TY  - JOUR
T1  - Analitik Hiyerarşi Prosesi ve Bulanık Mantık Yaklaşımlarının İhaleyi Kazanacak Kuruluşun Seçiminde Kullanılması
TT  - Use of Analytical Hierarchy Process and Fuzzy Logic Approaches in Selection of the Organization That Will Win the Tender
AU  - Altıntaş, Serhat
AU  - Uğur, Latif Onur
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.34248/bsengineering.1638291
JF  - Black Sea Journal of Engineering and Science
JO  - BSJ Eng. Sci.
PB  - Karyay Karadeniz Yayımcılık Ve Organizasyon Ticaret Limited Şirketi
WT  - DergiPark
SN  - 2619-8991
SP  - 1279
EP  - 1296
VL  - 8
IS  - 5
LA  - tr
AB  - Bu çalışmanın amacı, yüklenici seçim süreçlerinde kazanan kuruluşun, özellikle Analitik Hiyerarşi Prosesi ve Bulanık Mantık yaklaşımlarının entegrasyonunu incelemekte ve bu yöntemlerin etkinliğini değerlendirmektedir. Çalışma inşaat sektörüne yüklenici seçim kriterleri anlayışana ve en iyi yüklenici seçim süreçlerinin analiz edilmesi, seçilmesi için bir yöntem olan Analitik Hiyerarşi Prosesi ve Bulanık Mantık yaklaşımları ile katkıda bulunacaktır. Bu bağlamda kullanılan yöntemler, firmaların performansını objektif bir şekilde ölçülerek karşılaştırılmıştır. Sağlanan Python betiğinin Analitik Hiyerarşi Prosesi ve Bulanık Mantık yöntemlerini kullanarak yüklenici firmaların değerlendirilmesinin nasıl gerçekleştirdiğini detaylı bir şekilde açıklamaktadır. Çalışmada, ihalede başarılı bir yüklenici seçimi karar verme süreci için kritik faktörleri belirlemek, bu faktörleri değerlendirmek ve sonuçları analiz etmek amacıyla Analitik Hiyerarşi Prosesi ve Bulanık Mantık yöntemlerinin nasıl uygulanabileceğini detaylı bir şekilde açıklamaktadır. Ayrıca, her iki yaklaşımın avantajları, dezavantajları ve uygulama bağlamında karşılaştırılması yapılarak, ihale süreçlerinde karar verme süreçlerinin iyileştirilmesine yönelik öneriler sunulmaktadır. Bu çalışmanın Analitik Hiyerarşi Prosesi sonuçlarına göre; Firma-1 %78 ile en iyi performansa sahip firma olarak öne çıktığı tespit edilmiştir. Kalite, teknik yeterlilik ve maliyet gibi kriterlerde yüksek puanlar almış olduğu sonucuna varılmıştır. Bulanık Mantık sonuçlarına göre; Firma-1 yine %68.20 ile en yüksek puanı almış, ancak Bulanık Mantıkta bazı farklılıklar olduğu tespit edilmiştir. Bulanık mantıkta Firma-15, Firma-8 gibi firmalar öne çıkarken, Analitik Hiyerarşi Prosesi sonuçlarında daha geride kalan firmalar olduğuda tespit edilmiştir. Bu da Bulanık Mantık değerlendirme kriterlerinin esnekliğini ve bu iki yöntemin farklı neticeler verebileceği sonucuna varılmıştır. Bu çalışma, iş dünyasındaki profesyoneller, akademisyenler ve karar alıcılar için ihale süreçlerinde etkili karar verme stratejileri geliştirmek amacıyla bir temel oluşturmayı hedeflemektedir.
KW  - Python
KW  - Analitik hiyerarşi prosesi
KW  - Çok kriterli karar verme
KW  - Bulanık mantık
N2  - The purpose of this study is to examine the integration of the winning organization, especially Analytical Hierarchy Process and Fuzzy Logic approaches, in contractor selection processes and evaluate the effectiveness of these methods. The study will contribute to the understanding of contractor selection criteria in the construction industry and Analytical Hierarchy Process and Fuzzy Logic approaches, which are a method for analyzing and selecting the best contractor selection processes. The methods used in this context were compared by objectively measuring the performance of the companies. It explains in detail how the provided Python script performs the evaluation of contractor companies using Analytical Hierarchy Process and Fuzzy Logic methods. The study explains in detail how Analytical Hierarchy Process and Fuzzy Logic methods can be applied to identify critical factors for the decision-making process of successful contractor selection in the tender, evaluate these factors and analyze the results. Additionally, by comparing the advantages, disadvantages and application context of both approaches, suggestions are offered to improve decision-making processes in tender processes. According to the Analytical Hierarchy Process results of this study; It was determined that Company-1 stood out as the company with the best performance with 78%. It was concluded that it received high scores in criteria such as quality, technical competence and cost. According to Fuzzy Logic results; Company-1 again received the highest score with 68.20%, but it was determined that there were some differences in Fuzzy Logic. While companies such as Company-15 and Company-8 stand out in fuzzy logic, it has also been determined that there are companies that lag behind in the Analytical Hierarchy Process results. This shows the flexibility of Fuzzy Logic evaluation criteria and the conclusion that these two methods can give different results. This study aims to provide a basis for professionals, academics and decision makers in the business world to develop effective decision-making strategies in tender processes.
CR  - Acar E, Karpuz-Enücük G. 2022. Using the analytic hierarchy process for store manager selection: a real case study. J Econom Stat, 1(36): 63–76
CR  - Afolayan H, Ojokoh BA, Adetunmbi A. 2021. Feedback integrated web-based multi-criteria group decision support model for contractor selection using fuzzy analytic hierarchy process. Intell Syst Appl, 1251(2): 511–528
CR  - Ahmad AM, Jrad F, Makia S. 2023. A Data-driven approach to supplier selection in industrial construction projects. J Univ Duhok, 26(2): 201–215
CR  - Akar O. 2024. Estimation of the effect of electric vehicles on the aging of distribution transformers using fuzzy logic. Balk J Electr Comput Eng, 12(3): 199–205
CR  - Akarslan-Kodaloğlu F, Kodaloğlu M. 2023. Determining the drying rates of fabrics with different knit structures by fuzzy logic method. Int J Comput Exp Sci Eng, 9(2): 191–196
CR  - Allahverdi Ç, Allahverdi Y. 2024. Diffusion Limited Aggregation Via Python: Dendritic Structures and Algorithmic Art. JSR A, 58(1): 99–112
CR  - Alsaadoun S, Tchier F. 2024. Proposed intelligent irrigation system for riyadh city using fuzzy logic. EPSTEM, 28(1): 397–407
CR  - Alsmadi O, Abu-Hammour Z, Mahafzah K. 2024. Digital systems model order reduction with substructure preservation and fuzzy logic control. EPSTEM, 28(1): 14–22
CR  - Anlak S, Düven E. 2023. Improving end-point position control in hydraulic testing machines with a fuzzy logic based approach. JARNAS, 9(3): 531–544
CR  - Aslan B, Areta-Hızıroğlu O. 2024. Prediction of lung cancer with fuzzy logic methods: a systematic review. FATHER, 4(2): 155–192
CR  - Boyacıoğlu NM, Kocakulak T, Batar M, Uyumaz A, Solmaz H. 2023. Modeling and control of a PEM fuel cell hybrid energy system used in a vehicle with fuzzy logic method. Int J Comput Exp Sci Eng, 7(4): 295–308
CR  - Çetinkaya A. 2024. Fuzzy Logic Approach for Predicting Student Achievement in Scratch. Konjes, 12(2): 344–357
CR  - Eke G, Elgy J, Wedawatta G. 2019. Establishing a link between contractor selection strategy and project outcomes: simulation study. J Constr Eng Manag, 145(10): 1–36
CR  - Eltaleb M, Çelik H. 2024. PLC controlled fuzzy logic-based egg hatching machine. Turk J Sci Technol, 19(2): 339–350
CR  - Erdin C, Çağlar M. 2021. Rural fire risk assessment in geographic information system environment using fuzzy logic and AHP approaches. Pol J Environ Stud, 30(6): 4971–4984
CR  - Erener ŞA. 2024. Python-Based Evaluation of Road Network Constraints for Electric Scooters and Bicycles: İzmit Example. IJEG, 9(1): 34–48
CR  - Eze EC, Awodele IA, Adegboyega AA, Onyeagam OP, Guto JA. 2020. Assessment of the triggers of inefficient materials management practices by construction SMEs in Nigeria. Int J Real Estate Stud, 14(1): 38–56
CR  - Günal AY, Mehdi R. 2024. Application of a new fuzzy logic model known as ‘SMRGT’ for estimating flow coefficient rate. FOREIGN, 8(1): 46–55
CR  - Gürler C. 2023. Ranking the Factors Affecting the Choice of Crowdfunding Web Sites with Analytic Hierarchy Process. DEU Sos Bilim Enst Derg, 25(1): 187–203
CR  - Hoseinpoor MA. 2019. The combination of DEA and AHP approach in the selection of contractors participating in tenders. Holos, 35(2): 1–15
CR  - Irma B, Baihaqi I. 2018. The integration of AHP and QFD for contractors selection. J Adv Technol Eng Res, 4(3): 118–129
CR  - Jabbarzadeh A. 2018. Application of the AHP and TOPSIS in Project Management. J Project Manage, 3(5): 125–130
CR  - Jagoda JA, Schuldt SJ, Hoisington AJ. 2020. What to do? Let’s Think Using the Analytical Hierarchy Process to Make Decisions. Front Young Minds, 8(78)
CR  - Kannan J, Jayakumar V. 2023. Sustainable Method for Tender Selection Using Linear Diophantine Multi-Fuzzy Soft Set. Commun Fac Sci Univ Ank Ser A1 Math Stat, 72(4): 976–991
CR  - Keringingo T, Karakayacı Z. 2022. Analysis of potatoes growing decisions with the analytic hierarchy process method in Burundi. Eurasian J Agric Econ, 2(1): 15–28
CR  - Khoso AR, Yusof AM, Chen ZS, Wang XJ, Skibniewski MI, Memon NA. 2021. Embedded remote group environment through modification in macbeth – an application of contractor’s selection in construction. J Civil Eng Manage, 27(8): 595–616
CR  - Lazebna N. 2021. English-Language Basis of Python Programming Language. Res Bull Ser Philol Sci Natl Univ Zaporizhzhya Polytech, 1(1): 371–376
CR  - Lüy M, Metin NA, Civelek Z. 2024. Maximum power point tracking with incremental conductance and fuzzy logic controller in solar energy systems. El Cezeri J Sci Eng, 11(1): 120–130
CR  - Maqsoom A, Bajwa S, Zahoor H, Thaheem MJ, Dawood M. 2020. Optimizing contractor’s selection and bid evaluation process in construction industry: client’s perspective. J Constr, 18(3): 445–458
CR  - Marović MP, Hanak T. 2021. A multi-criteria decision support concept for selecting the optimal contractor. Appl Sci, 11(1660): 1–17
CR  - Mızrak F, Culduz M. 2023. Application of Analytic Hierarchy Process (AHP) in Evaluating Educational Leadership Theories for Effective School Management. Avrasya Sos Ekon Aras Derg, 10(4): 137–164
CR  - Mızrak F. 2023. Analyzing criteria affecting decision-making processes of human resource management in the aviation sector – a fuzzy logic approach. J Aviation, 7(3): 376–387
CR  - Morkunaite Z, Bausys R, Zavadskas EK. 2019. Contractor selection for sgraffito decoration of cultural heritage buildings using the WASPAS-SVNS method. Sustainability, 11(22): 1–24
CR  - Naik MG, Kishore R, Dehmourdi SAM. 2021. Modeling a multi-criteria decision support system for prequalification assessment of construction contractors using CRITIC and EDAS models. Oper Res Eng Sci Theory Appl, 4(2): 79–101
CR  - Najiazarpour S, Pouresfandyani H. 2019. Assessment and selection of contractors in specific contracting projects with supply chain approach using GRAY and AHP methods as decision support. Mod Appl Sci, 13(4): 51–60
CR  - Ojokoh BA, Afolayan AH, Adetunmbi AO. 2020. Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection. Sci African, 9(471): 1–12
CR  - Okereke RA, Pepple DI, Ihekweme NM. 2022. Assessment of the major contractors’ selection criteria and their impacts in civil engineering construction projects. J Eng Technol Ind Appl, 8(36): 4–13
CR  - Onyeagam OP, Eze EC, Adegboyega AA. 2019. Assessment of quantity surveying firms’ process and product innovation drive in Nigeria. Seisense J Manage, 2(2): 22–38
CR  - Özarslan Yatak M, Hisar Ç, Şahin F. 2024. Fuzzy logic controller for half vehicle active suspension system: an assessment on ride comfort and road holding. Int J Automot Sci Technol, 8(2): 179–187
CR  - Özdem B, Düğenci M, İpek M. 2024. Determination of Electricity Production by Fuzzy Logic Method. APJESS, 12(1): 14–20
CR  - Özdemir G. 2024. Bitcoin Price Prediction with Fuzzy Logic. A HANDFUL, 28(2): 259–269
CR  - Öztekin E. 2024. 1D Fuzzy Inverse Logic Method and Its Use in the Design of Thick Reinforced Concrete Columns. Sigma, 42(2): 459–474
CR  - Prıncıvıshvamalar J, Rajesh N, Brundha B. 2023. Properties of Double Fuzzy B-Open Sets. Konuralp J Math, 11(1): 90–96
CR  - Putri CG, Nusraningrum D. 2022. Subcontractors selection of building construction project using analytical hierarchy process (AHP) and technique for others reference by similarity (TOPSIS) methods. J Theory Appl Manage, 15(2): 261–273
CR  - Razi PZ, Ramli NI, Ali MI, Ramadhansyah PJ. 2020. Selection of contractor by using analytical hierarchy process (AHP). Mater Sci Eng, 712(1): 1–7
CR  - Rustandi D, Imaroh TS. 2021. Analysis fuzzy ahp for optimization contractor selection using multi-criteria in determining the best alternative contractor. Dinasti Int J Manage Sci, 2(6): 899–914
CR  - Şimşek H, Ertürk FN, Şeker R. 2023. A Fuzzy logic approach and path algorithm for time and energy management of smart cleaning robots. Gazi Univ J Sci, 36(3): 1034–1048
CR  - Şimşek H, Özaslan İH, Eryılmaz İ. 2022. Pilot selection in airline organizations with the analytical hierarchy. Process J Aviation, 6(2): 218–227
CR  - Tafazzoli M, Hazrati A, Shrestha K, Kisi K. 2024. Enhancing contractor selection through fuzzy TOPSIS and fuzzy SAW techniques. Buildings, 14(6): 1861
CR  - Thanh NV, Hai NH, Lan NTK. 2022. Fuzzy MCDM model for selection of infectious waste management contractors. Comput Mater Continua, 72(2): 2191–2202
CR  - Tubpawatin N, Srinon R. 2023. Influential criteria for large-scale factory and warehouse main contractor selection used for end-to-end procurement risk management. Proc Int Conf Ind Eng Oper Manage, 14(16): 926–937
CR  - Umarusman N. 2023. Multi-Objective De Novo Programming With Type-2 Fuzzy Objective for Optimal System Design. Alphanumeric, 11(2): 101–124
CR  - Uyhan R, Gök Z. 2022. Mathematical success with fuzzy logic modeling. Erzincan Univ J Sci Technol, 15(3): 862–872
CR  - Uysal LK, Altın N. 2023. Modelling and fuzzy logic based control scheme for a series hybrid electric vehicle. J Energy Syst, 7(1): 106–120
CR  - Warrad OI, Abdulaal RMS, Bafail O, Alamoudi MH. 2022. Four integrated MCDM models for construction contractors’ selection application at Al-Quds University. Int Rev Basic Appl Sci, 9(4): 317–329
CR  - Wibisono Adhipradana ST, Yudo A. 2024. Project selection of Indonesian local oil and gas service company using analytical hierarchy process (AHP). Int J Curr Sci Res Rev, 7(1): 395–400
CR  - Yarahmadi P, Dashti S, Sabzghabaei GR. 2018. Assessment and ranking of contractors from the point of view HSE performance using multi-criteria decision making method (AHP and TOPSIS) in Imam Khomeini port complex. J Occup Hyg Eng, 4(4): 70–80
CR  - Yeşilyurt M, Ayik YZ. 2024. Comparison of C# and Python Programming Languages in Terms of Performance and Coding on SQL Server DML Operations. NanoEra, 4(1): 23–33
CR  - Yıldırım E, Avcı E, Akgün Tanbay N. 2023. Prediction of unconfined compressive strength of microfine cement injected sands using fuzzy logic method. Asian J Educ Soc Stud, 11(2): 87–94
CR  - Yıldız MA, Kıpçak F, Erdil B. 2024. Evaluation of earthquake performance of reinforced concrete buildings with fuzzy logic method. Bitlis Eren Univ J Sci, 13(3): 601–617
CR  - Yılmaz H, Altun AA, Bilen M. 2023. Data center control application with fuzzy logic. Adv Artist Intel Res, 3(2): 54–65
CR  - Zengin B, Usta P, Onat Ö. 2023. Fuzzy logic methods for determining the mechanical behavior of masonry walls. Researcher, 3(2): 86–96.
UR  - https://doi.org/10.34248/bsengineering.1638291
L1  - https://dergipark.org.tr/tr/download/article-file/4602515
ER  -