A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management

Nowadays, urban solid waste management is one of the most crucial activities in municipalities and their affiliated organizations. It includes the processes of collection, transportation and disposal. These major operations require a large amount of resources and investments, which will always be su...

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Bibliographic Details
Published in:Waste management & research 2020-02, Vol.38 (2), p.156-172
Main Authors: Babaee Tirkolaee, Erfan, Mahdavi, Iraj, Seyyed Esfahani, Mir Mehdi, Weber, Gerhard-Wilhelm
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Benchmarks
Case studies
Cities
Computer simulation
Decision analysis
Design parameters
Municipal waste management
Municipalities
Optimization
Routing
Sensitivity analysis
Simulated annealing
Solid Waste
Solid waste management
Solid wastes
Taguchi methods
Transportation
Urban areas
Waste disposal
Waste Management
Waste management industry
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Summary:Nowadays, urban solid waste management is one of the most crucial activities in municipalities and their affiliated organizations. It includes the processes of collection, transportation and disposal. These major operations require a large amount of resources and investments, which will always be subject to limitations. In this paper, a chance-constrained programming model based on fuzzy credibility theory is proposed for the multi-trip capacitated arc routing problem to cope with the uncertain nature of waste amount generated in urban areas with the aim of total cost minimization. To deal with the complexity of the problem and solve it efficiently, a hybrid augmented ant colony optimization algorithm is developed based on an improved max–min ant system with an innovative probability function and a simulated annealing algorithm. The performance of hybrid augmented ant colony optimization is enhanced by using the Taguchi parameter design method to adjust the parameters’ values optimally. The overall efficiency of the algorithm is evaluated against other similar algorithms using well-known benchmarks. Finally, the applicability of the suggested methodology is tested on a real case study with a sensitivity analysis to evolve the managerial insights and decision aids.
ISSN:0734-242X
1096-3669
DOI:10.1177/0734242X19865782