Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem

Huthaifa Al-Khazraji

doi:10.4995/ijpme.2022.16736

Authors

Huthaifa Al-Khazraji University of Technology https://orcid.org/0000-0002-6290-3382

DOI:

https://doi.org/10.4995/ijpme.2022.16736

Keywords:

Servitization, Resource Assignment Problem, Workers Assignment Problem, Metaheuristic Optimization, Whale Optimization Algorithm, Flower Pollination Algorithm

Abstract

Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). The Workers Assignment Problem (WAP) is considered as a sub-class of RAP which aims to find an optimal assignment of workers to a number of tasks in order to optimize certain objectives. WAP is an NP-hard combinatorial optimization problem. Due to its importance, several algorithms have been developed to solve it. In this paper, it is considered that a manager is required to provide a training course to his workers in order to improve their level of skill or experience to have a sustainable competitive advantage in the industry. The training cost of each worker to perform a particular job is different. The WAP is to find the best assignment of workers to training courses such that the total training cost is minimized. Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. MATLAB Software is used to perform the simulation of the two proposed methods into WAP. The computational results for a set of randomly generated problems of various sizes show that the FPA is able to find good quality solutions.

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Author Biography

Huthaifa Al-Khazraji, University of Technology

Control and System Engineering Department

References

Abdel-Basset, M., & Shawky, L. A. (2019). Flower pollination algorithm: a comprehensive review. Artificial Intelligence Review, 52(4), 2533-2557. https://doi.org/10.1007/s10462-018-9624-4

Ammar, A., Pierreval, H., & Elkosentini, S. (2013). Workers assignment problems in manufacturing systems: A literature analysis. In Proceedings of 2013 international conference on industrial engineering and systems management (IESM) (pp. 1-7). IEEE.

Bouajaja, S., & Dridi, N. (2017). A survey on human resource allocation problem and its applications. Operational Research, 17(2), 339-369. https://doi.org/10.1007/s12351-016-0247-8

Caron, G., Hansen, P., & Jaumard, B. (1999). The assignment problem with seniority and job priority constraints. Operations Research, 47(3), 449-453. https://doi.org/10.1287/opre.47.3.449

Cattrysse, D. G., Salomon, M., & Van Wassenhove, L. N. (1994). A set partitioning heuristic for the generalized assignment problem. European Journal of Operational Research, 72(1), 167-174. https://doi.org/10.1016/0377-2217(94)90338-7

Chu, P. C., & Beasley, J. E. (1997). A genetic algorithm for the generalised assignment problem. Computers & Operations Research, 24(1), 17-23. https://doi.org/10.1016/S0305-0548(96)00032-9

Demiral, M. F. (2017). Ant Colony Optimization for a Variety of Classic Assignment Problems. In International Turkish World Engineering and Science Congress, Antalya.

Halawi, A., & Haydar, N. (2018). Effects of Training on Employee Performance: A Case Study of Bonjus and Khatib & Alami Companies. International Humanities Studies, 5(2).

Jia, Z., & Gong, L. (2008). Multi-criteria human resource allocation for optimization problems using multi-objective particle swarm optimization algorithm. In 2008 International Conference on Computer Science and Software Engineering, 1, 1187-1190. IEEE. https://doi.org/10.1109/CSSE.2008.1506

Koleva, N., & Andreev, O. (2018, June). Aspects of Training in the Field of Operations Management with Respect to Industry 4.0. In 2018 International Conference on High Technology for Sustainable Development (HiTech) (pp. 1-3). IEEE. https://doi.org/10.1109/HiTech.2018.8566581

Krokhmal, P. A., & Pardalos, P. M. (2009). Random assignment problems. European Journal of Operational Research, 194(1), 1-17. https://doi.org/10.1016/j.ejor.2007.11.062

Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2), 83-97. https://doi.org/10.1002/nav.3800020109

Lin, J. T., & Chiu, C. C. (2018). A hybrid particle swarm optimization with local search for stochastic resource allocation problem. Journal of Intelligent Manufacturing, 29(3), 481-495. https://doi.org/10.1007/s10845-015-1124-7

Mahmoud, K. I. (2009). Split Assignment With Transportation Model for Job-Shop Loading (Case Study). Journal of Engineering, 15(2).

Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008

Pentico, D. W. (2007). Assignment problems: A golden anniversary survey. European Journal of Operational Research, 176(2), 774-793. https://doi.org/10.1016/j.ejor.2005.09.014

Ross, G. T., & Soland, R. M. (1975). A branch and bound algorithm for the generalized assignment problem. Mathematical programming, 8(1), 91-103. https://doi.org/10.1007/BF01580430

Ruiz, M., Igartua, J. I., Mindeguia, M., & Orobengoa, M. (2020). Understanding and representation of organizational training programs and their evaluation. International Journal of Production Management and Engineering, 8(2), 99-109. https://doi.org/10.4995/ijpme.2020.12271

Satapathy, P., Mishra, S. P., Sahu, B. K., Debnath, M. K., & Mohanty, P. K. (2018, April). Design and implementation of whale optimization algorithm based PIDF controller for AGC problem in unified system. In International Conference on Soft Computing Systems (pp. 837-846). Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_85

Sharma, H. (2014). Importance and performance of managerial training in Indian companies-an empirical study. The Journal of Management Development, 33(2), 75-89. https://doi.org/10.1108/JMD-11-2013-0144

Suliman, A. S. A. (2019). Using ant colony algorithm to find the optimal assignment. AL-Anbar University journal of Economic and Administration Sciences, 11(25).

Ostadi, B., Taghizadeh Yazdi, M., & Mohammadi Balani, A. (2021). Process Capability Studies in an Automated Flexible Assembly Process: A Case Study in an Automotive Industry. Iranian Journal of Management Studies, 14(1), 1-37.

Walsh, B. & Volini, E. (2017). Rewriting the rules for the digital age. Deloitte University Press. New York.

Wang, Z., Li, S., Wang, Y., & Li, S. (2009, August). The research of task assignment based on ant colony algorithm. In 2009 International Conference on Mechatronics and Automation (pp. 2334-2339). IEEE.

Xuezhi, Q., & Xuehua, W. (1996). Dynamic programming model of a sort of optimal assignment problem [J]. Mathematics In Practice and Theory, 3.

Yadav, N., Banerjee, K., & Bali, V. (2020). A survey on fatigue detection of workers using machine learning. International Journal of E-Health and Medical Communications (IJEHMC), 11(3), 1-8. https://doi.org/10.4018/IJEHMC.2020070101

Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04944-6_14

Yang, X. S. (2012). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32894-7_27