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Chinese Journal of Management Science ›› 2026, Vol. 34 ›› Issue (11): 231-243.doi: 10.16381/j.cnki.issn1003-207x.2024.2389

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Multi-objective Optimization of Collection Routes for Multi-compartment Vehicles Considering Noise Pollution in Urban Construction Waste Sorted Collection

Jianhua Xiao1,2, Wenxue Zhang1(), Wantong Lv1,3, Boquan Gao1   

  1. 1.Research Center of Logistics,Nankai University,Tianjin 300071,China
    2.The Laboratory for Economic Behaviors and Policy Simulation,Nankai University,Tianjin 300071,China
    3.Shenzhen International Logistics Development Co. ,Ltd,Shenzhen 518083,China
  • Received:2024-12-31 Revised:2025-04-18 Online:2026-11-25 Published:2026-11-10
  • Contact: Wenxue Zhang E-mail:1120221336@mail.nankai.edu.cn

Abstract:

With the accelerating urbanization process, construction waste generation has been increasing year by year, making sorted construction waste collection an inevitable trend. However, urban construction waste is predominantly transported at night using heavy-duty vehicles, which often generate high-decibel noise during operation. Focusing on the characteristics of non-concrete construction waste-including its small volume, suitability for multi-compartment vehicle transportation, and significant noise pollution, for the first time the consideration of both multi-compartment vehicles and road noise pollution is introduced. With the dual objectives of minimizing total cost and noise pollution, we formulate a multi-objective optimization model considering noise pollution for multi-compartment vehicle collection routes in urban construction waste transportation under sorted collection.To address the NP-hard nature of the model, an IMOEA/D membrane algorithm based on tissue P systems is proposed. This is the first time that a tissue P system-based membrane algorithm has been introduced, enabling the IMOEA/D algorithm to incorporate multiple decomposition strategies for parallel problem-solving. Furthermore, to overcome the limitations of traditional MOEA/D algorithms, such as reliance on a single evolutionary operator, fixed control parameters, and a fixed neighborhood size that reduce solution efficiency, the IMOEA/D algorithm integrates an individual classification strategy, a self-adaptive differential evolution operator selection strategy, and a dynamic neighborhood adjustment strategy, thereby further enhancing algorithm performance.By solving instances of various scales and comparing the results with those of benchmark algorithms, the proposed algorithm demonstrates high solution quality, with improved convergence and diversity. In addition, the effectiveness of incorporating the membrane algorithm and designing a self-adaptive differential evolution (DE) operator selection strategy is validated. Model comparison analysis highlights the necessity of considering noise pollution in the sorted collection of non-concrete construction waste. Decision preference analysis confirms the significant advantage of the proposed model in balancing the two objectives. Moreover, it is verified that introducing multi-compartment vehicles into the sorted collection of non-concrete construction waste can significantly reduce both collection costs and noise pollution, and that noise thresholds indeed have a substantial impact on total collection costs. Finally, a real-world case study provides theoretical guidance and decision support for addressing practical issues in urban construction waste sorting and collection.

Key words: multi-compartment vehicle, noise pollution, multi-objective, non-concrete construction waste, waste sorted collection, membrane algorithm

CLC Number: