Application of ant colony algorithm to optimize waste transport distribution routes in Tegal
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Published:
May 30, 2024
Keywords:
Ant colony algorithm, Operational efficiency, Tegal City, Route Optimization, Waste management
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Computer Science
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Abstract
Effective and efficient waste management is an essential challenge in developing cities like Tegal City. Optimizing waste transport routes can reduce operational costs and environmental impact. This study aims to implement the Ant Colony Algorithm (ACO) to optimize waste distribution routes in Tegal City. This method was chosen for its proven ability to solve route optimization problems. This study developed a model for the simulation and analysis of waste transportation routes using actual location data from the Integrated Waste Treatment Site (TPST) to the landfill (TPA). The results showed that the implementation of ACO reduced the total mileage from 27.50 km to 21.05 km, a significant reduction that shows the algorithm's efficiency in determining the optimal route. The conclusion of this study confirms that ACO can be effectively used to improve waste transportation operations
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How to Cite
Gunawan, G. ., Handayani, S. . and Anandianskha, S. . (2024) “Application of ant colony algorithm to optimize waste transport distribution routes in Tegal”, Jurnal Mantik, 8(1), pp. 798-807. doi: 10.35335/mantik.v8i1.5223.
References
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Yi, N., Xu, J., Yan, L., & Huang, L. (2020). Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm. Future Generation Computer Systems, 109, 134–148. https://doi.org/https://doi.org/10.1016/j.future.2020.03.051
Zaman, H. R. R., & Gharehchopogh, F. S. (2022). An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Engineering with Computers, 38(Suppl 4), 2797–2831. https://doi.org/https://doi.org/10.1007/s00366-021-01431-6
Zhao, B., Gui, H., Li, H., & Xue, J. (2020). Cold chain logistics path optimization via improved multi-objective ant colony algorithm. Ieee Access, 8, 142977–142995. https://doi.org/10.1109/ACCESS.2020.3013951
Abubakar, I. R., Maniruzzaman, K. M., Dano, U. L., AlShihri, F. S., AlShammari, M. S., Ahmed, S. M. S., Al-Gehlani, W. A. G., & Alrawaf, T. I. (2022). Environmental sustainability impacts of solid waste management practices in the global South. International Journal of Environmental Research and Public Health, 19(19), 12717. https://doi.org/https://doi.org/10.3390/ijerph191912717
Ali, T., Irfan, M., Alwadie, A. S., & Glowacz, A. (2020). IoT-based smart waste bin monitoring and municipal solid waste management system for smart cities. Arabian Journal for Science and Engineering, 45, 10185–10198. https://doi.org/https://doi.org/10.1007/s13369-020-04637-w
Ayilara, M. S., Olanrewaju, O. S., Babalola, O. O., & Odeyemi, O. (2020). Waste management through composting: Challenges and potentials. Sustainability, 12(11), 4456. https://doi.org/https://doi.org/10.3390/su12114456
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330. https://doi.org/https://doi.org/10.1016/j.ese.2023.100330
Braik, M. S. (2021). Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Systems with Applications, 174, 114685. https://doi.org/https://doi.org/10.1016/j.eswa.2021.114685
Carrasco, J., García, S., Rueda, M. M., Das, S., & Herrera, F. (2020). Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm and Evolutionary Computation, 54, 100665. https://doi.org/https://doi.org/10.1016/j.swevo.2020.100665
Gad, A. G. (2022). Particle swarm optimization algorithm and its applications: a systematic review. Archives of Computational Methods in Engineering, 29(5), 2531–2561. https://doi.org/https://doi.org/10.1007/s11831-021-09694-4
Gao, H., Li, Z., Yu, X., & Qiu, J. (2021). Hierarchical multiobjective heuristic for PCB assembly optimization in a beam-head surface mounter. IEEE Transactions on Cybernetics, 52(7), 6911–6924. https://doi.org/10.1109/TCYB.2020.3040788
Halim, A. H., Ismail, I., & Das, S. (2021). Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artificial Intelligence Review, 54(3), 2323–2409. https://doi.org/https://doi.org/10.1007/s10462-020-09906-6
Hannan, M. A., Begum, R. A., Al-Shetwi, A. Q., Ker, P. J., Al Mamun, M. A., Hussain, A., Basri, H., & Mahlia, T. M. I. (2020). Waste collection route optimisation model for linking cost saving and emission reduction to achieve sustainable development goals. Sustainable Cities and Society, 62, 102393. https://doi.org/https://doi.org/10.1016/j.scs.2020.102393
Khan, S., Anjum, R., Raza, S. T., Bazai, N. A., & Ihtisham, M. (2022). Technologies for municipal solid waste management: Current status, challenges, and future perspectives. Chemosphere, 288, 132403. https://doi.org/https://doi.org/10.1016/j.chemosphere.2021.132403
Koul, B., Yakoob, M., & Shah, M. P. (2022). Agricultural waste management strategies for environmental sustainability. Environmental Research, 206, 112285. https://doi.org/https://doi.org/10.1016/j.envres.2021.112285
Lamb, W. F., Wiedmann, T., Pongratz, J., Andrew, R., Crippa, M., Olivier, J. G. J., Wiedenhofer, D., Mattioli, G., Al Khourdajie, A., & House, J. (2021). A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environmental Research Letters, 16(7), 73005. https://doi.org/10.1088/1748-9326/abee4e
Liu, C., Wu, L., Xiao, W., Li, G., Xu, D., Guo, J., & Li, W. (2023). An improved heuristic mechanism ant colony optimization algorithm for solving path planning. Knowledge-Based Systems, 271, 110540. https://doi.org/https://doi.org/10.1016/j.knosys.2023.110540
Liu, L., & Liao, W. (2021). Optimization and profit distribution in a two-echelon collaborative waste collection routing problem from economic and environmental perspective. Waste Management, 120, 400–414. https://doi.org/https://doi.org/10.1016/j.wasman.2020.09.045
Liu, S., Zhao, J., Xu, M., & Ahmadian, E. (2021). Effects of landscape patterns on the summer microclimate and human comfort in urban squares in China. Sustainable Cities and Society, 73, 103099. https://doi.org/https://doi.org/10.1016/j.scs.2021.103099
Lu, J., Li, B., Li, H., & Al-Barakani, A. (2021). Expansion of city scale, traffic modes, traffic congestion, and air pollution. Cities, 108, 102974. https://doi.org/https://doi.org/10.1016/j.cities.2020.102974
Miao, C., Chen, G., Yan, C., & Wu, Y. (2021). Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering, 156, 107230. https://doi.org/https://doi.org/10.1016/j.cie.2021.107230
Nanda, S., & Berruti, F. (2021). Municipal solid waste management and landfilling technologies: a review. Environmental Chemistry Letters, 19(2), 1433–1456. https://doi.org/https://doi.org/10.1007/s10311-020-01100-y
Sar, K., & Ghadimi, P. (2023). A systematic literature review of the vehicle routing problem in reverse logistics operations. Computers & Industrial Engineering, 177, 109011. https://doi.org/https://doi.org/10.1016/j.cie.2023.109011
Singh, A., Pal, D. B., Mohammad, A., Alhazmi, A., Haque, S., Yoon, T., Srivastava, N., & Gupta, V. K. (2022). Biological remediation technologies for dyes and heavy metals in wastewater treatment: New insight. Bioresource Technology, 343, 126154. https://doi.org/https://doi.org/10.1016/j.biortech.2021.12615
Yi, N., Xu, J., Yan, L., & Huang, L. (2020). Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm. Future Generation Computer Systems, 109, 134–148. https://doi.org/https://doi.org/10.1016/j.future.2020.03.051
Zaman, H. R. R., & Gharehchopogh, F. S. (2022). An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Engineering with Computers, 38(Suppl 4), 2797–2831. https://doi.org/https://doi.org/10.1007/s00366-021-01431-6
Zhao, B., Gui, H., Li, H., & Xue, J. (2020). Cold chain logistics path optimization via improved multi-objective ant colony algorithm. Ieee Access, 8, 142977–142995. https://doi.org/10.1109/ACCESS.2020.3013951
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