Research Article
BibTex RIS Cite

MAKİNELERDE HIZ DEĞİŞİKLİĞİNİN SÜRDÜRÜLEBİLİRLİĞE ETKİSİNİN İNCELENMESİ: BİR MATEMATİKSEL MODEL

Year 2024, Volume: 35 Issue: 1, 115 - 135, 30.04.2024

Feriştah Özçelik , Tuğba Saraç , Mehmet Ertem

Abstract

Sürdürülebilirlik, ekonomik büyüme, sosyal refah ve doğal çevreyi koruma arasında denge kurmayı hedefleyen bir yaklaşımdır. Günümüzde, şirketler, toplumlar ve bireyler faaliyetlerini sürdürülebilir bir şekilde yürütmek için çözümler aramakta ve sürdürülebilirlik stratejilerini benimsemektedir. Enerji tüketimi ve kaynak kullanımı sürdürülebilirlik açısından oldukça önemli faktörlerdendir. Bu nedenle üretim faaliyetleri, geleneksel yönetim anlayışları yerine, sürdürülebilirlik stratejileri ile yönetilmelidir. Üretimde önemli bir yere sahip olan makine çizelgeleme problemini ele alan çalışmalarda genellikle sürdürülebilirlik kavramı gözardı edilmiştir. Bu çalışmada, makinaların hız değişiklikleri ve enerji tüketimleri kaynaklı maliyetler de dahil edilerek, işletmelerin sürdürülebilirlik performanslarının iyileştirilmesi hedeflenmiştir. Çalışma kapsamında hız değişikliğinin dikkate alındığı makine çizelgeleme problemi ele alınmış ve problem için bir matematiksel model geliştirilmiştir. Rassal türetilen test problemleri önerilen matematiksel model ile çözülmüştür. Elde edilen sonuçlar, makinaların hız değişikliklerinin optimize edilmesinin enerji tasarrufu sağladığını, kaynak kullanımını optimize ettiğini ve çevresel etkileri azalttığını göstermektedir. Sonuçlar, bu alanda karar vericilere ve endüstriyel kullanıcılara rehberlik edebilir ve sürdürülebilir üretim sistemlerinin geliştirilmesine katkı sağlayabilir.

Keywords

Sürdürülebilirlik hız değişimi paralel makine çizelgeleme problemi doğrusal tamsayılı programlama

References

  • Akbar, M. ve Irohara, T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production, 205, 866-883. Doi: https://doi.org/10.1016/j.jclepro.2018.09.100
  • An, Y., Li, C., Chen, X., Li, Y., Zhao, Z., ve Cao, H. (2023). An optimal energy-efficient scheduling with processing speed selection and due date constraint in a single-machine environment. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Doi: https://doi.org/10.1177/09544054231180659
  • Antoniadis, A. ve Huang, C. C. (2013). Non-preemptive speed scaling. Journal of scheduling, 16(4), 385-394. Doi: https://doi.org/10.1007/s10951-013-0312-6
  • Arriaza, O., Kim, D., Lee, D., ve Suhaimi, M. (2017). Trade-off analysis between machining time and energy consumption in impeller NC machining. Robotics and Computer-integrated Manufacturing, 43, 164-170. https://doi.org/10.1016/J.RCIM.2015.09.014
  • Behnamian, J. ve Ghomi, S. F. (2011). Hybrid flowshop scheduling with machine and resource-dependent processing times. Applied Mathematical Modelling, 35(3), 1107-1123. Doi: https://doi.org/10.1016/j.apm.2010.07.057
  • Carlucci, D., Renna, P., ve Materi, S. (2021). A Job-Shop Scheduling Decision-Making Model for Sustainable Production Planning With Power Constraint. IEEE Transactions on Engineering Management, 70(5),1923-1932. Doi: https://doi.org/10.1109/TEM.2021.3103108
  • Cota, L. P., Coelho, V. N., Guimarães, F. G., ve Souza, M. J. (2021). Bi‐criteria formulation for green scheduling with unrelated parallel machines with sequence‐dependent setup times. International Transactions in Operational Research, 28(2), 996-1017. Doi: https://doi.org/10.1111/itor.12566
  • de Athayde Prata, B., Fernandez-Viagas, V., Framinan, J. M., ve Rodrigues, C. D. (2022). Matheuristics for the flowshop scheduling problem with controllable processing times and limited resource consumption to minimize total tardiness. Computers & Operations Research, 145, 105880. Doi: https://doi.org/10.1016/j.cor.2022.105880
  • Ding, J., Schulz, S., Shen, L., Buscher, U., ve Lü, Z. (2021). Energy aware scheduling in flexible flow shops with hybrid particle swarm optimization. Computers & Operations Research, 125, 105088. Doi: https://doi.org/10.1016/j.cor.2020.105088
  • Fang, K., Uhan, N., Zhao, F., ve Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234-240. Doi: https://doi.org/10.1016/j.jmsy.2011.08.004
  • Füchtenhans, M. ve Glock, C. H. (2023). The impact of incentive-based programmes on job-shop scheduling with variable machine speeds. International Journal of Production Research, 1-19. Doi: https://doi.org/10.1080/00207543.2023.2266765
  • Jiang, T., Zhang, C., ve Sun, Q. M. (2019). Green job shop scheduling problem with discrete whale optimization algorithm. IEEE Access, 7, 43153-43166. Doi: https://doi.org/10.1109/ACCESS.2019.2908200
  • Koulamas, C. ve Kyparisis, G. J. (2022). Flow shop scheduling with two distinct job due dates. Computers & Industrial Engineering, 163, 107835. Doi: https://doi.org/10.1016/j.cie.2021.107835
  • Liu, C. H., Nanthapodej, R., ve Hsu, S. Y. (2018). Scheduling two interfering job sets on parallel machines under peak power constraint. Production Engineering, 12, 611-619. Doi: https://doi.org/10.1007/s11740-018-0840-1
  • Lu, C., Zhang, B., Gao, L., Yi, J., ve Mou, J. (2021). A knowledge-based multiobjective memetic algorithm for green job shop scheduling with variable machining speeds. IEEE Systems Journal, 16(1), 844-855. Doi: https://doi.org/10.1109/JSYST.2021.3076481
  • Luo, S., Zhang, L., ve Fan, Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production, 234, 1365-1384. Doi: https://doi.org/10.1016/j.jclepro.2019.06.151
  • Mansouri, S. A., Aktas, E., ve Besikci, U. (2016). Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788. Doi: https://doi.org/10.1016/j.ejor.2015.08.064
  • Öztop, H., Tasgetiren, M. F., Eliiyi, D. T., Pan, Q. K., ve Kandiller, L. (2020). An energy-efficient permutation flowshop scheduling problem. Expert systems with applications, 150, 113279. Doi: https://doi.org/10.1016/j.eswa.2020.113279
  • Sharma, A., Zhao, F., ve Sutherland, J. W. (2015). Econological scheduling of a manufacturing enterprise operating under a time-of-use electricity tariff. Journal of Cleaner Production, 108, 256-270. Doi: https://doi.org/10.1016/j.jclepro.2015.06.002
  • Tirkolaee, E. B., Goli, A., ve Weber, G. W. (2020). Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE transactions on fuzzy systems, 28(11), 2772-2783. Doi: https://doi.org/10.1109/TFUZZ.2020.2998174
  • Trevino-Martinez, S., Sawhney, R., ve Shylo, O. (2022). Energy-carbon footprint optimization in sequence-dependent production scheduling. Applied Energy, 315, 118949. Doi: https://doi.org/10.1016/j.apenergy.2022.118949
  • Wang, L. ve Qi, Y. (2023). Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time. CMES-Computer Modeling in Engineering & Sciences, 135(1), 325-339. Doi: https://doi.org/10.32604/cmes.2022.019730
  • Wei, Z., Liao, W., ve Zhang, L. (2022). Hybrid energy-efficient scheduling measures for flexible job-shop problem with variable machining speeds. Expert Systems with Applications, 197, 116785. Doi: https://doi.org/10.1016/j.eswa.2022.116785
  • Yin, L., Li, X., Gao, L., Lu, C., ve Zhang, Z. (2017). Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm. Advances in Mechanical Engineering, 9(4), 1687814017695959. Doi: https://doi.org/10.1177/1687814017695959
  • Yoon, H. S., Kim, E. S., Kim, M. S., Lee, J. Y., Lee, G. B., ve Ahn, S. H. (2015). Towards greener machine tools–A review on energy saving strategies and technologies. Renewable and Sustainable Energy Reviews, 48, 870-891. Doi: https://doi.org/10.1016/j.rser.2015.03.100
  • Zhang, S., Nip, K., ve Wang, Z. (2022). Related machine scheduling with machine speeds satisfying linear constraints. Journal of Combinatorial Optimization, 44(3), 1724-1740. Doi: https://doi.org/10.1007/s10878-020-00523-1
  • Zhao, F., He, X., ve Wang, L. (2020). A two-stage cooperative evolutionary algorithm with problem-specific knowledge for energy-efficient scheduling of no-wait flow-shop problem. IEEE transactions on cybernetics, 51(11), 5291-5303. Doi: https://doi.org/10.1109/TCYB.2020.3025662

Year 2024, Volume: 35 Issue: 1, 115 - 135, 30.04.2024

Feriştah Özçelik , Tuğba Saraç , Mehmet Ertem

Abstract

References

  • Akbar, M. ve Irohara, T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production, 205, 866-883. Doi: https://doi.org/10.1016/j.jclepro.2018.09.100
  • An, Y., Li, C., Chen, X., Li, Y., Zhao, Z., ve Cao, H. (2023). An optimal energy-efficient scheduling with processing speed selection and due date constraint in a single-machine environment. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Doi: https://doi.org/10.1177/09544054231180659
  • Antoniadis, A. ve Huang, C. C. (2013). Non-preemptive speed scaling. Journal of scheduling, 16(4), 385-394. Doi: https://doi.org/10.1007/s10951-013-0312-6
  • Arriaza, O., Kim, D., Lee, D., ve Suhaimi, M. (2017). Trade-off analysis between machining time and energy consumption in impeller NC machining. Robotics and Computer-integrated Manufacturing, 43, 164-170. https://doi.org/10.1016/J.RCIM.2015.09.014
  • Behnamian, J. ve Ghomi, S. F. (2011). Hybrid flowshop scheduling with machine and resource-dependent processing times. Applied Mathematical Modelling, 35(3), 1107-1123. Doi: https://doi.org/10.1016/j.apm.2010.07.057
  • Carlucci, D., Renna, P., ve Materi, S. (2021). A Job-Shop Scheduling Decision-Making Model for Sustainable Production Planning With Power Constraint. IEEE Transactions on Engineering Management, 70(5),1923-1932. Doi: https://doi.org/10.1109/TEM.2021.3103108
  • Cota, L. P., Coelho, V. N., Guimarães, F. G., ve Souza, M. J. (2021). Bi‐criteria formulation for green scheduling with unrelated parallel machines with sequence‐dependent setup times. International Transactions in Operational Research, 28(2), 996-1017. Doi: https://doi.org/10.1111/itor.12566
  • de Athayde Prata, B., Fernandez-Viagas, V., Framinan, J. M., ve Rodrigues, C. D. (2022). Matheuristics for the flowshop scheduling problem with controllable processing times and limited resource consumption to minimize total tardiness. Computers & Operations Research, 145, 105880. Doi: https://doi.org/10.1016/j.cor.2022.105880
  • Ding, J., Schulz, S., Shen, L., Buscher, U., ve Lü, Z. (2021). Energy aware scheduling in flexible flow shops with hybrid particle swarm optimization. Computers & Operations Research, 125, 105088. Doi: https://doi.org/10.1016/j.cor.2020.105088
  • Fang, K., Uhan, N., Zhao, F., ve Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234-240. Doi: https://doi.org/10.1016/j.jmsy.2011.08.004
  • Füchtenhans, M. ve Glock, C. H. (2023). The impact of incentive-based programmes on job-shop scheduling with variable machine speeds. International Journal of Production Research, 1-19. Doi: https://doi.org/10.1080/00207543.2023.2266765
  • Jiang, T., Zhang, C., ve Sun, Q. M. (2019). Green job shop scheduling problem with discrete whale optimization algorithm. IEEE Access, 7, 43153-43166. Doi: https://doi.org/10.1109/ACCESS.2019.2908200
  • Koulamas, C. ve Kyparisis, G. J. (2022). Flow shop scheduling with two distinct job due dates. Computers & Industrial Engineering, 163, 107835. Doi: https://doi.org/10.1016/j.cie.2021.107835
  • Liu, C. H., Nanthapodej, R., ve Hsu, S. Y. (2018). Scheduling two interfering job sets on parallel machines under peak power constraint. Production Engineering, 12, 611-619. Doi: https://doi.org/10.1007/s11740-018-0840-1
  • Lu, C., Zhang, B., Gao, L., Yi, J., ve Mou, J. (2021). A knowledge-based multiobjective memetic algorithm for green job shop scheduling with variable machining speeds. IEEE Systems Journal, 16(1), 844-855. Doi: https://doi.org/10.1109/JSYST.2021.3076481
  • Luo, S., Zhang, L., ve Fan, Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production, 234, 1365-1384. Doi: https://doi.org/10.1016/j.jclepro.2019.06.151
  • Mansouri, S. A., Aktas, E., ve Besikci, U. (2016). Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788. Doi: https://doi.org/10.1016/j.ejor.2015.08.064
  • Öztop, H., Tasgetiren, M. F., Eliiyi, D. T., Pan, Q. K., ve Kandiller, L. (2020). An energy-efficient permutation flowshop scheduling problem. Expert systems with applications, 150, 113279. Doi: https://doi.org/10.1016/j.eswa.2020.113279
  • Sharma, A., Zhao, F., ve Sutherland, J. W. (2015). Econological scheduling of a manufacturing enterprise operating under a time-of-use electricity tariff. Journal of Cleaner Production, 108, 256-270. Doi: https://doi.org/10.1016/j.jclepro.2015.06.002
  • Tirkolaee, E. B., Goli, A., ve Weber, G. W. (2020). Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE transactions on fuzzy systems, 28(11), 2772-2783. Doi: https://doi.org/10.1109/TFUZZ.2020.2998174
  • Trevino-Martinez, S., Sawhney, R., ve Shylo, O. (2022). Energy-carbon footprint optimization in sequence-dependent production scheduling. Applied Energy, 315, 118949. Doi: https://doi.org/10.1016/j.apenergy.2022.118949
  • Wang, L. ve Qi, Y. (2023). Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time. CMES-Computer Modeling in Engineering & Sciences, 135(1), 325-339. Doi: https://doi.org/10.32604/cmes.2022.019730
  • Wei, Z., Liao, W., ve Zhang, L. (2022). Hybrid energy-efficient scheduling measures for flexible job-shop problem with variable machining speeds. Expert Systems with Applications, 197, 116785. Doi: https://doi.org/10.1016/j.eswa.2022.116785
  • Yin, L., Li, X., Gao, L., Lu, C., ve Zhang, Z. (2017). Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm. Advances in Mechanical Engineering, 9(4), 1687814017695959. Doi: https://doi.org/10.1177/1687814017695959
  • Yoon, H. S., Kim, E. S., Kim, M. S., Lee, J. Y., Lee, G. B., ve Ahn, S. H. (2015). Towards greener machine tools–A review on energy saving strategies and technologies. Renewable and Sustainable Energy Reviews, 48, 870-891. Doi: https://doi.org/10.1016/j.rser.2015.03.100
  • Zhang, S., Nip, K., ve Wang, Z. (2022). Related machine scheduling with machine speeds satisfying linear constraints. Journal of Combinatorial Optimization, 44(3), 1724-1740. Doi: https://doi.org/10.1007/s10878-020-00523-1
  • Zhao, F., He, X., ve Wang, L. (2020). A two-stage cooperative evolutionary algorithm with problem-specific knowledge for energy-efficient scheduling of no-wait flow-shop problem. IEEE transactions on cybernetics, 51(11), 5291-5303. Doi: https://doi.org/10.1109/TCYB.2020.3025662
There are 27 citations in total.

Details

Primary Language Turkish
Subjects Industrial Engineering
Journal Section Research Articles
Authors

Feriştah Özçelik 0000-0003-0329-203X

Tuğba Saraç 0000-0002-8115-3206

Mehmet Ertem 0000-0001-5363-3619

Early Pub Date April 21, 2024
Publication Date April 30, 2024
Acceptance Date April 8, 2024
Published in Issue Year 2024 Volume: 35 Issue: 1

Cite

APA Özçelik, F., Saraç, T., & Ertem, M. (2024). MAKİNELERDE HIZ DEĞİŞİKLİĞİNİN SÜRDÜRÜLEBİLİRLİĞE ETKİSİNİN İNCELENMESİ: BİR MATEMATİKSEL MODEL. Endüstri Mühendisliği, 35(1), 115-135.
 Download Cover Image

19736     14617            15235        15236           15240      15242