The Hybrid Ant Lion and Grey Wolf Algorithm (HALGW) for Cashew Nuts Production Plan with Split Demand

Authors

DOI:

https://doi.org/10.24425/mper.2024.151482

Abstract

The novel concept of split demand is introduced based on the dynamic single-level lotsizing (DSLLS), called the DSLLS-split demand model. The hybrid algorithm based on the combination between Ant Lion Optimization (ALO) and Gray Wolf Optimization (GWO), called the HALGW algorithm is proposed in this study. The suitable cashew nut production planning is examined with the DSLLS-split demand model and the HALGW algorithm. Four monthly datasets including demand, production quantity, production cost and holding cost are collected from January 2020 to December 2020. Two main concepts with split demand and without split demand are compared with three different algorithms: ALO, GWO and HALGW. The results found that the HALGW algorithm with the concept of split demand provides the minimum cost, 507,910.11 baht with lowest RMSE value, 106.08 and lowest MAPE value, 0.0000115. Hence, this method may help the community enterprise in Tha Pla, Uttaradit, Thailand to manage their processes, efficiently.

References

Avelina, A.-R., Erik, C., Alma, R., Abraham, M., & Elias, O.-B. (2020). An Improved GreyWolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem. Mathematics, 8 (8), 1–24. DOI: 10.3390/math8091457

Bo, Y., & Zihui, L. (2020). Thermal error modeling by integrating GWO and ANFIS algorithms for the gear hobbing machine. The International Journal of Advanced Manufacturing Technology, 109, 2441–2456. DOI: 10.1007/s00170-020-05791-z

Botchkarev, A. (2019). A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdisciplinary Journal of Information, Knowledge, and Management, 14, 45–79.

Chengzhi, Q., Wendong, G., Jing, Z., & Maiying, Z. (2020). A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning. Knowledge-Based Systems, 1–14. DOI: 10.1016/j.knosys.2020.105530

Chung-Yuan, D., & Liang-Yuh, O. (2011). A particle swarm optimization for solving joint pricing and lotsizing problem with fluctuating demand and trade credit financing. Computers & Industrial Engineering, 127–137. DOI: 10.1016/j.cie.2010.10.010

Duong, T.L., Nguyen, N.A., & Nguyen, T.T. (2021). Application of meta-Heuristic algorithm for finding the best solution for the optimal power flow problem. International Journal of Intelligent Engineering and Systems, 14 (5), 528–538. DOI: 10.22266/ijies2021.1231.47

EL, S., EL, K., & Marwa, E. (2020). Hybrid gray wolf and particle swarm optimization for feature selection. International Journal of Innovative Computing, Information and Control, 831–844. DOI: 10.24507/ijicic.16.03.831

Karimi, B., Ghomia, S.F., & Wilson, J.M. (2003). The capacitated lot sizing problem: a review of models and algorithms. The International Journal of Management Science, 31, 365–378. DOI: 10.1016/S0305-0483(03)00059-8

Khalilpourazari, S., & Pasandideh, S.H.R. (2019). Modeling and optimization of multi-item multiconstrained EOQ model for growing items. Knowledge-Based Systems, 164, 150–162. DOI: 10.1016/j.knosys.2018.10.032

Kuter, S. (2021). Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression. Remote Sensing of Environment, 255, 112294. DOI: 10.1016/j.rse.2021.112294

Luis, G., Diego, K., & Bernardo, L.A. (2014). Modeling lot sizing and scheduling problems with sequence dependent setups. European Journal of Operational Research, 239 (2), 644–662. DOI: 10.1016/j.ejor.2014.05.018

Maity, G., Roy, S.K., & Verdegay, J.L. (2016). Multiobjective transportation problem with cost reliability under uncertain environment. International Journal of Computational Intelligence Systems, 9(4), 839. DOI: 10.1080/18756891.2016.1237184

Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.

Mirjalili, S., Mirjalili, S.M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI: 10.1016/j.advengsoft.2013.12.007

Mohammad Reza, M.S., Amir, A., & Babak, S. (2008). Inventory lot-sizing with supplier selection using hybrid intelligent algorithm. Applied Soft Computing, 1523–1529. DOI: 10.1016/j.asoc.2007.11.001

Onanaye, A.S., & Oyebode, D.O. (2019). Cost implication of inventory management in organised systems. International Journal of Engineering and Management Research, 9 (1), 115–126. DOI: 10.31033/ijemr.9.1.11

Ozmen, A., Weber, G.W., Batmaz, İ., & Kropat, E. (2011). RCMARS: robustification of CMARS with different scenarios under polyhedral uncertainty set. Communications in Nonlinear Science and Numerical Simulation, 16(11), 4780–4787. DOI: 10.1016/j.cnsns.2011.04.001

Phromfaiy, A., Wangsoh, W., & Surin, P. (2022). An optimal production plan for cashew nuts community enterprise using metaheuristic algorithms. International Journal of Applied Metaheuristic Computing, 13 (1), 1–23. DOI: 10.4018/IJAMC.292514

Precup, R.-E., David, R.-C., Szedlak-Stinean, A.-I., Petriu, E.M., & Dragan, F. (2017). An easily understandable grey wolf optimizer and its application to fuzzy controller tuning. Algorithms, 10 (1), 1–15. DOI: 10.3390/a10020068

Singh, N., & Singh, S.B. (2017a). Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance. Journal of Applied Mathematics, 1–16. DOI: 10.1155/ 2017/2030489

Singh, N., & Singh, S.B. (2017b). A novel hybrid GWOSCA approach for optimization problems. Engineering Science and Technology, an International Journal, 1-16. DOI: 10.1016/j.jestch.2017.11.001

Tian, T., Changyu, L., Qi, G., Yi, Y., Wei, L., & Qiurong, Y. (2018). An improved ant lion optimization algorithm and its application in hydraulic turbine governing system parameter identification. Energies, 11 (1), 1–15.

Wei, Q., Zilong, Z., Yang, L., & Ou, T. (2019). A two-stage ant colony algorithm for hybrid flow shop scheduling with lot sizing and calendar constraints in printed circuit board assembly. Computers & Industrial Engineering, 138, 1–12.

Xiao, Y., You, M., Zuo, X., Zhou, S., & Pan, X. (2018). The uncapacitatied dynamic single-level lot-sizing problem under a time-varying environment and an exact solution approach. Sustainability, 1–14. DOI: 10.3390/su10113867

Zheng-Ming, G., & Juan, Z. (2019). An improved grey wolf optimization algorithm with variable weights. Computational Intelligence and Neuroscience, 2019, 1–13. DOI: 10.1155/2019/2981282

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Published

2024-09-30

How to Cite

Phromfaiy, Apisak, et al. “The Hybrid Ant Lion and Grey Wolf Algorithm (HALGW) for Cashew Nuts Production Plan With Split Demand”. Management and Production Engineering Review, vol. 15, no. 3, Sept. 2024, pp. 1-9, doi:10.24425/mper.2024.151482.

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