PMID- 35401725
OWN - NLM
STAT- MEDLINE
DCOM- 20220412
LR  - 20220413
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based 
      on an Improved Hierarchical Agglomerative Clustering Algorithm.
PG  - 7412611
LID - 10.1155/2022/7412611 [doi]
LID - 7412611
AB  - 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.
CI  - Copyright (c) 2022 Zeming Wei et al.
FAU - Wei, Zeming
AU  - Wei Z
AUID- ORCID: 0000-0002-9867-3929
AD  - School of Computer Science, South China Normal University, Guangzhou 510632, 
      China.
FAU - Liang, Chufeng
AU  - Liang C
AUID- ORCID: 0000-0003-4269-6117
AD  - School of Computer Science, South China Normal University, Guangzhou 510632, 
      China.
FAU - Tang, Hua
AU  - Tang H
AUID- ORCID: 0000-0002-9439-2687
AD  - School of Computer Science, South China Normal University, Guangzhou 510632, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20220330
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
RN  - 0 (Solid Waste)
SB  - IM
MH  - Algorithms
MH  - Cities
MH  - Cluster Analysis
MH  - *Refuse Disposal/methods
MH  - Solid Waste
MH  - Transportation
MH  - *Waste Management/methods
PMC - PMC8986388
COIS- The authors declare that there are no conflicts of interest regarding the 
      publication of this paper.
EDAT- 2022/04/12 06:00
MHDA- 2022/04/13 06:00
PMCR- 2022/03/30
CRDT- 2022/04/11 05:28
PHST- 2021/10/24 00:00 [received]
PHST- 2022/01/15 00:00 [revised]
PHST- 2022/03/03 00:00 [accepted]
PHST- 2022/04/11 05:28 [entrez]
PHST- 2022/04/12 06:00 [pubmed]
PHST- 2022/04/13 06:00 [medline]
PHST- 2022/03/30 00:00 [pmc-release]
AID - 10.1155/2022/7412611 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Mar 30;2022:7412611. doi: 10.1155/2022/7412611. 
      eCollection 2022.