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A fuzzy-TOPSIS model for maintenance outsourcing considering the quality of submitted tender documents

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper provides a multiple-experts Fuzzy-TOPSIS decision-making model for the selection among maintenance contractors based on the quality of tendering documents. The study introduces a set of selection criteria utilizing benefit and cost criteria from literature. The proposed model aggregates subjective linguistic assessments of multiple experts that express their opinions on the degree of importance of criteria and allows multiple decisionmakers to evaluate the compliance of contractors’ documents. For a case study, the model is applied to select among contractors tendering to maintain the heavy-duty cranes of an international steel company from literature. Several decision-making scenarios are investigated, and major changes in the final decision are observed. The changes in obtained results illustrate the need to better address uncertainties in rating and tendering an overqualified contractor at a higher cost.
Rocznik
Strony
443--453
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Industrial Engineering Department, Jordan University of Science and Technology, Irbid 22110, Jordan
  • Industrial Engineering Department, Jordan University of Science and Technology, Irbid 22110, Jordan
  • Industrial Engineering Department, Sakarya University, Sakarya, Turkey
  • Industrial Engineering Department, Jordan University of Science and Technology, Irbid 22110, Jordan
Bibliografia
  • 1. Alsyouf I. Cost Effective Maintenance for Competitive Advantages. Acta Wexionensia 2004; 33: 1-98.
  • 2. Alzahrani J, Emsley M. The impact of contractors' attributes on construction project success: A post construction evaluation. International Journal of Project Management 2012; 31: 313-322, https://doi.org/10.1016/j.ijproman.2012.06.006.
  • 3. Azimifard A, Moosavirad S, Ariafar S. Selecting sustainable supplier countries for Iran's steel industry at three levels by using AHP and TOPSIS methods. Resources Policy 2018; 57: 30-44, https://doi.org/10.1016/j.resourpol.2018.01.002.
  • 4. Bukowski L, Werbińska-Wojciechowska S. Using fuzzy logic to support maintenance decisions according to Resilience-Based Maintenance concept. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (2): 294-307, https://doi.org/10.17531/ein.2021.2.9.
  • 5. Cebi F, Otay I. An Integrated Approach to Supplier Selection and Order Allocation Problem: A Case Study in a Hospital. Proceedings of the 17th International Working Seminar on Production Economics, Innsbruck, Austria 2012.
  • 6. Chen, C. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 2000; 114: 1-9, https://doi.org/10.1016/S0165-0114(97)00377-1.
  • 7. Darvish M, Yasaei M, Saeedi A. Application of the graph theory and matrix methods to contractor ranking. International Journal of Project Management 2009; 27: 610-619, https://doi.org/10.1016/j.ijproman.2008.10.004.
  • 8. Fu Y. An integrated approach to catering supplier selection using AHP-ARASMCGP Methodology. Journal of Air Transport Management 2019; 75: 164-169, https://doi.org/10.1016/j.jairtraman.2019.01.011.
  • 9. Hafeez K, Malak N, Zhang Y. Outsourcing non-core assets and competences of a firm using AHP. Computers and Operations Research 2007; 34: 3592- 3608, https://doi.org/10.1016/j.cor.2006.01.004.
  • 10. Hammudah N. Multi Criteria Decision Making Model for Outsourcing Maintenance Services: A Case Study of Cranes Industry. Master thesis, Isra University, Jordan 2014
  • 11. Hua Y, Xiaoa S, Wena J, Li J. An ANP-multi-criteria-based methodology to construct maintenance networks for agricultural machinery cluster in a balanced scorecard context. Computers and Electronics in Agriculture 2019; 158: 1-10, https://doi.org/10.1016/j.compag.2019.01.031.
  • 12. Hwang C, Yoon K. Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag 1981, https://doi.org/10.1007/978-3-642-48318-9.
  • 13. Jain V, Sangaiah A, Sakhuja S, Thoduka N, Aggarwal R. Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications 2018; 29: 555-564, https://doi.org/10.1007/s00521-016-2533-z.
  • 14. Jasiulewicz-Kaczmarek M, Antosz K, Wyczółkowski R, Mazurkiewicz D, Sun B, Qian C, Ren Y. Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing. Energies 2021; 14: 1436, https://doi.org/10.3390/en14051436.
  • 15. Jaskowsk P, Biruk S, Bucon R. Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment. Automation in Construction 2010; 19: 120-126, https://doi.org/10.1016/j.autcon.2009.12.014.
  • 16. Lam K, Yu C. A multiple kernel learning-based decision support model for contractor pre-qualification. Automation in Construction 2011; 20: 531-536, https://doi.org/10.1016/j.autcon.2010.11.019.
  • 17. Mahdi I, Riley M, Fereig S, Alex A. A multicriteria approach to contractor selection. Engineering Construction and Architectural Management 2002; 9: 29-37, https://doi.org/10.1108/eb021204.
  • 18. Memari A, Dargib A, Jokara M, Robiah A, Abdul Rahim A. Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS Method. Journal of Manufacturing Systems 2019; 50: 9-24, https://doi.org/10.1016/j.jmsy.2018.11.002.
  • 19. Moline J, Coves A. Supplier Evaluation and Selection: A Review of the literature since 2007. The 7th International Conference on Industrial Engineering and Industrial Management, Spain 2013, https://doi.org/10.1007/978-3-319-04705-8_25.
  • 20. Nieto-Morote A, Ruz-Vila F. A fuzzy multi-criteria decision-making model for construction contractor prequalification. Automation in Construction 2012; 25: 8-19, https://doi.org/10.1016/j.autcon.2012.04.004.
  • 21. Pankaj G, Mukesh K, Divya M. Multi-objective optimization framework for software maintenance, component evaluation and selection involving outsourcing, redundancy and customer to customer relationship. Information Sciences 2019; 483: 21-52, https://doi.org/10.1016/j.ins.2019.01.017.
  • 22. Poudeha H, Cheshmberah M, Torabi H, Gavareshki M, Reza H. Determining and prioritizing the factors influencing the outsourcing of Complex Product Systems R&D projects employing ANP and grey-DEMATEL method (case study: Aviation Industries Organization, Iran). Technology in Society 2019; 56: 57-68, https://doi.org/10.1016/j.techsoc.2018.09.005.
  • 23. Rezaei J, Fahim P, Tavasszy L. Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications 2014; 41: 8165-8179, https://doi.org/10.1016/j.eswa.2014.07.005.
  • 24. Saghafian S, Hejazi S. Multi-criteria Group Decision Making Using a Modified Fuzzy TOPSIS Procedure. Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'05), Vienna, Austria 2005
  • 25. Singh D, Tiong R. Contractor Selection Criteria: Investigation of Opinions of Singapore Construction Practitioner. Journal of Construction Engineering and Management 2006; 132: 998-1008, https://doi.org/10.1061/(ASCE)0733-9364(2006)132:9(998).
  • 26. Victorian Civil Construction Industry, Best Practice Guide for Tendering and Contract Management. http://www.wellington.vic.gov.au/files/a708f74e-1c2f-4365-8b53-a1d300a96e05/VCCI-Best-Practice-Guide-for-Tendering-and-Contract-Management.pdf 2008
  • 27. Zhou F, Wang X, Goh M, Zhou L, He Y. Supplier portfolio of key outsourcing parts selection using a two-stage decision making framework for Chinese domestic auto-maker. Computers & Industrial Engineering 2019; 128: 559-575, https://doi.org/10.1016/j.cie.2018.12.014.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cbbc2f22-f472-4421-9a14-64bdc4c6c28a
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