Optimization of Multi-Product Aggregate Production Planning Using Improved Genetic Algorithm

Wayan F Mahmudy, Gusti E Yuliastuti, Agung M Rizki, Ishardita P Tama, Aji P Wibawa


Medium-term production planning with aggregate production planning (APP) is a crucial step in the manufacturing industry's supply chain. The essential phase determines the production size of each product over a planning horizon. Poor planning will undoubtedly directly impact the company regarding production costs and profits. The aggregate production planning is classified as NP-Hard combinatorial problem. Thus, a powerful approach is required. Most models in aggregate production planning consider a single product. This study modeled aggregate production planning to address a multi-period and multi-product. Thus, a more complex mathematical model is required. Implementing genetic algorithms (GA) may solve the problem with reasonably good solutions. This study aims to improve the GA by applying real-coded chromosomes and the adaptive change of crossover and mutation rates based on predetermined change criteria. The planning produced by the modified genetic algorithm is compared to the manufacturer's actual planning to prove the proposed approach's effectiveness. A set of computational experiments proves that adaptive evolution enables the genetic algorithm to balance its exploration and exploitation ability and obtain better solutions. The modified GA produces a less fluctuating pattern of the production amount. Even though the modified GA yields more inventory cost, the high cost of recruiting new workers can be eliminated. Using the proposed approach, the company can reduce 9 percent of the production cost.


Aggregate production planning; adaptive genetic algorithm; crossover; mutation.

Full Text:



G. E. Yuliastuti, A. M. Rizki, W. F. Mahmudy, and I. P. Tama, "Optimization of multi-product aggregate production planning using hybrid simulated annealing and adaptive genetic algorithm," International Journal of Advanced Computer Science and Applications, vol. 10, no. 11, pp. 484–489, 2019, doi: 10.14569/IJACSA.2019.0101167.

S. S. Chauhan and P. Kotecha, "Single-Level Production Planning in Petrochemical Industries Using Novel Computational Intelligence Algorithms," Modeling and Optimization in Science and Technologies, vol. 16. pp. 215–243, 2020, doi: 10.1007/978-3-030-26458-1_13.

M. Ramyar, E. Mehdizadeh, and S. M. Hadji Molana, "A Bi-objective Model to Optimize Reliability and Cost of System for the Aggregate Production Planning in a Supply Chain Network," Journal of Optimization in Industrial Engineering, vol. 1, no. 1, pp. 81–98, 2018, doi: 10.22094/joie.2018.558585.1539.

J. Goldston, "A Qualitative Study of Risk Mitigation in Enterprise Resource Planning Implementations," Global Scientific Journals, vol. 7, no. 12, pp. 1129–1159, 2019.

E. A. Oliveira, C. B. B. Costa, and M. A. da S. S. Ravagnani, "An optimization model for production planning in the drying sector of an industrial laundry," Acta Scientiarum. Technology, vol. 39, no. 1 SE-Chemical Engineering, Feb. 2017, doi: 10.4025/actascitechnol.v39i1.29797.

D. Rahmani, A. Zandi, S. Behdad, and A. Entezaminia, "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, vol. 21, no. 1, pp. 273–297, 2021, doi: 10.1007/s12351-019-00451-x.

J. Khalili and A. Alinezhad, "Performance evaluation in aggregate production planning using integrated RED-SWARA method under uncertain condition," Scientia Iranica, vol. 28, no. 2 E, pp. 912–926, 2021, doi: 10.24200/sci.2020.50202.1584.

S. M. T. Ahmed, T. Biswas, and C. Nundy, "An Optimization Model for Aggregate Production Planning and Control: A Genetic Algorithm Approach," International Journal of Research in Industrial Engineering, vol. 8, no. 3, pp. 203–224, Jan. 2019, doi: 10.22105/riej.2019.192936.1090.

A. Jamalnia, J.-B. Yang, D.-L. Xu, and A. Feili, "Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: Case study in beverage industry," Computers & Industrial Engineering, vol. 114, pp. 54–68, 2017, doi: https://doi.org/10.1016/j.cie.2017.09.044.

B. Zhu, J. Hui, F. Zhang, and L. He, "An Interval Programming Approach for Multi-period and Multi-product Aggregate Production Planning by Considering the Decision Maker's Preference," International Journal of Fuzzy Systems, vol. 20, no. 3, pp. 1015–1026, 2018, doi: 10.1007/s40815-017-0341-y.

D. H. Tuan and N. Chiadamrong, "Solving an aggregate production planning problem by using interactive fuzzy linear programming," Asia-Pacific Journal of Science and Technology, vol. 26, no. 1, 2021, doi: 10.14456/apst.2021.5.

Y. Chauhan, V. Aggarwal, and P. Kumar, "Application of FMOMILP for aggregate production planning: A case of multi-product and multi-period production model," in 2017 International Conference on Advances in Mechanical, Industrial, Automation and Management Systems (AMIAMS), 2017, pp. 266–271, doi: 10.1109/AMIAMS.2017.8069222.

R. Khemiri, K. Elbedoui-Maktouf, B. Grabot, and B. Zouari, "Integrating fuzzy TOPSIS and goal programming for multiple objective integrated procurement-production planning," in the 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2017, pp. 1–6.

A. M. Rizki, G. E. Yuliastuti, W. F. Mahmudy, and I. P. Tama, "Variable Neighborhoods Search for Multi-Site Production Planning," Journal of Information Technology and Computer Science, vol. 3, no. 2, pp. 169–174, 2018, [Online]. Available: http://jitecs.ub.ac.id/index.php/jitecs/article/view/65.

A. A. Zaidan, B. Atiya, M. R. Abu Bakar, and B. B. Zaidan, "A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment," Neural Computing and Applications, vol. 31, no. 6, pp. 1823–1834, 2019, doi: 10.1007/s00521-017-3159-5.

B. Almada-Lobo, J. F. Oliveira, and M. A. Carravilla, "Production planning and scheduling in the glass container industry: A VNS approach," International Journal of Production Economics, vol. 114, no. 1, pp. 363–375, 2008, doi: https://doi.org/10.1016/j.ijpe.2007.02.052.

S. Santosa, R. A. Pramunendar, D. P. Prabowo, and Y. P. Santosa, "Wood Types Classification using Back-Propagation Neural Network based on Genetic Algorithm with Gray Level Co-occurrence Matrix for Features Extraction," International Journal of Computer Science, vol. 46, no. 2, pp. 149–155, 2019.

Z. Zakaria, S. Deris, M. R. Othman, and S. Kasim, "Non-Reshuffle-Based Approach for Rescheduling of Flexible Manufacturing System," International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4-2, pp. 1543–1552, 2017, doi: 10.18517/ijaseit.7.4-2.3464.

Y. Wang and Q. Shi, "Multi-objective Robust Optimization Model for Spare Parts Supply in Wartime," Engineering Letters, vol. 27, no. 4, pp. 794–801, 2019.

A. Rahmi, W. F. Mahmudy, and M. Z. Sarwani, "Genetic Algorithms for Optimization of Multi-Level Product Distribution ," International Journal of Artificial Intelligence, vol. 18, no. 1, pp. 135–147, 2020, [Online]. Available: http://www.ceser.in/ceserp/index.php/ijai/article/view/6382.

G. Ettaye, A. El Barkany, and A. El Khalfi, "Applying genetic algorithm for integrated planning of production and maintenance," in 2017 International Colloquium on Logistics and Supply Chain Management: Competitiveness and Innovation in Automobile and Aeronautics Industries, LOGISTIQUA 2017, 2017, pp. 166–170, doi: 10.1109/LOGISTIQUA.2017.7962892.

W. F. Mahmudy, M. Z. Sarwani, A. Rahmi, and A. W. Widodo, "Optimization of Multi-Stage Distribution Process Using Improved Genetic Algorithm," International Journal of Intelligent Engineering and Systems, vol. 14, no. 2, pp. 211–219, 2021, [Online]. Available: http://www.inass.org/2021/2021043019.pdf.

A. J. Delima, A. Sison, and R. Medina, "A modified genetic algorithm with a new crossover mating scheme," Indonesian Journal of Electrical Engineering and Informatics, vol. 7, pp. 165–181, Jun. 2019, doi: 10.11591/ijeei.v7i2.1047.

A. Rostamian, S. Jamshidi, and E. Zirbes, "The development of a novel multi-objective optimization framework for non-vertical well placement based on a modified non-dominated sorting genetic algorithm-II," Computational Geosciences, vol. 23, no. 5, pp. 1065–1085, 2019, doi: 10.1007/s10596-019-09863-2.

D. Jude Hemanth and J. Anitha, "Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images," Applied Soft Computing, vol. 75, pp. 21–28, 2019, doi: https://doi.org/10.1016/j.asoc.2018.10.054.

V. Kralev, "Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem," International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 3, pp. 762–770, 2018, doi: 10.18517/ijaseit.8.3.4867.

W. Wen-jing, "Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities," International Journal of Emerging Technologies in Learning (iJET); Vol 13, No 06 (2018), May 2018, [Online]. Available: https://online-journals.org/index.php/i-jet/article/view/8442/4990.

X. Zhou, F. Miao, and H. Ma, "Genetic Algorithm with an Improved Initial Population Technique for Automatic Clustering of Low-Dimensional Data," Information, vol. 9, no. 4, pp. 1–22, 2018.

A. Iranmanesh and H. R. Naji, "DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing," Cluster Computing, vol. 24, no. 2, pp. 667–681, 2021, doi: 10.1007/s10586-020-03145-8.

J. B. C. Chagas, J. Blank, M. Wagner, M. J. F. Souza, and K. Deb, "A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem," Journal of Heuristics, vol. 27, no. 3, pp. 267–301, 2021, doi: 10.1007/s10732-020-09457-7.

A. Rahmi, W. F. Mahmudy, and S. Anam, "A crossover in simulated annealing for population initialization of genetic algorithm to optimize the distribution cost," Journal of Telecommunication, Electronic and Computer Engineering, vol. 9, no. 2–8, pp. 177–182, 2017.

I. Kholidasari, L. Setiawati, and Tartila, "The implementation of forecasting method by incorporating human judgment," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, pp. 1982–1988, 2019, doi: 10.18517/ijaseit.9.6.10640.

W. Jiang and L. Wu, "Flow shop optimization of hybrid make-to-order and make-to-stock in precast concrete component production," Journal of Cleaner Production, vol. 297, 2021, doi: 10.1016/j.jclepro.2021.126708.

A. K. Ariyani, W. F. Mahmudy, and Y. P. Anggodo, "Hybrid genetic algorithms and simulated annealing for multi-trip vehicle routing problem with time windows," International Journal of Electrical and Computer Engineering, vol. 8, no. 6, 2018, doi: 10.11591/ijece.v8i6.pp.4713-4723.

C. So, I.-M. Ho, J.-S. Chae, and K.-H. Hong, "PWR core loading pattern optimization with adaptive genetic algorithm," Annals of Nuclear Energy, vol. 159, p. 108331, 2021, doi: https://doi.org/10.1016/j.anucene.2021.108331.

DOI: http://dx.doi.org/10.18517/ijaseit.12.6.15177


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development