Efficient Scheduling of Plantation Company Workers using Genetic Algorithm

Wayan Firdaus Mahmudy, Andreas Pardede, Agus Wahyu Widodo, Muh Arif Rahman


Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually. 

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H. Kikuchi et al., “Association of overtime work hours with various stress responses in 59,021 Japanese workers: Retrospective cross-sectional study,” PLoS One, vol. 15, no. 3, p. e0229506, Mar. 2020, [Online]. Available: https://doi.org/10.1371/journal.pone.0229506.

K. Bhui, S. Dinos, M. Galant-Miecznikowska, B. de Jongh, and S. Stansfeld, “Perceptions of work stress causes and effective interventions in employees working in public, private and non-governmental organisations: a qualitative study,” BJPsych Bull., vol. 40, no. 6, pp. 318–325, Dec. 2016, doi: 10.1192/pb.bp.115.050823.

Z. Jia, J. Yan, J. Y. T. Leung, K. Li, and H. Chen, “Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities,” Appl. Soft Comput., vol. 75, pp. 548–561, 2019, doi: https://doi.org/10.1016/j.asoc.2018.11.027.

L. Wang, J. Cai, M. Li, and Z. Liu, “Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization,” Sci. Program., vol. 2017, p. 9016303, 2017, doi: 10.1155/2017/9016303.

C. Gallo and V. Capozzi, “A Simulated Annealing Algorithm for Scheduling Problems,” J. Appl. Math. Phys., vol. 7, Oct. 2019, doi: 10.4236/jamp.2019.simann.

F. Chahyadi, A. Azhari, and H. Kurniawan, “Hospital Nurse Scheduling Optimization Using Simulated Annealing and Probabilistic Cooling Scheme,” Indones. J. Comput. Cybern. Syst., vol. 12, no. 1, pp. 21–32, 2018.

A. Dabah, A. Bendjoudi, and A. AitZai, “An efficient Tabu Search neighborhood based on reconstruction strategy to solve the blocking job shop scheduling problem,” J. Ind. Manag. Optim., vol. 13, no. 4, pp. 2015–2031, 2017, doi: 10.3934/jimo.2017029.

A. I. Awad, N. A. El-Hefnawy, and H. M. Abdel_kader, “Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments,” Procedia Comput. Sci., vol. 65, pp. 920–929, 2015, doi: https://doi.org/10.1016/j.procs.2015.09.064.

H. Jiang, J. Liu, H.-W. Cheng, and Y. Zhang, “Particle swarm optimization based space debris surveillance network scheduling,” Res. Astron. Astrophys., vol. 17, no. 3, p. 30, 2017, doi: 10.1088/1674-4527/17/3/30.

S. Thevenin and N. Zufferey, “Learning Variable Neighborhood Search for a scheduling problem with time windows and rejections,” Discret. Appl. Math., vol. 261, pp. 344–353, 2019, doi: https://doi.org/10.1016/j.dam.2018.03.019.

W. Jomaa, M. Eddaly, and B. Jarboui, “Variable neighborhood search algorithms for the permutation flowshop scheduling problem with the preventive maintenance,” Oper. Res., 2019, doi: 10.1007/s12351-019-00507-y.

M. Samà, A. D׳Ariano, F. Corman, and D. Pacciarelli, “A variable neighbourhood search for fast train scheduling and routing during disturbed railway traffic situations,” Comput. Oper. Res., vol. 78, pp. 480–499, 2017, doi: https://doi.org/10.1016/j.cor.2016.02.008.

R. Rody, W. F. Mahmudy, and I. P. Tama, “Using Guided Initial Chromosome of Genetic Algorithm for Scheduling Production-Distribution System,” J. Inf. Technol. Comput. Sci., vol. 4, no. 1, pp. 26–32, 2019, [Online]. Available: http://jitecs.ub.ac.id/index.php/jitecs/article/view/95.

M. L. Seisarrina, I. Cholissodin, and H. Nurwarsito, “Invigilator Examination Scheduling using Partial Random Injection and Adaptive Time Variant Genetic Algorithm,” J. Inf. Technol. Comput. Sci., vol. 3, no. 2, pp. 113–119, 2018.

H. Algethami, R. L. Pinheiro, and D. Landa-Silva, “A genetic algorithm for a workforce scheduling and routing problem,” in 2016 IEEE Congress on Evolutionary Computation (CEC), 2016, pp. 927–934.

V. Meilia, B. D. Setiawan, and N. Santoso, “Extreme Learning Machine Weights Optimization Using Genetic Algorithm In Electrical Load Forecasting,” J. Inf. Technol. Comput. Sci., vol. 3, no. 1, pp. 77–87, 2018.

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

V. N. Wijayaningrum and W. F. Mahmudy, “Optimization of ship’s route scheduling using genetic algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 2, no. 1, 2016, doi: 10.11591/ijeecs.v2.i1.pp180-186.

L. R. Abreu, J. O. Cunha, B. A. Prata, and J. M. Framinan, “A genetic algorithm for scheduling open shops with sequence-dependent setup times,” Comput. Oper. Res., vol. 113, p. 104793, 2020, doi: https://doi.org/10.1016/j.cor.2019.104793.

W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system,” 2013, doi: 10.1109/CyberneticsCom.2013.6865795.

M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization. New York: John Wiley & Sons, Inc., 2000.

B. F. Rosa, M. J. F. Souza, S. R. de Souza, M. F. de França Filho, Z. Ales, and P. Y. P. Michelon, “Algorithms for job scheduling problems with distinct time windows and general earliness/tardiness penalties,” Comput. Oper. Res., vol. 81, pp. 203–215, 2017, doi: https://doi.org/10.1016/j.cor.2016.12.024.

DOI: http://dx.doi.org/10.17977/um018v3i22020p60-66


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