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Bulanık analitik hiyerarşi prosesi ve genetik algoritma ile üretim planlama optimizasyonu

Yıl 2025, Cilt: 27 Sayı: 1, 384 - 396

Öz

Rekabetin yoğun olduğu iş dünyasında, üretim planlamasının doğru bir şekilde yapılması, verimliliği artırmak ve kaynak (malzeme, enerji, çalışanlar) ile ilgili maliyetleri düşürmek için önemlidir. Farklı ürünler arasında geçiş sürelerinin değişiklik gösterdiği asimetrik kurulum süreleri ile başa çıkmak, bu görevi çok daha zorlaştırır. Geleneksel planlama teknikleri genellikle bu nüansları göz ardı eder ve optimal olmayan çizelgeler üretir. Bu makale, asimetrik kurulum süreleri ve Genetik Algoritma (GA) ile Bulanık Analitik Hiyerarşi Prosesi (B-AHP) kullanarak üretim planlamasını optimize etmeye yönelik yeni bir yaklaşım önermektedir.

BAHS, belirsizlik ve bulanıklığı kapsayan bulanık mantığın gücünü, Analitik Hiyerarşi Prosesi’nin (AHP) yapılandırılmış hiyerarşisiyle birleştirir. Önerilen metodoloji, adım adım bir süreç içerir. İlk aşama, temel hedefleri tanımlar: iş bitirme süresi, toplam atık maliyeti ve maksimum ağırlıklı gecikme. İlk aşamada karar vericiler, her kriterin kendi hiyerarşi seviyesindeki göreceli önemini bulanık sayılar kullanarak karşılaştırır. Bu karşılaştırmaların tutarlılığı, bulanık tutarlılık oranı hesaplamaları ile değerlendirilir. Aynı zamanda, her üretim planlama alternatifi için genel öncelik ağırlıkları, hiyerarşi boyunca bulanık yargıların toplamı alınarak belirlenir. İkinci aşamada, hesaplanan ağırlıklar kullanılarak, asimetrik kurulum süreleri ile sıralama ve lot büyüklüğü değişkenlerini dikkate alarak üretim planı GA ile optimize edilir. Optimum çözüm ile önerilen yaklaşım kullanılarak karşılaştırmalar gerçekleştirilir.

Kaynakça

  • L. Liu, Q. Zhao, E. D. R. Santibanez Gonzalez, and X. Xi, ‘Sourcing and production decisions for perishable items under quantity discounts and its impacts on environment’, Journal of Cleaner Production, vol. 317, p. 128455, Oct. 2021, doi: 10.1016/j.jclepro.2021.128455.
  • L. Zhao, B. Wang, and C. Shen, ‘A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation’, PLoS ONE, vol. 16, no. 6, p. e0252293, Jun. 2021, doi: 10.1371/journal.pone.0252293.
  • Z. Hu, W. Liu, S. Ling, and K. Fan, ‘Research on multi-objective optimal scheduling considering the balance of labor workload distribution’, PLoS ONE, vol. 16, no. 8, p. e0255737, Aug. 2021, doi: 10.1371/journal.pone.0255737.
  • I. Thammachantuek and M. Ketcham, ‘Path planning for autonomous mobile robots using multi-objective evolutionary particle swarm optimization’, PLoS ONE, vol. 17, no. 8, p. e0271924, Aug. 2022, doi: 10.1371/journal.pone.0271924.
  • M. Aruldoss, T. M. Lakshmi, and V. P. Venkatesan, ‘A Survey on Multi Criteria Decision Making Methods and Its Applications’, American Journal of Mechanical Engineering.
  • M. Velasquez and P. T. Hester, ‘An Analysis of Multi-Criteria Decision Making Methods’, vol. 10, no. 2, 2013.
  • F. Yiğit, ‘A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations’, Decision Analytics Journal, p. 100259, 2023.
  • C. Kahraman, ‘Proportional picture fuzzy sets and their AHP extension: Application to waste disposal site selection’, Expert Systems with Applications, vol. 238, p. 122354, Mar. 2024, doi: 10.1016/j.eswa.2023.122354.
  • T. Saaty, ‘The analytic hierarchy process (AHP) for decision making’, in Kobe, Japan, 1980, pp. 1–69.
  • F. H. F. Liu and H. L. Hai, ‘The voting analytic hierarchy process method for selecting supplier’, International Journal of Production Economics, vol. 97, no. 3, pp. 308–317, 2005, doi: 10.1016/j.ijpe.2004.09.005.
  • M. Tavana, M. Soltanifar, and F. J. Santos-Arteaga, ‘Analytical hierarchy process : revolution and evolution’, Annals of Operations Research, 2021, doi: 10.1007/s10479-021-04432-2.
  • C. Kahraman, S. Çebi, S. Ç. Onar, and B. Öztayşi, ‘Recent Developments on Fuzzy AHP and ANP Under Vague and Imprecise Data: Evidence from INFUS Conferences’, International Journal of the Analytic Hierarchy Process, vol. 14, no. 2, pp. 1–17, 2022, doi: 10.13033/IJAHP.V14I2.1033.
  • M. B. S. Alaa El Din M. Riad Nouran M. Radwan, Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system. Int J Adv Comput Technol (IJACT) 8(5):95–105., 2016.
  • A. Alinezad, A. Seif, and N. Esfandiari, ‘Supplier evaluation and selection with QFD and FAHP in a pharmaceutical company’, International Journal of Advanced Manufacturing Technology, vol. 68, no. 1–4, pp. 355–364, 2013, doi: 10.1007/s00170-013-4733-3.
  • M. Bakir and Ö. Atalik, ‘Application of fuzzy ahp and fuzzy marcos approach for the evaluation of e-service quality in the airline industry’, Decision Making: Applications in Management and Engineering, vol. 4, no. 1, pp. 127–152, 2021, doi: 10.31181/dmame2104127b.
  • A. M. F. Saghih * and A. P. S.-H. Mirghaderi, ‘X2- Perishable inventory management using GA-ANN and Saeideh Farajzadeh Bardeji’, vol. 13, no. 3, pp. 347–382, 2020.
  • S. Rabah, A. B. Zaier, and H. Dahman, ‘New Energy Efficient Clustering Method Based on Fuzzy Logic and Genetic Algorithm in IoT Network’, Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, pp. 29–33, 2020, doi: 10.1109/SSD49366.2020.9364211.
  • P. Dziwinski and L. Bartczuk, ‘A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic’, IEEE Transactions on Fuzzy Systems, vol. 28, no. 6, pp. 1140–1154, 2020, doi: 10.1109/TFUZZ.2019.2957263.
  • M. R. Garey, D. S. Johnson, and R. Sethi, ‘The Complexity of Flowshop and Jobshop Scheduling’, Mathematics of Operations Research, vol. 1, no. 2, pp. 117–129, 1976.
  • H. Xiong, S. Shi, D. Ren, and J. Hu, ‘A survey of job shop scheduling problem: The types and models’, Computers & Operations Research, vol. 142, p. 105731, Jun. 2022, doi: 10.1016/j.cor.2022.105731.
  • K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai, and Q. K. Pan, ‘A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion’, Expert Systems with Applications, vol. 42, no. 21, pp. 7652–7663, Nov. 2015, doi: 10.1016/j.eswa.2015.06.004.
  • M. Á. González, C. Rodríguez Vela, and R. Varela, ‘An Efficient Memetic Algorithm for the Flexible Job Shop with Setup Times’, ICAPS, vol. 23, pp. 91–99, Jun. 2013, doi: 10.1609/icaps.v23i1.13542.
  • L. R. Abreu, J. O. Cunha, B. A. Prata, and J. M. Framinan, ‘A genetic algorithm for scheduling open shops with sequence-dependent setup times’, Computers & Operations Research, vol. 113, p. 104793, Jan. 2020, doi: 10.1016/j.cor.2019.104793.
  • N. Al-Hinai and T. Y. ElMekkawy, ‘Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm’, International Journal of Production Economics, vol. 132, no. 2, pp. 279–291, Aug. 2011, doi: 10.1016/j.ijpe.2011.04.020.
  • E. P. Bafghi, ‘Clustering of Customers Based on Shopping Behavior and Employing Genetic Algorithms’, Engineering, Technology & Applied Science Research, vol. 7, no. 1, pp. 1420–1424, 2017, doi: 10.48084/etasr.752.
  • S. Veskovic, Ž. Stevic, G. Stojic, M. Vasiljevic, and S. Milinkovic, ‘Evaluation of the railway management model by using a new integrated model delphi-swara-mabac’, Decision Making: Applications in Management and Engineering, vol. 1, no. 2, pp. 34–50, 2018, doi: 10.31181/dmame1802034v.
  • F. Zhao, Z. Wang, and L. Wang, ‘A Reinforcement Learning Driven Artificial Bee Colony Algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times’, IEEE Trans. Automat. Sci. Eng., vol. 20, no. 4, pp. 2305–2320, Oct. 2023, doi: 10.1109/TASE.2022.3212786.
  • Y. Li, X. Li, L. Gao, and L. Meng, ‘An improved artificial bee colony algorithm for distributed heterogeneous hybrid flowshop scheduling problem with sequence-dependent setup times’, Computers & Industrial Engineering, vol. 147, p. 106638, Sep. 2020, doi: 10.1016/j.cie.2020.106638.
  • Y.-W. Chen, Y.-J. Zhu, G.-K. Yang, and Y.-Z. Lu, ‘Improved extremal optimization for the asymmetric traveling salesman problem’, Physica A: Statistical Mechanics and its Applications, vol. 390, no. 23–24, pp. 4459–4465, Nov. 2011, doi: 10.1016/j.physa.2011.06.070.
  • X. Xin, Q. Jiang, C. Li, S. Li, and K. Chen, ‘Permutation flow shop energy-efficient scheduling with a position-based learning effect’, International Journal of Production Research, vol. 61, no. 2, pp. 382–409, Jan. 2023, doi: 10.1080/00207543.2021.2008041.
  • S. Wu and L. Liu, ‘Green Hybrid Flow Shop Scheduling Problem Considering Sequence Dependent Setup Times and Transportation Times’, IEEE Access, vol. 11, pp. 39726–39737, 2023, doi: 10.1109/ACCESS.2023.3269293.
  • F. Zhao, S. Di, and L. Wang, ‘A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem’, IEEE Trans. Cybern., vol. 53, no. 5, pp. 3337–3350, May 2023, doi: 10.1109/TCYB.2022.3192112.
  • P. J. M. V. Laarhoven and W. Pedrycz, ‘A fuzzy extension of Saaty’s priority theory’, Fuzzy sets and Systems, vol. 11, no. 1–3, pp. 229–241, 1983.
  • T.-S. Liou and M.-J. J. Wang, ‘Ranking fuzzy numbers with integral value’, Fuzzy Sets and Systems, vol. 50, no. 3, pp. 247–255, Sep. 1992, doi: 10.1016/0165-0114(92)90223-Q.
  • G. Papazoglou and P. Biskas, ‘Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem’, Energies, vol. 16, no. 3, p. 1152, Jan. 2023, doi: 10.3390/en16031152.
  • D. Y. Chang, ‘Applications of the extent analysis method on fuzzy AHP’, European Journal of Operational Research, vol. 95, no. 3, pp. 649–655, 1996, doi: 10.1016/0377-2217(95)00300-2.
  • F. R. Lima-Junior and L. C. R. Carpinetti, ‘Dealing with the problem of null weights and scores in Fuzzy Analytic Hierarchy Process’, Soft Comput, vol. 24, no. 13, pp. 9557–9573, Jul. 2020, doi: 10.1007/s00500-019-04464-8.

Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm

Yıl 2025, Cilt: 27 Sayı: 1, 384 - 396

Öz

Proper production planning is essential for improving productivity and lowering resource (material, energy, employees) related costs in the highly competitive business world. Dealing with the challenges of asymmetric setup times—where the time required to switch between manufacturing different products varies —makes this task much more difficult. Conventional planning techniques frequently ignore these articulations and produce sub-optimal schedules. This paper proposes a novel approach to tackle the following challenge: optimizing production planning using the Fuzzy Analytic Hierarchy Process (FAHP) with asymmetric setup times and Genetic Algorithm (GA). The proposed methodology involves a step-by-step process. The first stage defines key objectives: makespan, total waste cost, and maximum weighted tardiness. Decision-makers compare the relative importance of each criterion within its hierarchy level using fuzzy numbers. The consistency of these comparisons is assessed using fuzzy consistency ratio computations. At the same time, the overall priority weights for each production planning alternative are determined by summing fuzzy judgments across the hierarchy. In the second stage, the production plan is optimized using GA, considering sequence and lot size variables and asymmetric setup times, by applying the computed weights. The comparisons are performed using the proposed approach with the optimum solution.

Kaynakça

  • L. Liu, Q. Zhao, E. D. R. Santibanez Gonzalez, and X. Xi, ‘Sourcing and production decisions for perishable items under quantity discounts and its impacts on environment’, Journal of Cleaner Production, vol. 317, p. 128455, Oct. 2021, doi: 10.1016/j.jclepro.2021.128455.
  • L. Zhao, B. Wang, and C. Shen, ‘A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation’, PLoS ONE, vol. 16, no. 6, p. e0252293, Jun. 2021, doi: 10.1371/journal.pone.0252293.
  • Z. Hu, W. Liu, S. Ling, and K. Fan, ‘Research on multi-objective optimal scheduling considering the balance of labor workload distribution’, PLoS ONE, vol. 16, no. 8, p. e0255737, Aug. 2021, doi: 10.1371/journal.pone.0255737.
  • I. Thammachantuek and M. Ketcham, ‘Path planning for autonomous mobile robots using multi-objective evolutionary particle swarm optimization’, PLoS ONE, vol. 17, no. 8, p. e0271924, Aug. 2022, doi: 10.1371/journal.pone.0271924.
  • M. Aruldoss, T. M. Lakshmi, and V. P. Venkatesan, ‘A Survey on Multi Criteria Decision Making Methods and Its Applications’, American Journal of Mechanical Engineering.
  • M. Velasquez and P. T. Hester, ‘An Analysis of Multi-Criteria Decision Making Methods’, vol. 10, no. 2, 2013.
  • F. Yiğit, ‘A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations’, Decision Analytics Journal, p. 100259, 2023.
  • C. Kahraman, ‘Proportional picture fuzzy sets and their AHP extension: Application to waste disposal site selection’, Expert Systems with Applications, vol. 238, p. 122354, Mar. 2024, doi: 10.1016/j.eswa.2023.122354.
  • T. Saaty, ‘The analytic hierarchy process (AHP) for decision making’, in Kobe, Japan, 1980, pp. 1–69.
  • F. H. F. Liu and H. L. Hai, ‘The voting analytic hierarchy process method for selecting supplier’, International Journal of Production Economics, vol. 97, no. 3, pp. 308–317, 2005, doi: 10.1016/j.ijpe.2004.09.005.
  • M. Tavana, M. Soltanifar, and F. J. Santos-Arteaga, ‘Analytical hierarchy process : revolution and evolution’, Annals of Operations Research, 2021, doi: 10.1007/s10479-021-04432-2.
  • C. Kahraman, S. Çebi, S. Ç. Onar, and B. Öztayşi, ‘Recent Developments on Fuzzy AHP and ANP Under Vague and Imprecise Data: Evidence from INFUS Conferences’, International Journal of the Analytic Hierarchy Process, vol. 14, no. 2, pp. 1–17, 2022, doi: 10.13033/IJAHP.V14I2.1033.
  • M. B. S. Alaa El Din M. Riad Nouran M. Radwan, Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system. Int J Adv Comput Technol (IJACT) 8(5):95–105., 2016.
  • A. Alinezad, A. Seif, and N. Esfandiari, ‘Supplier evaluation and selection with QFD and FAHP in a pharmaceutical company’, International Journal of Advanced Manufacturing Technology, vol. 68, no. 1–4, pp. 355–364, 2013, doi: 10.1007/s00170-013-4733-3.
  • M. Bakir and Ö. Atalik, ‘Application of fuzzy ahp and fuzzy marcos approach for the evaluation of e-service quality in the airline industry’, Decision Making: Applications in Management and Engineering, vol. 4, no. 1, pp. 127–152, 2021, doi: 10.31181/dmame2104127b.
  • A. M. F. Saghih * and A. P. S.-H. Mirghaderi, ‘X2- Perishable inventory management using GA-ANN and Saeideh Farajzadeh Bardeji’, vol. 13, no. 3, pp. 347–382, 2020.
  • S. Rabah, A. B. Zaier, and H. Dahman, ‘New Energy Efficient Clustering Method Based on Fuzzy Logic and Genetic Algorithm in IoT Network’, Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, pp. 29–33, 2020, doi: 10.1109/SSD49366.2020.9364211.
  • P. Dziwinski and L. Bartczuk, ‘A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic’, IEEE Transactions on Fuzzy Systems, vol. 28, no. 6, pp. 1140–1154, 2020, doi: 10.1109/TFUZZ.2019.2957263.
  • M. R. Garey, D. S. Johnson, and R. Sethi, ‘The Complexity of Flowshop and Jobshop Scheduling’, Mathematics of Operations Research, vol. 1, no. 2, pp. 117–129, 1976.
  • H. Xiong, S. Shi, D. Ren, and J. Hu, ‘A survey of job shop scheduling problem: The types and models’, Computers & Operations Research, vol. 142, p. 105731, Jun. 2022, doi: 10.1016/j.cor.2022.105731.
  • K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai, and Q. K. Pan, ‘A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion’, Expert Systems with Applications, vol. 42, no. 21, pp. 7652–7663, Nov. 2015, doi: 10.1016/j.eswa.2015.06.004.
  • M. Á. González, C. Rodríguez Vela, and R. Varela, ‘An Efficient Memetic Algorithm for the Flexible Job Shop with Setup Times’, ICAPS, vol. 23, pp. 91–99, Jun. 2013, doi: 10.1609/icaps.v23i1.13542.
  • L. R. Abreu, J. O. Cunha, B. A. Prata, and J. M. Framinan, ‘A genetic algorithm for scheduling open shops with sequence-dependent setup times’, Computers & Operations Research, vol. 113, p. 104793, Jan. 2020, doi: 10.1016/j.cor.2019.104793.
  • N. Al-Hinai and T. Y. ElMekkawy, ‘Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm’, International Journal of Production Economics, vol. 132, no. 2, pp. 279–291, Aug. 2011, doi: 10.1016/j.ijpe.2011.04.020.
  • E. P. Bafghi, ‘Clustering of Customers Based on Shopping Behavior and Employing Genetic Algorithms’, Engineering, Technology & Applied Science Research, vol. 7, no. 1, pp. 1420–1424, 2017, doi: 10.48084/etasr.752.
  • S. Veskovic, Ž. Stevic, G. Stojic, M. Vasiljevic, and S. Milinkovic, ‘Evaluation of the railway management model by using a new integrated model delphi-swara-mabac’, Decision Making: Applications in Management and Engineering, vol. 1, no. 2, pp. 34–50, 2018, doi: 10.31181/dmame1802034v.
  • F. Zhao, Z. Wang, and L. Wang, ‘A Reinforcement Learning Driven Artificial Bee Colony Algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times’, IEEE Trans. Automat. Sci. Eng., vol. 20, no. 4, pp. 2305–2320, Oct. 2023, doi: 10.1109/TASE.2022.3212786.
  • Y. Li, X. Li, L. Gao, and L. Meng, ‘An improved artificial bee colony algorithm for distributed heterogeneous hybrid flowshop scheduling problem with sequence-dependent setup times’, Computers & Industrial Engineering, vol. 147, p. 106638, Sep. 2020, doi: 10.1016/j.cie.2020.106638.
  • Y.-W. Chen, Y.-J. Zhu, G.-K. Yang, and Y.-Z. Lu, ‘Improved extremal optimization for the asymmetric traveling salesman problem’, Physica A: Statistical Mechanics and its Applications, vol. 390, no. 23–24, pp. 4459–4465, Nov. 2011, doi: 10.1016/j.physa.2011.06.070.
  • X. Xin, Q. Jiang, C. Li, S. Li, and K. Chen, ‘Permutation flow shop energy-efficient scheduling with a position-based learning effect’, International Journal of Production Research, vol. 61, no. 2, pp. 382–409, Jan. 2023, doi: 10.1080/00207543.2021.2008041.
  • S. Wu and L. Liu, ‘Green Hybrid Flow Shop Scheduling Problem Considering Sequence Dependent Setup Times and Transportation Times’, IEEE Access, vol. 11, pp. 39726–39737, 2023, doi: 10.1109/ACCESS.2023.3269293.
  • F. Zhao, S. Di, and L. Wang, ‘A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem’, IEEE Trans. Cybern., vol. 53, no. 5, pp. 3337–3350, May 2023, doi: 10.1109/TCYB.2022.3192112.
  • P. J. M. V. Laarhoven and W. Pedrycz, ‘A fuzzy extension of Saaty’s priority theory’, Fuzzy sets and Systems, vol. 11, no. 1–3, pp. 229–241, 1983.
  • T.-S. Liou and M.-J. J. Wang, ‘Ranking fuzzy numbers with integral value’, Fuzzy Sets and Systems, vol. 50, no. 3, pp. 247–255, Sep. 1992, doi: 10.1016/0165-0114(92)90223-Q.
  • G. Papazoglou and P. Biskas, ‘Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem’, Energies, vol. 16, no. 3, p. 1152, Jan. 2023, doi: 10.3390/en16031152.
  • D. Y. Chang, ‘Applications of the extent analysis method on fuzzy AHP’, European Journal of Operational Research, vol. 95, no. 3, pp. 649–655, 1996, doi: 10.1016/0377-2217(95)00300-2.
  • F. R. Lima-Junior and L. C. R. Carpinetti, ‘Dealing with the problem of null weights and scores in Fuzzy Analytic Hierarchy Process’, Soft Comput, vol. 24, no. 13, pp. 9557–9573, Jul. 2020, doi: 10.1007/s00500-019-04464-8.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği, İmalat Yönetimi, Üretimde Optimizasyon
Bölüm Araştırma Makalesi
Yazarlar

Fatih Yiğit 0000-0002-7919-544X

Ana M. Lazarevska Bu kişi benim 0000-0002-3493-8465

Erken Görünüm Tarihi 16 Ocak 2025
Yayımlanma Tarihi
Gönderilme Tarihi 4 Kasım 2024
Kabul Tarihi 14 Ocak 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 27 Sayı: 1

Kaynak Göster

APA Yiğit, F., & Lazarevska, A. M. (2025). Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 384-396.
AMA Yiğit F, Lazarevska AM. Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. BAUN Fen. Bil. Enst. Dergisi. Ocak 2025;27(1):384-396.
Chicago Yiğit, Fatih, ve Ana M. Lazarevska. “Production Planning Optimization With Fuzzy Analytic Hierarchy Process and Genetic Algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27, sy. 1 (Ocak 2025): 384-96.
EndNote Yiğit F, Lazarevska AM (01 Ocak 2025) Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27 1 384–396.
IEEE F. Yiğit ve A. M. Lazarevska, “Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm”, BAUN Fen. Bil. Enst. Dergisi, c. 27, sy. 1, ss. 384–396, 2025.
ISNAD Yiğit, Fatih - Lazarevska, Ana M. “Production Planning Optimization With Fuzzy Analytic Hierarchy Process and Genetic Algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27/1 (Ocak 2025), 384-396.
JAMA Yiğit F, Lazarevska AM. Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. BAUN Fen. Bil. Enst. Dergisi. 2025;27:384–396.
MLA Yiğit, Fatih ve Ana M. Lazarevska. “Production Planning Optimization With Fuzzy Analytic Hierarchy Process and Genetic Algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 27, sy. 1, 2025, ss. 384-96.
Vancouver Yiğit F, Lazarevska AM. Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. BAUN Fen. Bil. Enst. Dergisi. 2025;27(1):384-96.