A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 7412611 - 17
• Main Authors: Wei, Zeming, Liang, Chufeng, Tang, Hua
• Format: Journal Article
• Language: English
• Published: United States Hindawi 30.03.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Analysis; Big Data; Carrying capacity; Clustering; Comparative analysis; Datasets; Garbage collection; Human resources; Modules; Municipal waste management; Operating costs; Optimization; Path planning; Planning; Regions; Route planning; Sanitation services; Scheduling; Solid waste management; Transportation; Transportation industry; Vehicles; Waste management; China
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/7412611

The disposal of municipal solid waste (MSW) is based on the divide-regional operation model at present, which brings management convenience. However, due to the constraints of the division between the operating regions, there are problems such as inflexible vehicle scheduling and low efficiency. Changing this mode will put pressure on management. It is necessary to break the constraint of the region and form a fully automatic operating scheme without too much manual management. First, the initial clusters are formed by considering the distance in the initial allocation module. Secondly, through the type labeling and reallocation module, the single big data set is transformed into multiple small data sets by considering the allocated amount of garbage and the carrying capacity of garbage vehicles. Then, this work proposes the improved hierarchical agglomerative clustering (IHAC) algorithm and the garbage collecting path planning (GCPP) algorithm to realize the intelligent allocation of waste and scheduling route planning of garbage vehicles. Finally, through simulation by real example and comparative analysis, the advantages of the proposed scheme are discussed. The results show that the proposed scheme is more effective than the original scheme and other advanced methods, which can provide decision support for the scientific and intelligent collection and transportation of MSW.