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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.
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Tom
Strony
1--9
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
autor
- Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, Thailand
autor
- Faculty of Science and Technology, Pathumwan Institute of Technology, 833 Rama 1 Wangmai Pathumwan, Bangkok 10330, Thailand
autor
- Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, Thailand
Bibliografia
- 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
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-28705890-b9f4-477d-8122-abd6df391881
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