Model Scheduling Optimization Workforce Management Marketing
This research focuses on completing workforce management marketing scheduling using genetic metaheuristic methods. Optimal scheduling to determine the duration of a job, the quality of employees, and meet the challenges to increase work scheduling targets according to marketing employee skills by maximizing operational time while providing satisfactory services to customers. Marketing employees increasingly rely on the right time to connect with their customers. The problem in this study considers marketing skills and activities to carry out work activities, namely promotion, follow-up, and stand-by in the office with the limitations of the existing workforce. This problem has the nature of NP-Hard so a quick solution requires the use of metaheuristic methods. The metaheuristic method was built in 11 constraints. The results show that the genetic metaheuristic method is capable of producing far better results. Therefore, employee scheduling is very important. This study aims to develop an optimization model for employee scheduling and to maximize operational time work. With this model, it is expected to achieve optimal scheduling for marketing management workforce.
Pinedo, M., Zacharias, C., &Zhu, N. (2015). Scheduling in the service industries: An overview. Journal of systems science and systems engineering, 24(1), 1-48.
Molnar, G., Jakobović, D., & Pavelić, M. (2016, March). Workforce Scheduling in Inbound Customer Call Centres with a Case Study. In European Conference on the Applications of Evolutionary Computation(pp. 831-846). Springer, Cham.
Reid, K. N., Li, J., Swan, J., McCormick, A., & Owusu, G. (2016, December). Variable neighbourhood search: A case study for a highly-constrained workforce scheduling problem. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI)(pp. 1-6). IEEE.
Zabihi, S., Kahag, M. R., Maghsoudlou, H., & Afshar-Nadjafi, B. (2019). Multi-objective teaching-learning-based meta-heuristic algorithms to solve multi-skilled project scheduling problem. Computers & Industrial Engineering, 136, 195-211.
Seada, A. A., & Eltawil, A. B. (2015, March). Modeling and analysis of workforce management decisions in modern call centers. In 2015 International Conference on Industrial Engineering and Operations Management (IEOM)(pp. 1-10). IEEE.
Ang, B. Y., Lam, S. W. S., Pasupathy, Y., & Ong, M. E. H. (2018). Nurse workforce scheduling in the emergency department: A sequential decision supportsystem considering multiple objectives. Journal of nursing management, 26(4), 432-441.
Gans, N., Shen, H., Zhou, Y. P., Korolev, N., McCord, A., & Ristock, H. (2015). Parametric forecasting and stochastic programming models for call-center workforce scheduling. Manufacturing & Service Operations Management, 17(4), 571-588.
Reid, K. N.,Li, J., Veerapen, N., Swan, J., McCormick, A., Kern, M., & Owusu, G. (2018, September). Shift Scheduling and Employee Rostering: An Evolutionary Ruin & Stochastic Recreate Solution. In 2018 10th Computer Science and Electronic Engineering (CEEC)(pp. 19-23). IEEE.
Moschakis, I. A., & Karatza, H. D. (2015). A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs. Simulation Modelling Practice and Theory, 57, 1-25.
Diveev, A. I., & Bobr, O. V. (2017). Variational genetic algorithm for np-hard scheduling problem solution. Procedia Computer Science, 103, 52-58.
Shishido, H. Y., Estrella, J. C., Toledo, C. F. M., & Arantes, M. S. (2018). Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds. Computers & Electrical Engineering, 69, 378-394.
Torres-Jiménez, J., & Pavón, J. (2014). Applications of metaheuristics in real-life problems.
Rahardja, U., Hariguna, T., & Baihaqi, W.M. (2019). OPINION MINING ON E-COMMERCE DATA USING SENTIMENT ANALYSIS AND K-MEDOID CLUSTERING. 2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media), 168-170.
Aini, Q., Rahardja, U., & Hariguna, T.(2019). The antecedent of perceived value to determine of student Continuance Intention and student Participate Adoption of iLearning. The Fifth Information Systems International Conference 2019, 1-8. Rahardja, U. (2009, May). Artificial informatics. In 2009 4th IEEE Conference on Industrial Electronics and Applications (pp. 3064-3067). IEEE.