İşgücü Yönetiminde Sezgiseller: Geliştirilmiş Deve Algoritmasının Bir Uygulaması

Year 2024, Volume: 11 Issue: 1, 37 - 48, 30.06.2024

Mehmet Fatih Demiral

https://doi.org/10.34232/pjess.1498652

Abstract

Geliştirilmiş Deve Algoritması (MCA), 2016, 2019 ve 2021 yıllarında mühendislik problemlerine uygulanan zorlu bir algoritmadır. MCA, diğer tekniklere kıyasla işletme, ekonomi, işgücü yönetimi ve bilim alanlarına uygulanabilir. Saf MCA, literatürdeki optimizasyon problemlerini etkili ve oldukça hızlı çözer. Geliştirilmiş deve algoritmasını kullanarak iş gücü yönetiminde matematiksel modeli geliştirmek ve uygulamak için bu algoritma, tur oluşturma yöntemi (MC) gibi popüler buluşsal yöntemle birleştirildi ve daha sonra örneğin 2-opt, 3-opt ve k-opt gibi yerel aramalarla iyileştirildi. Önerilen hibrit algoritmalar uygun parametreler altında test edilmiştir. Deneysel çalışmada düzgün dağılım sağlanarak rastgele model veri setleri ve uygun parametreler kullanılmıştır. Deneysel sonuçlar örnek verisetleri ve uygun parametreler için en iyi, ortalama, standart sapma ve CPU zamanı olarak verilmiştir. Özet olarak, önerilen hibrit meta-sezgiseller, örnek rastgele veri kümeleri için kabul edilebilir CPU zamanında makul iş gücü yönetimi çözümleri bulmaktadır.

Keywords

Atama problemi, Geliştirilmiş Deve Algoritması, İşgücü Yönetimi, Metasezgiseller, Sezgiseller

Ethical Statement

Bu araştırma etik kurallar çerçevesince hazırlanmıştır.

Heuristics in Labor Management: An Application of Modified Camel Algorithm

Abstract

Modified Camel Algorithm (MCA) is a challenging algorithm applied to engineering problems in 2016, 2019, and 2021. MCA can be implemented to the field of business, economics, labor management, and science compared to the other techniques. The pure MCA solves optimization problems effectively and quite fast in literature. To develop and apply the mathematical model in labor management using the modified camel algorithm, it was combined with popular heuristics, such as constructive heuristic (MC), and then improved with local searches, for instance 2-opt, 3-opt, and k-opt. The suggested hybrid algorithms are tested under proper parameters. In the experimental study, random model datasets, and suitable parameters are used via uniform distribution. The experimental outcomes are given as best, average, std. deviation and CPU time for sample datasets with proper parameters. In short, the suggested hybrid metaheuristics find reasonable solutions of labor management in acceptable CPU time for all random datasets.

Keywords

Assignment problem, Heuristics, Labor Management, Metaheuristics, Modified Camel algorithm

References

• Ali, R.S., Alnahwi, F.M., & Abdullah, A.S. (2019). A modified camel travelling behavior algor- ithm for engineering applications. Australian Journal of Electrical and Electronics Engineering, 16(3), 176-186. doi: 10.1080/1448837X.2-019.1640010
• Bouajaja, S., & Dridi, N. (2017). A survey on human resource allocation problem and its applic- ations. Operational Research, 17(2), 339-369.
• Bozorgi, S.M., & Yazdani, S. (2019). IWOA: An improved whale optimization algorithm for optimization problems. Journal of Computational Design and Engineering, 6(3), 243–259.
• Caron, G., Hansen, P. & Jaumard, D. (1999). The assignment problem with seniority and job priority constraints. Operations Research, 47(3), 449-454.
• Demiral, M. F. (2022). Application of a Hybrid Camel Traveling Behavior Algorithm for Trave- ling Salesman Problem. Dokuz Eylul University Journal of Science and Engineering, 24(72), 725-735. doi: 10.21205/deufmd.2022247204
• Demiral, M. F. (2024). Perspective Chapter: Computational Analysis of Camel Algorithm with Heuristics in Assignment Problem. In: M.S. Cengiz (Ed.) New Studies in Engineering (pp. 4-18). Duvar Yayınları, Izmir Feng, X., Liu, Y., Yu, H., & Luo, F. (2019). Physarum-energy optimization algorithm. Soft Computing, 23, 871–888. doi: 10.1007/s00500-017-2796-z.
• Geng, X., Chen, Z., Yang, W., Shi, D., & Zhao, K. (2011) Solving the travelling salesman problem based on an adaptive simulated annealing algorithm with greedy search. Applied Soft Computing, 11(4), 3680-3689. Gilbert, K.C., & Hofstra, R.B. (1988). Multidimensional assignment problems. Decision Scienc- es, 19(2), 306-321.
• Hassan, K.H., Abdulmuttalib, T.R., & Jasim, B.H. (2021). Parameters estimation of solar Photo- voltaic module using camel behavior search algorithm. International Journal of Electrical Computer Engineering (IJECE), 11(1), 788-793.
• Hatamlou, A (2013). Black hole: a new heuristic optimization approach for data clustering. Information Sciences, 222, 175–184
• Hatamlou, A. (2018). Solving travelling salesman problem using black hole algorithm. Soft Co- mputing, 22(24), 8167-8175. doi: 10.1007/s00500-017-2760-y
• Ibrahim, M.K., & Ali, R.S. (2016). Novel optimization algorithm inspired by camel traveling b- Ehavior. Iraqi Journal for Electrical and Electronic Engineering, 12(2), 167-177.
• Lin, Y., Bian, Z., & Liu, X. (2016). Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing-tabu search algorithm to solve the symmetrical traveling salesman problem. Applied Soft Computing, 49, 937-952. Liu, L., Song, Y., Zhang, H., Huadong, M., & Vasilakos, A.V. (2015). Physarum optimization: a biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 818–831.
• Mian, T.A., Muhammad, U., & Riaz, A. (2012). Jobs scheduling and worker assignment proble- m to minimize makespan using ant colony optimization metaheuristic. World Academy of Science, Engineering and Technology, 6(12), 2823-2826.
• Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Systems Software, 95, 51-67. Nowak, M., Epelman, M. & Pollock, S.M. (2006). Assignment of swimmers to dual meet events. Computers & Operations Research, 33, 1951-1962.
• Pentico, D.W. (2007). Assignment problems: a golden anniversary survey. European Journal of Operational Research, 176(2), 774-793.
• Rajabioun, R. (2011) Cuckoo optimization algorithm. Applied Soft Computing, 11(8), 5508–5518
• Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshoper optimization algorithm: theory and application. Advances in Engineering Software, 105, 30-47.
• Szeto, W.Y., Yongzhong, W., & Ho, S.C. (2011). An artificial bee colony algorithm for the capac- itated vehicle routing problem. European Journal of Operational Research, 215(1): 126-135.
• Tawhid, M.A., & Savsani P. (2019) Discrete sine-cosine algorithm (DSCA) with local search for solving traveling salesman problem. Arabian Journal for Science and Engineering, 44, 3669-3679. https://doi.org/10.1007/s13369-018-3617-0
• Utama, D.M., Safitri, W. O. N., & Garside, A. K. (2022). A Modified Camel Algorithm for Opti- mizing Green Vehicle Routing Problem with Time Windows. Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri, 24(1), 23-36.
• Yang, XS (2010a). Firefly algorithm, lévy flights and global optimization. In: M. Bramer, R. Ellis & M. Petridis (Eds.) Research and Development in Intelligent Systems XXVI (pp.209-218). Springer, London
• Yang, XS (2010b). A new metaheuristic bat-inspired algorithm. In: J.R. González, D.A. Pelta, C. Cruz, G. Terrazas & N. Krasnogor (Eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 65-74). Studies in Computational Intelligence 284. Springer, Heidelberg.
• Yildirim, A.E., & Karci, A. (2018). Applications of artificial atom algorithm to small-scale traveling salesman problems. Soft Computing, 22(22), 7619-7631. https://doi.org/10.1007/s00500-017-2735-z