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Multiple attribute decision-making methods for the dynamic operator allocation problem

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  • Yang, Taho
  • Chen, Mu-Chen
  • Hung, Chih-Ching

Abstract

This study explores two multiple attribute decision-making (MADM) methods to solve a dynamic operator allocation problem. Both methods use an analytic hierarchy process (AHP) to determine attribute weights a priori. The first method uses a technique for order preference by similarity to ideal solution (TOPSIS). The second method incorporates a fuzzy-based logic that uses linguistic variable representation, fuzzy operation, and fuzzy defuzzification. The TOPSIS uses deterministic performance ratings and attribute weights, whilst the fuzzy-based is a linguistic method. An applied case study drawn from existing literature is used to demonstrate and test findings. The proposed methods systematically evaluate alternative scenarios, with the result indicating promise for solving an operator allocation decision problem.

Suggested Citation

  • Yang, Taho & Chen, Mu-Chen & Hung, Chih-Ching, 2007. "Multiple attribute decision-making methods for the dynamic operator allocation problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(5), pages 285-299.
    • Handle: RePEc:eee:matcom:v:73:y:2007:i:5:p:285-299
    DOI: 10.1016/j.matcom.2006.04.002
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    References listed on IDEAS

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    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Cheng, Ching-Hsue & Lin, Yin, 2002. "Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 174-186, October.
    3. Finan, J. S. & Hurley, W. J., 1999. "Transitive calibration of the AHP verbal scale," European Journal of Operational Research, Elsevier, vol. 112(2), pages 367-372, January.
    4. Belton, Valerie & Gear, Tony, 1983. "On a short-coming of Saaty's method of analytic hierarchies," Omega, Elsevier, vol. 11(3), pages 228-230.
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    Cited by:

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    2. Ehsan Pourjavad & Rene V. Mayorga, 2019. "A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1085-1097, March.
    3. Ali BAYRAKDAROGLU & Nese YALCIN, 2012. "Strategic Financial Performance Evaluation of the Turkish Companies Traded on ISE," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 12(4), pages 529-539.
    4. Kuo, Yiyo & Yang, Taho & Cho, Chiwoon & Tseng, Yao-Ching, 2008. "Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(1), pages 40-56.
    5. G. La Scalia & F.P. Marra & J. Rühl & R. Sciortino & T. Caruso, 2016. "A fuzzy multi-criteria decision-making methodology to optimise olive agro-engineering processes based on geo-spatial technologies," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 15(1), pages 1-15.

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