Municipal solid waste collection and transportation routing optimization based on IAC-SFLA

    Youbiao Hu Affiliation
    ; Qiding Ju Affiliation
    ; Taosheng Peng Affiliation
    ; Shiwen Zhang Affiliation
    ; Xingming Wang Affiliation


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.
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Feb 1, 2024
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