Free Access
RAIRO-Oper. Res.
Volume 53, Number 5, November-December 2019
Page(s) 1709 - 1720
Published online 09 October 2019
  • R. Aggarwal, S.P. Singh and P.K. Kapur, Integrated dynamic vendor selection and order allocation problem for the time dependent and stochastic data. Benchmarking An Int. J. 25 (2018) 777–796. [CrossRef] [Google Scholar]
  • A. Azadeh and S.M. Alem, A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis. Expert Syst. Appl. 37 (2010) 7438–7448. [Google Scholar]
  • M. Biehl, A. Ghosh and B. Hammer, Dynamics and generalization ability of LVQ algorithms. J. Mach. Learn. Res. 8 (2007) 323–360. [Google Scholar]
  • G. Büyüközkan and G. Çifçi, A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Comput. Ind. 62 (2011) 164–174. [Google Scholar]
  • F. Çebi and D. Bayraktar, An integrated approach for supplier selection. Logist. Inf. Manag. 16 (2003) 395–400. [CrossRef] [Google Scholar]
  • J. Chai, J.N.K. Liu and E.W.T. Ngai, Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Syst. Appl. 40 (2013) 3872–3885. [Google Scholar]
  • V. Chaudhary, R. Kulshrestha and S. Routroy, State-of-the-art literature review on inventory models for perishable products. J. Adv. Manag. Res. 15 (2018) 306–346. [CrossRef] [Google Scholar]
  • Y.J. Chen, Structured methodology for supplier selection and evaluation in a supply chain. Inf. Sci. (NY) 181 (2011) 1651–1670. [Google Scholar]
  • L. Cheng, E. Subrahmanian and A.W. Westerberg, Design and planning under uncertainty: issues on problem formulation and solution. Comput. Chem. Eng. 27 (2003) 781–801. [Google Scholar]
  • S. De Kumar and S.S. Sana, Two-layer supply chain model for Cauchy type Stochastic demand under fuzzy environment. Int. J. Intell. Comput. Cybern. 11 (2018) 285–308. [CrossRef] [Google Scholar]
  • L. Duan and J.A. Ventura, A dynamic supplier selection and inventory management model in a serial supply chain with a novel supplier price break scheme and flexible time periods. Eur. J. Oper. Res. 272 (2019) 979–998. [Google Scholar]
  • M. Dursun and E.E. Karsak, A QFD-based fuzzy MCDM approach for supplier selection. Appl. Math. Model. 37 (2013) 5864–5875. [Google Scholar]
  • C. Gencer and D. Gürpinar, Analytic network process in supplier selection: A case study in an electronic firm. Appl. Math. Model. 31 (2007) 2475–2486. [Google Scholar]
  • S.H. Ghodsypour and C. O’brien, The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. Int. J. Prod. Econ. 73 (2001) 15–27. [Google Scholar]
  • W. Ho, X. Xu and P.K. Dey, Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. Eur. J. Oper. Res. 202 (2010) 16–24. [Google Scholar]
  • S. Karsoliya, Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int. J. Eng. Trends Technol. 3 (2012) 714–717. [Google Scholar]
  • T. Kohonen, Self-Organizing Maps. Springer, Berlin (1995). [CrossRef] [Google Scholar]
  • R. Kouwenberg, Scenario generation and stochastic programming models for asset liability management. Eur. J. Oper. Res. 134 (2001) 279–292. [Google Scholar]
  • R.J. Kuo, Y.C. Wang and F.C. Tien, Integration of artificial neural network and MADA methods for green supplier selection. J. Clean. Prod. 18 (2010) 1161–1170. [Google Scholar]
  • M. Kurimo, Using self-organizing maps and learning vector quantization for mixture density hidden Markov models. CiteSeer (1997). [Google Scholar]
  • Z. Liao and J. Rittscher, A multi-objective supplier selection model under stochastic demand conditions. Int. J. Prod. Econ. 105 (2007) 150–159. [Google Scholar]
  • S. Ljubojević, D. Pamučar, D. Jovanović and V. Vešović, Outsourcing transport service: a fuzzy multi-criteria methodology for provider selection based on comparison of the real and ideal parameters of providers. Oper. Res. Int. J. 19 (2019) 399–433. [CrossRef] [Google Scholar]
  • A. Mendoza and J.A. Ventura, Modeling actual transportation costs in supplier selection and order quantity allocation decisions. Oper. Res. 13 (2013) 5–25. [Google Scholar]
  • N.M. Modak, Exploring Omni-channel supply chain under price and delivery time sensitive stochastic demand. Sup. Chain. For. Int. J. 18 (2017) 218–230. [CrossRef] [Google Scholar]
  • N.M. Modak and P. Kelle, Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand. Eur. J. Oper. Res. 272 (2019) 147–161. [Google Scholar]
  • R.L. Nydick and R.P. Hill, Using the analytic hierarchy process to structure the supplier selection procedure. J. Supply Chain Manag. 28 (1992) 31. [Google Scholar]
  • G. Panchal, A. Ganatra, Y.P. Kosta and D. Panchal, Behaviour analysis of multilayer perceptronswith multiple hidden neurons and hidden layers. Int. J. Comput. Theory Eng. 3 (2011) 332–337. [CrossRef] [Google Scholar]
  • T.C. Poon, K.L. Choy, C.K. Cheng, S.I. Lao and H.Y. Lam, Effective selection and allocation of material handling equipment for stochastic production material demand problems using genetic algorithm. Expert Syst. Appl. 38 (2011) 12497–12505. [Google Scholar]
  • H. Pujara and K. Prasad, Image segmentation using learning vector quantization of artificial neural network. Int. J. Adv. Res. Artif. Intell. 2 (2013) 51–55. [CrossRef] [Google Scholar]
  • R. Ramanathan, Supplier selection problem: integrating DEA with the approaches of total cost of ownership and AHP. Supply Chain Manag. Int. J. 12 (2007) 258–261. [CrossRef] [Google Scholar]
  • R. Farzipoor Saen, Suppliers selection in the presence of both cardinal and ordinal data. Eur. J. Oper. Res. 183 (2007) 741–747. [Google Scholar]
  • N.V. Sahinidis, Optimization under uncertainty: state-of-the-art and opportunities. Comput. Chem. Eng. 28 (2004) 971–983. [Google Scholar]
  • M. Sakawa, I. Nishizaki and Y. Uemura, A decentralized two-level transportation problem in a housing material manufacturer: Interactive fuzzy programming approach. Eur. J. Oper. Res. 141 (2002) 167–185. [Google Scholar]
  • H. Shin, D.A. Collier and D. Wilson, Supply management orientation and supplier/buyer performance. J. Oper. Manag. 18 (2000) 317–333. [CrossRef] [Google Scholar]
  • H. Stadtler, Supply chain management and advanced planning – basics, overview and challenges. Eur. J. Oper. Res. 163 (2005) 575–588. [Google Scholar]
  • D. Stathakis, How many hidden layers and nodes?. Int. J. Remote Sens. 30 (2009) 2133–2147. [Google Scholar]
  • A.A. Taleizadeh, S.T. Akhavan Niaki and F. Barzinpour, Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: a harmony search algorithm. Appl. Math. Comput. 217 (2011) 9234–9253. [Google Scholar]
  • S.-C. Ting and D.I. Cho, An integrated approach for supplier selection and purchasing decisions. Supply Chain Manag. Int. J. 13 (2008) 116–127. [CrossRef] [Google Scholar]
  • S. Türk, E. Özcan and R. John, Multi-objective optimisation in inventory planning with supplier selection. Expert Syst. Appl. 78 (2017) 51–63. [Google Scholar]
  • T. Villmann, A. Bohnsack and M. Kaden, Can learning vector quantization be an alternative to SVM and deep learning?-recent trends and advanced variants of learning vector quantization for classification learning. J. Artif. Intell. Soft Comput. Res. 7 (2017) 65–81. [CrossRef] [Google Scholar]
  • A. Witoelar, M. Biehl and B. Hammer, Learning vector Quantization: generalization ability and dynamics of competing prototypes, In Dagstuhl Seminar Proceedings, Schloss Dagstuhl-Leibniz-Zentrum fur Informatik (2007). [Google Scholar]
  • J. Wu, LVQ neural network based classification decision approach to mechanism type in conceptual design. Artif. Intell. Comput. Intell. 7004 (2011) 378–384. [CrossRef] [Google Scholar]
  • P.C. Yang, H.M. Wee, S. Pai and Y.F. Tseng, Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm. Expert Syst. Appl. 38 (2011) 14773–14777. [Google Scholar]
  • Z. Qu, H. Raff and N. Schmitt, Incentives through Inventory Control in Supply Chains. Int. J. Ind. Organ. 59 (2018) 486–513. [Google Scholar]
  • J.L. Zhang and M.Y. Zhang, Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand. Int. J. Prod. Econ. 129 (2011) 1–7. [Google Scholar]
  • A. Zouggari and L. Benyoucef, Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem. Eng. Appl. Artif. Intell. 25 (2012) 507–519. [Google Scholar]

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