Municipal solid waste collection and transportation routing optimization based on IAC-SFLA
Abstract
In order to realize the efficient collection and low-carbon transport of municipal garbage and accelerate the realize the “dual-carbon” goal for urban transport system, based on the modeling and solving method of vehicle routing problem, the municipal solid waste (MSW) collection and transport routing optimization of an Improved Ant Colony-Shuffled Frog Leaping Algorithm (IAC-SFLA) is proposed. In this study, IAC-SFLA routing Optimization model with the goal of optimization collection distance, average loading rate, number of collections, and average number of stations is constructed. Based on the example data of garbage collection and transport in southern Baohe District, the comparative analysis with single-vehicle models, multiple-vehicle models, and basic ant colony algorithms. The multi-vehicle model of collection and transportation is superior to the single-vehicle model and the improved ant colony algorithm yields a total collection distance that is 19.76 km shorter and an average loading rate that rises by 4.15% from 93.95% to 98.1%. Finally, the improved ant colony algorithm solves for the domestic waste collection and transportation path planning problem in the north district of Baohe. Thus, the effectiveness and application of the proposed algorithm is verified. The research result can provide reference for vehicle routing in the actual collection and transport process, improve collection and transport efficiency, and achieve the goal of energy conservation and emission reduction.
Keyword : municipal solid waste, improved ant colony algorithm, collection and transportation routing planning, vehicle routing problem
How to Cite
Hu, Y., Ju, Q., Peng, T., Zhang, S., & Wang, X. (2024). Municipal solid waste collection and transportation routing optimization based on IAC-SFLA. Journal of Environmental Engineering and Landscape Management, 32(1), 31–44. https://doi.org/10.3846/jeelm.2024.20774
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Mahato, D. P., & Singh, R. S. (2018). Maximizing availability for task scheduling in on-demand computing-based transaction processing system using ant colony optimization. Concurrency and Computation: Practice and Experience, 30(11), e4405. https://doi.org/10.1002/cpe.4405
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Pellerin, R., Perrier, N., & Berthaut, F. (2020). A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 280(2), 395–416. https://doi.org/10.1016/j.ejor.2019.01.063
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Wang, Y., Peng, S., Zhou, X., Mahmoudi, M., & Zhen, L. (2020a). Green logistics location-routing problem with eco-packages. Transportation Research Part E: Logistics and Transportation Review, 143, 102118. https://doi.org/10.1016/j.tre.2020.102118
Wang, Y., Wang, L., Chen, G., Cai, Z., Zhou, Y., & Xing, L. (2020b). An improved ant colony optimization algorithm to the periodic vehicle routing problem with time window and service choice. Swarm and Evolutionary Computation, 55, 100675. https://doi.org/10.1016/j.swevo.2020.100675
Wang, Y., Wang, Z., Hu, X., Xue, G., & Guan, X. (2022a). Truck–drone hybrid routing problem with time-dependent road travel time. Transportation Research Part C: Emerging Technologies, 144, 103901. https://doi.org/10.1016/j.trc.2022.103901
Wang, Y., Yuan, Y., Guan, X., Xu, M., Wang, L., Wang, H., & Liu, Y. (2020c). Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation. Journal of Cleaner Production, 258, 120590. https://doi.org/10.1016/j.jclepro.2020.120590
Wang, Y., Zhe, J., Wang, X., Fan, J., Wang, Z., & Wang, H. (2022b). Collaborative multicenter reverse logistics network design with dynamic customer demands. Expert Systems with Applications, 206, 117926. https://doi.org/10.1016/j.eswa.2022.117926
Wu, X., Li, R., Chu, C.-H., Amoasi, R., & Liu, S. (2022). Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach. Annals of Operations Research, 308(1–2), 653–684. https://doi.org/10.1007/s10479-021-04012-4
Yesodha, R., & Amudha, T. (2022). A bio-inspired approach: Firefly algorithm for Multi-Depot Vehicle Routing Problem with Time Windows. Computer Communications, 190, 48–56. https://doi.org/10.1016/j.comcom.2022.04.005
Zhang, Q., & Xiong, S. (2018). Routing optimization of emergency grain distribution vehicles using the immune ant colony optimization algorithm. Applied Soft Computing, 71, 917–925. https://doi.org/10.1016/j.asoc.2018.07.050
Zhang, S., Zhang, J., Zhao, Z., & Xin, C. (2022). Robust optimization of municipal solid waste collection and transportation with uncertain waste output: A case study. Journal of Systems Science and Systems Engineering, 31(2), 204–225. https://doi.org/10.1007/s11518-021-5510-8
Zhao, X., Liu, H., & Ding, L. (2022). Decomposition analysis of the decoupling and driving factors of municipal solid waste: Taking China as an example. Waste Management, 137, 200–209. https://doi.org/10.1016/j.wasman.2021.11.003
Zhou, Y., Li, W., Wang, X., Qiu, Y., & Shen, W. (2022). Adaptive gradient descent enabled ant colony optimization for routing problems. Swarm and Evolutionary Computation, 70, 101046. https://doi.org/10.1016/j.swevo.2022.101046
Zhu, S., Zhu, W., Zhang, X., & Cao, T. (2020). Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance. International Journal of Advanced Robotic Systems, 17(3), 1–14. https://doi.org/10.1177/1729881419898979
Bräysy, O., & Gendreau, M. (2005). Vehicle routing problem with time windows, Part I: Route construction and local search algorithms. Transportation Science, 39(1), 104–118. https://doi.org/10.1287/trsc.1030.0056
Cao, S., Liao, W., & Huang, Y. (2021). Heterogeneous fleet recyclables collection routing optimization in a two-echelon collaborative reverse logistics network from circular economic and environmental perspective. Science of the Total Environment, 758, 144062. https://doi.org/10.1016/j.scitotenv.2020.144062
Chen, S., Yang, J., Li, Y., & Yang, J. (2017). Multiconstrained network intensive vehicle routing adaptive ant colony algorithm in the context of neural network analysis. Complexity, 2017, 8594792. https://doi.org/10.1155/2017/8594792
Cheng, J., Shi, F., Yi, J., & Fu, H. (2020). Analysis of the factors that affect the production of municipal solid waste in China. Journal of Cleaner Production, 259, 120808. https://doi.org/10.1016/j.jclepro.2020.120808
Dalavi, A. M., Pawar, P. J., & Singh, T. P. (2016). Tool path planning of hole-making operations in ejector plate of injection mould using modified shuffled frog leaping algorithm. Journal of Computational Design and Engineering, 3(3), 266–273. https://doi.org/10.1016/j.jcde.2016.04.001
Deng, W., Xu, J., Song, Y., & Zhao, H. (2020). An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application. International Journal of Bio-Inspired Computation, 16(3), 158–170. https://doi.org/10.1504/IJBIC.2020.111267
Dhanya, K. M., & Kanmani, S. (2016). Solving vehicle routing problem using hybrid swarm intelligent methods. In 2016 International Conference on Communication and Signal Processing (ICCSP) (pp. 1461–1465), Melmaruvathur, India. https://doi.org/10.1109/ICCSP.2016.7754399
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41. https://doi.org/10.1109/3477.484436
Duan, X., Niu, T., & Huang, Q. (2018). An improved shuffled frog leaping algorithm and its application in dynamic emergency vehicle dispatching. Mathematical Problems in Engineering, 2018, 7896926. https://doi.org/10.1155/2018/7896926
Eusuff, M. M., & Lansey, K. E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, 129(3), 210–225. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210)
Ganji, M., Kazemipoor, H., Hadji Molana, S. M., & Sajadi, S. M. (2020). A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows. Journal of Cleaner Production, 259, 120824. https://doi.org/10.1016/j.jclepro.2020.120824
Goli, A., Golmohammadi, A.-M., & Edalatpanah, S. A. (2022). Application of artificial intelligence in forecasting the demand for supply chains considering Industry 4.0. In A roadmap for enabling Industry 4.0 by artificial intelligence (pp. 43–55). Wiley. https://doi.org/10.1002/9781119905141.ch4
Goli, A., Zare, H. K., Tavakkoli‐Moghaddam, R., & Sadegheih, A. (2020). Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment. Computational Intelligence, 36(1), 4–34. https://doi.org/10.1111/coin.12228
He, M., Wei, Z., Wu, X., & Peng, Y. (2021). An adaptive variable neighborhood search ant colony algorithm for vehicle routing problem with soft time windows. IEEE Access, 9, 21258–21266. https://doi.org/10.1109/ACCESS.2021.3056067
Hidalgo-Paniagua, A., Vega-Rodríguez, M. A., Ferruz, J., & Pavón, N. (2015). MOSFLA-MRPP: Multi-objective shuffled frog-leaping algorithm applied to mobile robot path planning. Engineering Applications of Artificial Intelligence, 44, 123–136. https://doi.org/10.1016/j.engappai.2015.05.011
Homberger, J., & Gehring, H. (2005). A two-phase hybrid metaheuristic for the vehicle routing problem with time windows. European Journal of Operational Research, 162(1), 220–238. https://doi.org/10.1016/j.ejor.2004.01.027
Luo, Q., Wang, H., Zheng, Y., & He, J. (2020). Research on path planning of mobile robot based on improved ant colony algorithm. Neural Computing and Applications, 32(6), 1555–1566. https://doi.org/10.1007/s00521-019-04172-2
Mahato, D. P., & Singh, R. S. (2018). Maximizing availability for task scheduling in on-demand computing-based transaction processing system using ant colony optimization. Concurrency and Computation: Practice and Experience, 30(11), e4405. https://doi.org/10.1002/cpe.4405
Nguyen-Trong, K., Nguyen-Thi-Ngoc, A., Nguyen-Ngoc, D., & Dinh-Thi-Hai, V. (2017). Optimization of municipal solid waste transportation by integrating GIS analysis, equation-based, and agent-based model. Waste Management, 59, 14–22. https://doi.org/10.1016/j.wasman.2016.10.048
Pellerin, R., Perrier, N., & Berthaut, F. (2020). A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 280(2), 395–416. https://doi.org/10.1016/j.ejor.2019.01.063
Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 15, 169–176. https://doi.org/10.1016/j.asoc.2013.10.017
Wang, A., Zhang, L., Shi, Y., Rozelle, S., Osborn, A., & Yang, M. (2017). Rural solid waste management in China: Status, problems and challenges. Sustainability, 9(4), 506. https://doi.org/10.3390/su9040506
Wang, C. L., & Li, S. W. (2018). Hybrid fruit fly optimization algorithm for solving multi-compartment vehicle routing problem in intelligent logistics. Advances in Production Engineering & Management, 13(4), 466–478. https://doi.org/10.14743/apem2018.4.304
Wang, Y., Peng, S., Zhou, X., Mahmoudi, M., & Zhen, L. (2020a). Green logistics location-routing problem with eco-packages. Transportation Research Part E: Logistics and Transportation Review, 143, 102118. https://doi.org/10.1016/j.tre.2020.102118
Wang, Y., Wang, L., Chen, G., Cai, Z., Zhou, Y., & Xing, L. (2020b). An improved ant colony optimization algorithm to the periodic vehicle routing problem with time window and service choice. Swarm and Evolutionary Computation, 55, 100675. https://doi.org/10.1016/j.swevo.2020.100675
Wang, Y., Wang, Z., Hu, X., Xue, G., & Guan, X. (2022a). Truck–drone hybrid routing problem with time-dependent road travel time. Transportation Research Part C: Emerging Technologies, 144, 103901. https://doi.org/10.1016/j.trc.2022.103901
Wang, Y., Yuan, Y., Guan, X., Xu, M., Wang, L., Wang, H., & Liu, Y. (2020c). Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation. Journal of Cleaner Production, 258, 120590. https://doi.org/10.1016/j.jclepro.2020.120590
Wang, Y., Zhe, J., Wang, X., Fan, J., Wang, Z., & Wang, H. (2022b). Collaborative multicenter reverse logistics network design with dynamic customer demands. Expert Systems with Applications, 206, 117926. https://doi.org/10.1016/j.eswa.2022.117926
Wu, X., Li, R., Chu, C.-H., Amoasi, R., & Liu, S. (2022). Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach. Annals of Operations Research, 308(1–2), 653–684. https://doi.org/10.1007/s10479-021-04012-4
Yesodha, R., & Amudha, T. (2022). A bio-inspired approach: Firefly algorithm for Multi-Depot Vehicle Routing Problem with Time Windows. Computer Communications, 190, 48–56. https://doi.org/10.1016/j.comcom.2022.04.005
Zhang, Q., & Xiong, S. (2018). Routing optimization of emergency grain distribution vehicles using the immune ant colony optimization algorithm. Applied Soft Computing, 71, 917–925. https://doi.org/10.1016/j.asoc.2018.07.050
Zhang, S., Zhang, J., Zhao, Z., & Xin, C. (2022). Robust optimization of municipal solid waste collection and transportation with uncertain waste output: A case study. Journal of Systems Science and Systems Engineering, 31(2), 204–225. https://doi.org/10.1007/s11518-021-5510-8
Zhao, X., Liu, H., & Ding, L. (2022). Decomposition analysis of the decoupling and driving factors of municipal solid waste: Taking China as an example. Waste Management, 137, 200–209. https://doi.org/10.1016/j.wasman.2021.11.003
Zhou, Y., Li, W., Wang, X., Qiu, Y., & Shen, W. (2022). Adaptive gradient descent enabled ant colony optimization for routing problems. Swarm and Evolutionary Computation, 70, 101046. https://doi.org/10.1016/j.swevo.2022.101046
Zhu, S., Zhu, W., Zhang, X., & Cao, T. (2020). Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance. International Journal of Advanced Robotic Systems, 17(3), 1–14. https://doi.org/10.1177/1729881419898979