11431981
    Fan, Y., & Stevenson, M. (2018). A review of supply chain risk management: Definition,
    theory, and research agenda. International Journal of Physical Distribution & Logistics
    Management, 48(3), 205-230. doi: 10.1108/IJPDLM-01-2017-0043
    Gurtu, A., & Johny, J. (2021). Supply Chain Risk Management: Literature Review. Risks,
    9(1), 16. doi: 10.3390/risks9010016
    Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk –
    Definition, measure and modeling. Omega, 52, 119-132. doi:
    10.1016/j.omega.2014.10.004
    Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk –
    Definition, measure and modeling. Omega, 52, 119-132. doi:10.1016/j.omega.2014.10.004
    Heidari, S. S., Khanbabaei, M., & Sabzehparvar, M. (2018). A model for supply chain risk management in the automotive industry using fuzzy analytic hierarchy process and
    fuzzy TOPSIS. Benchmarking: An International Journal, 25(9), 3831-3857.doi:10.1108/BIJ-11-2016-0167
    Hohenstein, N.-O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the
    phenomenon of supply chain resilience: A systematic review and paths for further
    Investigation. International Journal of Physical Distribution & Logistics Management,
    45(1/2), 90-117. doi:10.1108/IJPDLM-05-2013-0128
    Huang, A. (2017). A framework and metrics for sustainable manufacturing performance
    evaluation at the production line, plant and enterprise levels. Theses and
    Dissertations—Mechanical Engineering. doi:10.13023/ETD.2017.373
    Hwang, G., Han, S., Jun, S. & Park, J. (2014). Operational performance metrics in
    manufacturing process: based on SCOR model and RFID technology. International
    Journal of Innovation, Management and Technology, Vol. 5, No. 1, February 2014.
    doi: 10.7763/IJIMT.2014.V5.485
    Junaid, M., Xue, Y., Syed, M. W., Li, J. Z., & Ziaullah, M. (2019). A neutrosophic AHP and TOPSIS framework for supply chain risk assessment in automotive industry of
    Pakistan. Sustainability, 12(1), 154. doi:10.3390/su12010154
    Kaaffah, S., Ridwan, A. Y., & Novitasari, N. (2020). Designing Vendor Selection System
    Using Intuitionistic Fuzzy TOPSIS and Entropy Weighting Method in Oil and Gas
    Industry. In Proceedings of International Conference on Engineering and Information
    Technology for Sustainable Industry (ICONETSI 2020) (pp. 1-6). Association for
    Computing Machinery. doi: 10.1145/3429789.3429840
    Kaaffah, S., Ridwan, A., & Novitasari, N. (2020). Designing vendor selection system using intuitionistic fuzzy TOPSIS and entropy weighing method in oil and gas industry. International Conference on Engineering and Information Technology for Sustainable
    Industry. doi: 10.1145/3429789.3429842
    Kettering University. (2016, June 7). The impact of natural disasters on global supply chains.
    Kettering University Online. Retrieved March 10, 2023, from https://online.kettering.edu/news/2016/06/07/impact-natural-disasters-global-supplychains
    Khan, S., Khan, M. I., Haleem, A., & Jami, A. R. (2019). Prioritising the risks in Halal food supply chain: an MCDM approach. Journal of Islamic Marketing, ahead-ofprint(ahead-of-print).doi:10.1108/jima-10-2018-0206
    Kharisma, S. A., & Ardi, R. (2020). Supply chain risk assessment of generic medicine in
    Indonesia using DEMATEL-based ANP (DANP). 2020 IEEE International Conference
    on Industrial Engineering and Engineering Management (IEEM).
    doi:10.1109/ieem45057.2020.9309793
    Kiani Mavi, R., Goh, M., & Kiani Mavi, N. (2016). Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management. Procedia - Social and Behavioral Sciences, 235, 216-225. doi: 10.1016/j.sbspro.2016.11.017
    Kumar, S. K., Tiwari, M. K., & Babiceanu, R. F. (2010). Minimisation of supply chain cost with embedded risk using computational intelligence approaches. International Journal of Production Research, 48(13), 3717-3739
    Lee, A., Chen, W., & Chang, C. (2008). A fuzzy AHP and BSC approach for evaluating
    performance of IT department in the manufacturing industry in Taiwan. Expert System
    with Applications, Vol. 34 Issue: 1. doi: 10.1016/j.eswa.2006.08.022
    Liu, S. & Chen, H. (2018). Research on supply chain risk assessment based on FMEA. In Li,X. & Xu, X. (Eds.), Uncertainty and operations research: Proceedings of the fifth
    international forum on decision sciences (pp. 84-85). Springer Nature Singapore Pte
    Ltd. 2018
    Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: theory building through structured experiments and simulation. International Journal of Production Research, 56(13), 4337-4355.
    doi:10.1080/00207543.2017.1421787
    Marasini, D., Quatto, P., & Ripamonti, E. (2015). Intuitionistic fuzzy sets in questionnaire analysis. Springer Science+Business Media Dordrecht 2015 50:767–790. doi:
    10.1007/s11135-015-0175-3
    Meier, M., & Pinto, E. (2020). Covid-19 supply chain disruptions. Federal Reserve Board.
    Retrieved March 10 2023, from https://www.federalreserve.gov/econres/notes/fedsnotes/covid-19-supply-chain-disruptions-20201117.htm
    Morgan, L. (n.d). Operational risk. TechRaget. Retrieved March 10, 2023, from
    https://www.techtarget.com/searchsecurity/definition/operational-risk
    Munir, M., Jajja, M., Chatha, K., & Farooq, S. (2020). Supply chain risk management and
    operational perfromance: the enabling role of supply chain integration. International
    Journal of Production Economics. doi: 10.1016/j.ijpe.2020.107667
    Mzougui, I., Carpitella, S., Certa, A., El Felsoufi, Z., & Izquierdo, J. (2020). Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA.Processes, 8(5), 579. doi: 10.3390/pr8050579
    Mzougui, I., Carpitella, S., Certa, A., Felsoufi, Z., & Izquierdo, J. (2020). Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA.
    Processes 2020, 8, 579. doi:10.3390/pr8050579
    Okuyama, Y., & Santos, J. R. (2014). Disaster impact and input-output analysis. Economic Systems Research, 26(1), 1–12. doi:10.1080/09535314.2013.871505.
    Park, J.H., Cho, H.J., & Kwun, Y.C. (2014). Extension of the VIKOR method to dynamic
    intuitionistic fuzzy multiple attribute decision making. Computers and Mathematics
    with Applications, 65 (2013) 731-744. doi: 10.1016/j.camwa.2012.12.008
    Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply
    chains for a high-demand item during COVID-19. International Journal of Production
    Research, Vol. 51 No. 2, 2021 (pp. 104-125). doi: 10.1108/IJPDLM-04-2020-0127
    Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience:
    Development and implementation of an assessment tool. Journal of Business Logistics,
    34(1), 46-76. doi: 10.1111/jbl.12009
    Pinto, L. (2020). Green supply chain practices and company performance in Portuguese
    manufacturing sector. Business Strategy and the Environment. doi:10.1002/bse.2471
    Prajogo, D. I., Oke, A., & Olhager, J. (2016). Supply chain processes: linking supply logistics integration, supply performance, lean processes, and competitive performance.
    International Journal of Operations & Production Management, 36(2). doi:
    10.1108/IJOPM-03-2014-0129
    Rathore, R., Thakkar, J., & Jha, J. (2017). A quantitative risk assessment methodology and evaluation of food supply chain. The International Journal of Logistics Management
    Vol. 28 No. 4, 1272-1293. doi:10.1108/IJLM-08-2016-0198
    Reddy, V. R., Singh, S. K., & Anbumozhi, V. (2016). Food supply chain disruption due to
    natural disasters: Entities, risks, and strategies for resilience. ERIA Discussion Paper Series. Research Institute of Economy, Trade and Industry.
    Rostamzadeh, R., Ghorabaee, K., Govindan, K., Esmaeili, A., & Nobar, H. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSISCRITIC approach. Journal of Cleaner Production, 175, 651–669.
    doi:10.1016/j.jclepro.2017.12.07
    Sadiq, S., Chatha, K., & Farooq, S. (2018). Impact of supply chain risk on agility performance:mediating role of supply chain integration. International Journal of Production Economics. doi:10.1016/j.ijpe.2018.08.032
    Segal, T. (2023, January 16). Operational risk. Investopedia. Retrieved March 10, 2023, from https://www.investopedia.com/terms/o/operational_risk.asp
    Singh, S., Kumar, R., Panchal, R., & Tiwari, M. K. (2020). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 1-16. doi:10.1080/00207543.2020.1792000
    Sudarmin, C., & Ardi, R. (2020). DEMATEL-based Analytic Network Process (ANP)
    approach to assess the vaccine supply chain risk in Indonesia. 2020 IEEE International
    Conference on Industrial Engineering and Engineering Management (IEEM).
    doi:10.1109/ieem45057.2020.930991
    Szmidt, E. (2014). Studies in fuzziness and soft computing: Distances and similarities in intuitionistic fuzzy sets. Springer International Publishing Switzerland 2014. doi:
    10.1007/978-3-319-01640-5
    Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 505-518. doi:10.1016/S0165-0114(98)00244-9
    Szmidt, E., & Kacprzyk, J. (2008). A new approach to ranking alternatives expressed via Intuitionistic fuzzy sets. In D. Ruan, J. Kacprzyk, & G. Chen (Eds.), Computational
    Intelligence in Decision and Control (pp. 265-270). World Scientific.
    Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain
    resilience: Definition, review and theoretical foundations for further study.
    International Journal of Production Research, 53(18), 5592–5623.
    doi:10.1080/00207543.2015.1037934
    Venkatesan, S., & Kumanan, S. (2012). Supply chain risk prioritisation using a hybrid AHP and PROMETHEE approach. Int. J. Services and Operations Management, Vol. 13,
    No. 1, 2012.
    Verma, V., Gunasekaran, A., & Ambilkar, A. (2021). COVID-19 and supply chain risk
    mitigation: a case study from India. The International Journal of Logistics
    Management. doi: 10.1108/IJLM-04-2021-0197
    Vlachos, I. K., & Sergiadis, G. D. (2007). Intuitionistic fuzzy information – Applications to pattern recognition. Pattern Recognition Letters, 28, 197-206. doi:
    10.1016/j.patrec.2006.07.004
    Wang, L., & Rani, P. (2022). Sustainable supply chains under risk in the manufacturing firms:an extended double normalization-based multiple aggregation approach under an
    intuitionistic fuzzy environment. Journal of Enterprise Information Management,
    35(4/5), 1067-1099. doi: 10.1108/JEIM-05-2021-022.
    Yazdani, M., Gonzalez, E., & Chatterjee, P. (2019). A multi-criteria decision-making
    framework for agriculture supply chain risk management under a circular economy
    context. Management Decision, MD-10-2018-1088. doi:10.1108/MD-10-2018-1088
    Yilmaz, I. (2022). Evaluating industry 4.0 barrieris by intuitionistic fuzzy VIKOR method. In Erdebilli, B., Weber, GW. (Eds.), Multiple criteria decision making with fuzzy sets(pp.167-178). Multiple citeria decision making. Springer, Cham. doi: 10.1007/978-3-
    030-98872-2_11
    Yu, X., & Xu, Z. (2013). Prioritized intuitionistic fuzzy aggregation operators. Information
    Fusion, 14(1), 108–116. doi:10.1016/j.inffus.2012.01.011
    Zhang, N., & Wei, G. (2013). Extension of VIKOR method for decision making problem
    based on hesitant fuzzy set. Applied Mathematical Modelling, 37(7), 4938–4947.
    doi:10.1016/j.apm.2012.10.002
    Zhang, X., Sun, B., Chen, X., Chu, X., & Yang, J. (2020). An approach to evaluating
    sustainable supply chain risk management based on BWM and linguistic value soft set
    theory. Journal of Intelligent & Fuzzy Systems, 1–14. doi:10.3233/jifs-200372
    Zhao, J., You, X.-Y., Liu, H.-C., & Wu, S.-M. (2017). An Extended VIKOR Method Using
    Intuitionistic Fuzzy Sets and Combination Weights for Supplier Selection. Symmetry,
    9(9), 169. doi:10.3390/sym9090169
    Zhao, L., Huo, B., Sun, L., & Zhao, X. (2013). The impact of supply chain risk on supply chain integration and company performance: a global investigation. Supply Chain
    Management: An International Journal 18/2 (2013) 115–131. doi:
    10.1108/13598541311318773

    無法下載圖示 全文公開日期 2028/06/25 (校內網路)
    全文公開日期 2028/06/25 (校外網路)
    全文公開日期 2028/06/25 (國家圖書館:臺灣博碩士論文系統)
    QR CODE