In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 7 ( 2023-7-10), p. e0278814-
Abstract:
For the optimal design of the sustainable supply chain network, considering the comprehensiveness of the problem factors, considering the three aspects of economy, environment and society, the goal is to minimize the establishment cost, minimize the emission of environ-mental pollution and maximize the number of labor. A mixed integer programming model is established to maximize the efficiency of the supply chain network. The innovation of this paper, first, is to consider the impact of economic, environmental and social benefits in a continuous supply chain, where the environmental benefits not only consider carbon emissions but also include the emissions of plant wastewater, waste and solid waste as influencing factors. Second, a multi-objective fuzzy affiliation function is constructed to measure the quality of the model solution in terms of the overall satisfaction value. Finally, the chaotic particle ant colony algorithm is proposed, and the problem of premature convergence in the operation of the particle swarm algorithm is solved. Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0278814
DOI:
10.1371/journal.pone.0278814.g001
DOI:
10.1371/journal.pone.0278814.g002
DOI:
10.1371/journal.pone.0278814.g003
DOI:
10.1371/journal.pone.0278814.g004
DOI:
10.1371/journal.pone.0278814.g005
DOI:
10.1371/journal.pone.0278814.g006
DOI:
10.1371/journal.pone.0278814.g007
DOI:
10.1371/journal.pone.0278814.g008
DOI:
10.1371/journal.pone.0278814.t001
DOI:
10.1371/journal.pone.0278814.t002
DOI:
10.1371/journal.pone.0278814.t003
DOI:
10.1371/journal.pone.0278814.t004
DOI:
10.1371/journal.pone.0278814.t005
DOI:
10.1371/journal.pone.0278814.t006
DOI:
10.1371/journal.pone.0278814.t007
DOI:
10.1371/journal.pone.0278814.t008
DOI:
10.1371/journal.pone.0278814.t009
DOI:
10.1371/journal.pone.0278814.t010
DOI:
10.1371/journal.pone.0278814.t011
DOI:
10.1371/journal.pone.0278814.t012
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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