Learning to Optimize Meter Reading Routes in Billing Management Problems in the Balata Regional Unit

  • Gatot Mochamad Muchtar Institut Teknologi Bandung
  • Rinovia Mery Garnierita Simanjuntak Institut Teknologi Bandung
Keywords: Meter Reading Route, Traveling Salesman Problem, Optimization

Abstract

Meter reading route optimization is a significant challenge in the billing management of the State Electricity Company (PLN). This study evaluates the potential of the Traveling Salesman Problem (TSP) in overcoming this problem. By applying TSP to the Balata Region case study, this study aims to find a more efficient and effective route solution. The results of the study are expected to reduce operational costs, increase officer productivity, and increase customer satisfaction. It is hoped that TSP can be an attractive alternative for electricity companies in optimizing the meter reading process.

References

Aranski, A. W. (2022). Optimization Of The Smallest Road Using The Traveling Salesman Problem (Tsp) Method. IJISTECH (International Journal of Information System and Technology), 6(1), 159-166. https://doi.org/10.30645/ijistech.v6i1.224

Beinert, D., Holzhüter, C., Thomas, J. M., & Vogt, S. (2023). Power flow forecasts at transmission grid nodes using graph neural networks. Energy and AI, 14, 100262. http://dx.doi.org/10.1016/j.egyai.2023.100262

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. http://dx.doi.org/10.1023/A:1010950718922

Brody, S., Alon, U., & Yahav, E. (2021). How attentive are graph attention networks? ArXiv Preprint ArXiv:2105.14491. http://dx.doi.org/10.48550/arXiv.2105.14491

Chen, Z., Amani, A. M., Yu, X., & Jalili, M. (2023). Control and optimisation of power grids using smart meter data: A review. Sensors, 23(4), 2118. https://doi.org/10.3390/s23042118

Cortes, C. (1995). Support-Vector Networks. Machine Learning.

Das, R., & Soylu, M. (2023). A key review on graph data science: The power of graphs in scientific studies. Chemometrics and Intelligent Laboratory Systems, 240, 104896. https://doi.org/10.1016/j.chemolab.2023.104896

Gallegos, J., Arévalo, P., Montaleza, C., & Jurado, F. (2024). Sustainable electrification—advances and challenges in electrical-distribution networks: a review. Sustainability, 16(2), 698. https://doi.org/10.3390/su16020698

Hlaing, Z. C. S. S., & Khine, M. A. (2011). Solving traveling salesman problem by using improved ant colony optimization algorithm. International Journal of Information and Education Technology, 1(5), 404-409. http://dx.doi.org/10.7763/IJIET.2011.V1.67

Kovács, L., & Jlidi, A. (2024). Neural Networks for Vehicle Routing Problem. ArXiv Preprint ArXiv:2409.11290. http://dx.doi.org/10.32971/als.2024.014

Lischka, A., Wu, J., Chehreghani, M. H., & Kulcsár, B. (2024). A GREAT Architecture for Edge-Based Graph Problems Like TSP. ArXiv Preprint ArXiv:2408.16717. http://dx.doi.org/10.48550/arXiv.2408.16717

Mzili, T., Riffi, M. E., Mzili, I., & Dhiman, G. (2022). A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem. Decision making: applications in management and engineering, 5(2), 287-299. http://dx.doi.org/10.31181/dmame0318062022m

Natras, R., Soja, B., & Schmidt, M. (2022). Ensemble machine learning of random forest, AdaBoost and XGBoost for vertical total electron content forecasting. Remote Sensing, 14(15), 3547. https://doi.org/10.3390/rs14153547

Nemani, R., Cherukuri, N., Rao, G. R. K., Srinivas, P. V. V. S., Pujari, J. J., & Prasad, C. (2021, November). Algorithms and optimization techniques for solving tsp. In 2021 Fifth international conference on I-SMAC (IoT in social, mobile, analytics and Cloud)(I-SMAC) (pp. 809-814). IEEE. http://dx.doi.org/10.1109/I-SMAC52330.2021.9640907

Peng, J., Kimmig, A., Wang, D., Niu, Z., Liu, X., Tao, X., & Ovtcharova, J. (2024). Energy consumption forecasting based on spatio-temporal behavioral analysis for demand-side management. Applied Energy, 374, 124027. https://doi.org/10.1016/j.apenergy.2024.124027

Pop, P. C., Cosma, O., Sabo, C., & Sitar, C. P. (2024). A comprehensive survey on the generalized traveling salesman problem. European Journal of Operational Research, 314(3), 819-835. https://doi.org/10.1016/j.ejor.2023.07.022

Punnoose, R., & Xlri -Xavier, C. (2016). Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting. In IJARAI) International Journal of Advanced Research in Artificial Intelligence (Vol. 5, Issue 9). www.ijarai.thesai.org

Rondano, F. (2025). Heuristic Algorithms for the Min-Max Traveling Salesman Problem (Doctoral dissertation, Politecnico di Torino).

Şahinbaş, K. (2022). Employee promotion prediction by using machine learning algorithms for imbalanced dataset. 2022 2nd International Conference on Computing and Machine Intelligence (ICMI), 1–5. http://dx.doi.org/10.1109/ICMI55296.2022.9873744

Vassallo, M., Leerschool, A., Bahmanyar, A., Duchesne, L., Gerard, S., Wehenkel, T., & Ernst, D. (2024). An Optimization Algorithm for Customer Topological Paths Identification in Electrical Distribution Networks. arXiv preprint arXiv:2409.09073. https://doi.org/10.48550/arXiv.2409.09073

Zafeiropoulou, M., Mentis, I., Sijakovic, N., Terzic, A., Fotis, G., Maris, T. I., ... & Ekonomou, L. (2022). Forecasting transmission and distribution system flexibility needs for severe weather condition resilience and outage management. Applied Sciences, 12(14), 7334. https://doi.org/10.3390/app12147334

Zhang, Z., & Yang, J. (2022). A discrete cuckoo search algorithm for traveling salesman problem and its application in cutting path optimization. Computers & Industrial Engineering, 169, 108157. http://dx.doi.org/10.1504/IJCI.2014.064853

Published
2025-05-16
How to Cite
Muchtar, G. M., & Simanjuntak, R. M. G. (2025). Learning to Optimize Meter Reading Routes in Billing Management Problems in the Balata Regional Unit. Journal La Multiapp, 6(2), 445-456. https://doi.org/10.37899/journallamultiapp.v6i2.2043