Araştırma Makalesi

Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm

Cilt: 27 Sayı: 1 20 Ocak 2025
PDF İndir
EN TR

Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm

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.

Anahtar Kelimeler

Kaynakça

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. M. Velasquez and P. T. Hester, ‘An Analysis of Multi-Criteria Decision Making Methods’, vol. 10, no. 2, 2013.
  7. F. Yiğit, ‘A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations’, Decision Analytics Journal, p. 100259, 2023.
  8. 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.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Endüstri Mühendisliği, İmalat Yönetimi, Üretimde Optimizasyon

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

16 Ocak 2025

Yayımlanma Tarihi

20 Ocak 2025

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. https://doi.org/10.25092/baunfbed.1578402
AMA
1.Yiğit F, Lazarevska AM. Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. BAUN Fen. Bil. Enst. Dergisi. 2025;27(1):384-396. doi:10.25092/baunfbed.1578402
Chicago
Yiğit, Fatih, ve Ana M. Lazarevska. 2025. “Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27 (1): 384-96. https://doi.org/10.25092/baunfbed.1578402.
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
[1]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, Oca. 2025, doi: 10.25092/baunfbed.1578402.
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 (01 Ocak 2025): 384-396. https://doi.org/10.25092/baunfbed.1578402.
JAMA
1.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, Ocak 2025, ss. 384-96, doi:10.25092/baunfbed.1578402.
Vancouver
1.Fatih Yiğit, Ana M. Lazarevska. Production planning optimization with fuzzy analytic hierarchy process and genetic algorithm. BAUN Fen. Bil. Enst. Dergisi. 01 Ocak 2025;27(1):384-96. doi:10.25092/baunfbed.1578402