Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 30, No. 6 (June 2017) 867-875    Article in Press

downloaded Downloaded: 74   viewed Viewed: 1642

  SUSTAINABLE SUPPLIER SELECTION BY A NEW HYBRID SUPPORT VECTOR-MODEL BASED ON THE CUCKOO OPTIMIZATION ALGORITHM
 
N. Foroozesh and R. Tavakkoli-Moghaddam
 
( Received: January 29, 2017 – Accepted in Revised Form: April 21, 2017 )
 
 

Abstract    For assessing and selecting sustainable suppliers, this study considers a triple-bottom-line approach, including profit, people and planet, and regards business operations, environmental effects along with social responsibilities of the suppliers. Diverse metrics are acquainted with measure execution in these three issues. This study builds up a new hybrid intelligent model, namely COA-LS-SVM, for taking performance variations of the sustainable suppliers quantified by the performance index. The presented artificial intelligent (AI) model is introduced in light of a new combination of least squares-support vector machine (LS-SVM) and cuckoo optimization algorithm (COA). The LS-SVM is used in regards to the mapping capacity amongst performance index and its causative input criteria. The COA is presented to advance LS-SVM tuning parameters. In this exploration, an illustrative database comprising of 80 historical cases is gathered to set up the presented intelligence system. In the light of experimental results, the presented COA-LS-SVM can effectively illustrate performance index’s variances since it has accomplished relatively low statistical metrics. Therefore, the proposed hybrid AI framework can be a promising approach to help the supply chain decision-makers in sustainable supply chain management (SSCM).

 

Keywords    Computational intelligence, Sustainable supplier selection, Least square-support vector machine (LS-SVM), Cuckoo optimization algorithm

 

چکیده    به منظور ارزیابی و انتخاب تامین­کنندگان پایدار، این تحقیق یک رویکرد سه قسمتی شامل سود، مردم و سیاره و توجه به عملیات کسب و کار، اثرات زیست محیطی همراه با مسئولیت­های اجتماعی از تامین کنندگان را در نظر می گیرد. متریک­های متنوع با اجرای اندازه گیری، ما را از این سه مسئله مطلع می­سازد. این مقاله یک مدل هوشمند ترکیبی جدید، به نام COA-LS-SVM، برای تعیین کمی تغییرات عملکرد تامین­کنندگان پایدار با استفاده از شاخص عملکرد ایجاد می­کند. مدل پیشنهاد شده هوشمند مصنوعی (AI) ترکیب جدیدی از ماشین بردار پشتیبان با حداقل مربعات (LS-SVM) و الگوریتم بهینه­سازی فاخته (COA) معرفی می­کند. LS-SVM برای بیان ظرفیت نگاشت در میان شاخص­های عملکرد و معیارهای ورودی مسبب آن استفاده شده است. COA به منظور پیشبرد میزان­سازی پارامتر­های LS-SVM پیشنهاد شده است. در این جستجو، یک پایگاه داده متشکل از 80 داده تاریخی برای راه اندازی سیستم هوشمند ارائه شده، جمع آوری شده است. در پرتو نتایج تجربی، از آن-جا که متریک­های آماری نسبتا کم انجام می­گیرد، COA-LS-SVM می­تواند به طور موثری شاخص عملکرد واریانس را نشان دهد. بنابراین، چارچوب AI ارائه شده می­تواند یک روش امیدبخش برای کمک به تصمیم گیرندگان زنجیره تامین، در مدیریت زنجیره تامین پایدار باشد.

References   

1.      Khodakarami, M., Shabani, A., Saen, R.F. and Azadi, M., "Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management", Measurement,  Vol. 70, (2015), 62-74.

2.      Ansari, Z.N. and Kant, R., "A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management", Journal of Cleaner Production,  Vol. 142, (2017), 2524-2543.

3.      Keskin, G.A., Ilhan, S. and Ozkan, C., "The fuzzy art algorithm: A categorization method for supplier evaluation and selection", Expert Systems with Applications,  Vol. 37, No. 2, (2010), 1235-1240.

4.      Önüt, S., Efendigil, T. and Kara, S.S., "A combined fuzzy mcdm approach for selecting shopping center site: An example from istanbul, turkey", Expert Systems with Applications,  Vol. 37, No. 3, (2010), 1973-1980.

5.      Beck, P. and Hofmann, E., "Multiple criteria decision making in supply chain management–currently available methods and possibilities for future research", Die Unternehmung,  Vol. 66, No. 2, (2012), 180-213.

6.      Selmi, M., Kormi, T. and Ali, N.B.H., "Comparing multi-criteria decision aid methods through a ranking stability index", in Modeling, Simulation and Applied Optimization (ICMSAO), 5th International Conference on, IEEE., (2013), 1-5.

7.      Azadnia, A.H., Ghadimi, P., Saman, M.Z.M., Wong, K.Y. and Heavey, C., "An integrated approach for sustainable supplier selection using fuzzy logic and fuzzy AHP", in Applied Mechanics and Materials, Trans Tech Publ. Vol. 315, (2013), 206-210.

8.      Ng, S.T. and Skitmore, R.M., "Cp-dss: Decision support system for contractor prequalification", Civil Engineering Systems,  Vol. 12, No. 2, (1995), 133-159.

9.      Cook, R.L., "Case‐based reasoning systems in purchasing: Applications and development", Journal of Supply Chain Management,  Vol. 33, No. 4, (1997), 32-39.

10.    Khoo, L.-P., Tor, S.B. and Lee, S.S., "The potential of intelligent software agents in the world wide web in automating part procurement", Journal of Supply Chain Management,  Vol. 34, No. 1, (1998), 46-53.

11.    Amindoust, A., Ahmed, S., Saghafinia, A. and Bahreininejad, A., "Sustainable supplier selection: A ranking model based on fuzzy inference system", Applied Soft Computing,  Vol. 12, No. 6, (2012), 1668-1677.

12.    Kuo, R., Hong, S. and Huang, Y., "Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection", Applied Mathematical Modelling,  Vol. 34, No. 12, (2010), 3976-3990.

13.    Omurca, S.I., "An intelligent supplier evaluation, selection and development system", Applied Soft Computing,  Vol. 13, No. 1, (2013), 690-697.

14.    Jauhar, S.K., Pant, M. and Abraham, A., A novel approach for sustainable supplier selection using differential evolution: A case on pulp and paper industry, in Intelligent data analysis and its applications, volume ii. (2014), Springer.105-117.

15.    Jauhar, S. and Pant, M., Sustainable supplier selection: A new differential evolution strategy with automotive industry application, in Recent developments and new direction in soft-computing foundations and applications. (2016), Springer.353-371.

16.    Bhardwaj, B.R., "Role of green policy on sustainable supply chain management: A model for implementing corporate social responsibility (CSR)", Benchmarking: An International Journal,  Vol. 23, No. 2, (2016), 456-468.

17.    Kara, M.E., Yurtsever, O. and Fırat, S.U.O., "Sustainable supplier evaluation and selection criteria", Social and Economic Perspectives on Sustainability,  (2016), 159-166.

18.    Ahmadi, H.B., Petrudi, S.H.H. and Wang, X., "Integrating sustainability into supplier selection with analytical hierarchy process and improved grey relational analysis: A case of telecom industry", The International Journal of Advanced Manufacturing Technology,  (2016), 1-15.

19.    Agan, Y., Kuzey, C., Acar, M.F. and Acıkgoz, A., "The relationships between corporate social responsibility, environmental supplier development, and firm performance", Journal of Cleaner Production,  Vol. 112, (2016), 1872-1881.

20.    Girubha, J., Vinodh, S. and KEK, V., "Application of interpretative structural modelling integrated multi criteria decision making methods for sustainable supplier selection", Journal of Modelling in Management,  Vol. 11, No. 2, (2016), 358-388.

21.    Luthra, S., Govindan, K., Kannan, D., Mangla, S.K. and Garg, C.P., "An integrated framework for sustainable supplier selection and evaluation in supply chains", Journal of Cleaner Production,  Vol. 140, (2017), 1686-1698.

22.    Ghadimi, P., Dargi, A. and Heavey, C., "Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry", Computers & Industrial Engineering,  (2017).

23.    Suykens, J.A., Van Gestel, T. and De Brabanter, J., "Least squares support vector machines, World Scientific,  (2002).

24.    Suykens, J.A. and Vandewalle, J., "Least squares support vector machine classifiers", Neural Processing Letters,  Vol. 9, No. 3, (1999), 293-300.

25.    Yu, L., Chen, H., Wang, S. and Lai, K.K., "Evolving least squares support vector machines for stock market trend mining", IEEE Transactions on Evolutionary Computation,  Vol. 13, No. 1, (2009), 87-102.

26.    Wang, H. and Hu, D., "Comparison of svm and ls-svm for regression", in Neural Networks and Brain,. ICNN&B'05. International Conference on, IEEE. Vol. 1, (2005), 279-283.

27.    Cheng, M.-Y., Hoang, N.-D. and Wu, Y.-W., "Hybrid intelligence approach based on ls-svm and differential evolution for construction cost index estimation: A taiwan case study", Automation in Construction,  Vol. 35, (2013), 306-313.

28.    Vahdani, B., Mousavi, S.M., Mousakhani, M., Sharifi, M. and Hashemi, H., "A neural network model based on support vector machine for conceptual cost estimation in construction projects", Journal of Optimization in Industrial Engineering,  Vol. 5, No. 10, (2012), 11-18.

29.    Mousavi, S.M., Tavakkoli-Moghaddam, R., Vahdani, B., Hashemi, H. and Sanjari, M., "A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects", Robotics and Computer-Integrated Manufacturing,  Vol. 29, No. 1, (2013), 157-168.

30.    Mousavi, S.M., Vahdani, B. and Abdollahzade, M., "An intelligent model for cost prediction in new product development projects", Journal of Intelligent & Fuzzy Systems,  Vol. 29, No. 5, (2015), 2047-2057.

31.    Wang, H.K., Ma, J.S., Fang, L.Q., Yang, Y.F. and Liu, H.P., "Application of the least squares support vector machine based on quantum particle swarm optimization for data fitting of small samples", in Applied Mechanics and Materials, Trans Tech Publ. Vol. 472, (2014), 485-489.

32.    Ahmad, A., Hassan, M., Abdullah, M., Rahman, H., Hussin, F., Abdullah, H. and Saidur, R., "A review on applications of ann and svm for building electrical energy consumption forecasting", Renewable and Sustainable Energy Reviews,  Vol. 33, (2014), 102-109.

33.    Lu, X., Zou, W. and Huang, M., "A novel spatiotemporal ls-svm method for complex distributed parameter systems with applications to curing thermal process", IEEE Transactions on Industrial Informatics,  Vol. 12, No. 3, (2016), 1156-1165.

34.    Vahdani, B., Mousavi, S.M., Tavakkoli-Moghaddam, R. and Hashemi, H., "A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study", International Journal of Computational Intelligence Systems,  Vol. 10, No. 1, (2017), 293-311.

35.    Kisi, O. and Parmar, K.S., "Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution", Journal of Hydrology,  Vol. 534, (2016), 104-112.





International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir