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  • رویکرد تصمیم‌گیری چند معیاره فازی ترکیبی برای مدیریت اقلام موجودی (موردمطالعه: شرکت مپنا)

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آدرس مقاله


کد مقاله : 536809 بازدید : 127 صفحه: 87 - 104

نوع مقاله: پژوهشی

رویکرد تصمیم‌گیری چند معیاره فازی ترکیبی برای مدیریت اقلام موجودی (موردمطالعه: شرکت مپنا)

محورهای موضوعی : مدیریت صنعتی

Ali Mohaghar 1 ( Professor of Dept. of Industrial Management, University of Tehran, Tehran, Iran )
Alireza Arab 2 ( Ph.D. student of Operations Research, Faculty of management, University of Tehran, Tehran, Iran )
Seyyed Jalaladdin Hosseini Dehshiri 3 ( Ph.D. student of Operation and Production Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran. )

تاریخ دریافت : 1395/07/15 تاریخ پذیرش : 1396/07/01 تاریخ انتشار : 1396/10/03

کلید واژه: تصمیم گیری چند معیاره, Inventory management, دیمتل فازی, Multiple Criteria Decision Making, ویکور فازی, مدیریت موجودی, شرکت مپنا, FDEMATEL, FVIKOR, MAPNA Company,

چکیده مقاله :

مدیریت موجودی یکی از مهم ترین قسمت های فرآیند برنامه ریزی تولید برای کسب‌وکارها می باشد که دربرگیرنده تصمیماتی در مورد تعیین زمان و تعداد دفعات خرید یا ساخت بوده که می توان با استفاده از رتبه بندی اقلام موجودی انجام داد. هدف پژوهش حاضر ارائه یک مدل تصمیم گیری چند معیاره فازی ترکیبی به‌منظور مدیریت اقلام موجودی با استفاده از روش های دیمتل فازی و ویکور فازی می باشد. در این پژوهش از روش دیمتل فازی برای تعیین اوزان معیارهای تصمیم گیری و هم چنین از روش ویکور فازی برای ارزیابی و رتبه بندی اقلام موجودی بهره گیری شده است. به‌کارگیری هم‌زمان این روش ها نوآوری این پژوهش در میان ادبیات مرتبط با این حوزه می باشد. برای نشان دادن کارایی مدل پیشنهادی از یک مطالعه موردی در شرکت مهندسی و ساخت برق و کنترل مپنا (مکو) استفاده گردید. نتایج نشان دهنده اثربخشی چارچوب پیشنهادی برای مدیریت اقلام موجودی بوده است. در انتها نتایج و پیشنهاد‌هایی در راستای مسئله تحقیق ارائه گردیده است.

چکیده انگلیسی:

Inventory management is an important part of production planning process for enterprises which involve decision-making process. They can determine when and how many things to buy or decisions can be made by ranking the inventory items. The aim of this paper is providing a model based on fuzzy hybrid MCDM methods by using FDEMATEL and FVIKOR in order to Inventory Management. In this study, a fuzzy DEMATEL approach was proposed to determine the weights of the decision criteria and Fuzzy VIKOR method to evaluate and rank the inventory. Concurrently applying fuzzy DEMATEL and FVIKOR to a multi-criteria inventory classification problem is the novelty of this study in the related literature. A real case study at MAPNA Electric & Control, Engineering & Manufacturing Company used to illustrate the efficiency of the proposed approach. Results showed that the proposed framework for inventory management is effective. Finally, some conclusions and suggestions are provided at the end of this study.

منابع و مأخذ:


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_||_

  1. BaykasoğLu, A., KaplanoğLu, V., DurmuşOğLu, Z. D., & ŞAhin, C. (2013). Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications, 40(3), 899-907.
    2.
  2. Björnfot, A., & Torjussen, L. (2012). Extent and Effect of Horizontal Supply Chain Collaboration among Construction SME. Journal of Engineering, Project, and Production Management, 2(1), 47.
    3.
  3. Braglia, M., Grassi, A., & Montanari, R. (2004). Multi-attribute classification method for spare parts inventory management. Journal of Quality in Maintenance Engineering, 10(1), 55-65.
    4.
  4. Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378.
    5.
  5. Çebi, F., Kahraman, C., & Bolat, B. (2010, July). A multiattribute ABC classification model using fuzzy AHP. In Computers and Industrial Engineering (CIE), 2010 40th International Conference on (pp. 1-6). IEEE.
    6.
  6. Chaghooshi, A., Arab, A., & Dehshiri, S. (2016). A fuzzy hybrid approach for project manager selection. Decision Science Letters, 5(3), 447-460.
    7.
  7. Chase, R.B., Jacobs, F. R., Aquilano, N.J., and Agarwal, N.K. (2006). Operations Management for CompetitiveAdvantage. 11th Edition, McGraw Hill, New York, USA.
    8.
  8. Chen, Y., & Qu, L. (2006). Evaluating the selection of logistics centre location using fuzzy MCDM model based on entropy weight. In Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on (Vol. 2, pp. 7128-7132). IEEE.
    9.
  9. Chen, Y., Li, K. W., Kilgour, D. M., & Hipel, K. W. (2008). A case-based distance model for multiple criteria ABC analysis. Computers & Operations Research, 35(3), 776-796.
    10.
  10. Chu, C. W., Liang, G. S., & Liao, C. T. (2008). Controlling inventory by combining ABC analysis and fuzzy classification. Computers & Industrial Engineering, 55(4), 841-851.
    11.
  11. Cohen, M. A., & Ernst, R. (1988). Multi-Item Classification and Generic Inventory Stock Contr. Production and Inventory Management Journal, 29(3), 6.
    12.
  12. Dalalah, D., Hayajneh, M., & Batieha, F. (2011). A fuzzy multi-criteria decision making model for supplier selection. Expert systems with applications, 38(7), 8384-8391.
    13.
  13. Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International journal of approximate reasoning, 21(3), 215-231.
    14.
  14. Flores, B. E., & Clay Whybark, D. (1986). Multiple criteria ABC analysis. International Journal of Operations & Production Management, 6(3), 38-46.
    15.
  15. Flores, B. E., & Whybark, D. C. (1987). Implementing multiple criteria ABC analysis. Journal of Operations Management, 7(1-2), 79-85.
    16.
  16. Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer modelling, 16(12), 71-82.
    17.
  17. Gogus, O., & Boucher, T. O. (1998). Strong transitivity, rationality and weak monotonicity in fuzzy pairwise comparisons. Fuzzy Sets and Systems, 94(1), 133-144.
    18.
  18. Guvenir, H. A., & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm. European journal of operational research, 105(1), 29-37.
    19.
  19. Hadi-Vencheh, A. (2010). An improvement to multiple criteria ABC inventory classification. European Journal of Operational Research, 201(3), 962-965.
    20.
  20. Hadi-Vencheh, A., & Mohamadghasemi, A. (2011). A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification. Expert Systems with Applications, 38(4), 3346-3352.
    21.
  21. Huang, J. J., Tzeng, G. H., & Liu, H. H. (2009). A revised VIKOR model for multiple criteria decision making-The perspective of regret theory. Cutting-Edge Research Topics on Multiple Criteria Decision Making, 761-768.
    22.
  22. Jamshidi, H., & Jain, A. (2008). Multi-criteria ABC inventory classification: With exponential smoothing weights. Journal of Global Business Issues, 2(1), 61.
    23.
  23. Lei, Q., Chen, J., & Zhou, Q. (2005). Multiple criteria inventory classification based on principal components analysis and neural network. Advances in Neural Networks–ISNN 2005, 981-981.
    24.
  24. Lin, C. L., & Wu, W. W. (2004). A fuzzy extension of the DEMATEL method for group decision making.
    25.
  25. Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34.
    26.
  26. Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74.
    27.
  27. Momani, M. (2007). New Topics in Operations Research. Tehran: University of Tehran publication, 2nd Ed. [In Persian].
    28.
  28. Nahmias, S. (2004). Production and Operations Analysis. 5th Edition, Irwin/McGraw Hill, Burr Ridge, IL, USA, 213-215.
    29.
  29. Nakhaei Kamal-abadi A., Bagheri M.(2008). Presentation of an outsourcing decision making model of production activities by using ANP and DEMATEI techniques in fuzzy environment. Industry Manegment Journal of the Humanities College of Islamic Azad University (Sanandaj Branch), Third year, No. 5. [In Persian].
    30.
  30. Ng, W. L. (2007). A simple classifier for multiple criteria ABC analysis. European Journal of Operational Research, 177(1), 344-353.
    31.
  31. Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications, 38(10), 12983-12990.
    32.
  32. Park, J., Bae, H., & Bae, J. (2014). Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification. Computers & Industrial Engineering, 76, 40-48.
    33.
  33. Partovi, F. Y., & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers & Industrial Engineering, 41(4), 389-404.
    34.
  34. Partovi, F. Y., & Burton, J. (1993). Using the analytic hierarchy process for ABC analysis. International Journal of Operations & Production Management, 13(9), 29-44.
    35.
  35. Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700.
    36.
  36. Rezaei, J. (2007). A fuzzy model for multi-criteria inventory classification. Analysis of Manufacturing Systems, 167-172.
    37.
  37. Kiriş, Ş. (2013). Multi-criteria inventory classification by using a fuzzy analytic network process (ANP) approach. Informatica, 24(2), 199-217.
    38.
  38. Safari, H., Faraji, Z., & Majidian, S. (2016). Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. Journal of Intelligent Manufacturing, 27(2), 475-486.
    39.
  39. Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167.
    40.
  40. Šimunović, K., Šimunović, G., & Šarić, T. (2009). Application of artificial neural networks to multiple criteria inventory classification. Strojarstvo: časopis za teoriju i praksu u strojarstvu, 51(4), 313-321.
    41.
  41. Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754.
    42.
  42. Yu, M. C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421.
    43.
  43. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
    44.
  44. Zhou, P., & Fan, L. (2007). A note on multi-criteria ABC inventory classification using weighted linear optimization. European journal of operational research, 182(3), 1488-1491.
    45.

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