In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 4 ( 2023-4-7), p. e0282312-
Kurzfassung:
In recent years, intelligent robots have facilitated intelligent production, and a new type of problem (personnel–robot-position matching (PRPM)) has been encountered in personnel–position matching (PPM). In this study, a dynamic three-sided matching model is proposed to solve the PRPM problem in an intelligent production line based on man–machine collaboration. The first issue considered is setting the dynamic reference point, which is addressed in the information evaluation phase by proposing a method for setting the dynamic reference point based on the prospect theory. Another important issue involves multistage preference information integration, wherein a probability density function and a value function are introduced. Considering the attenuation of preference information in a time series, the attenuation index model is introduced to calculate the satisfaction matrix. Furthermore, a dynamic three-sided matching model is established. Additionally, a multi-objective decision-making model is established to optimize the matching of multiple sides (personnel, intelligent robots, and positions). Subsequently, the model is transformed into a single objective model using the triangular balance principle, which is introduced to obtain the final optimisation results in this modelling process. A case study is presented to illustrate the practicality of the dynamic three-sided matching model in intelligent environments. The results indicate that this model can solve the PRPM problem in an intelligent production line.
Materialart:
Online-Ressource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0282312
DOI:
10.1371/journal.pone.0282312.g001
DOI:
10.1371/journal.pone.0282312.g002
DOI:
10.1371/journal.pone.0282312.g003
DOI:
10.1371/journal.pone.0282312.g004
DOI:
10.1371/journal.pone.0282312.t001
DOI:
10.1371/journal.pone.0282312.t002
DOI:
10.1371/journal.pone.0282312.t003
DOI:
10.1371/journal.pone.0282312.t004
DOI:
10.1371/journal.pone.0282312.t005
DOI:
10.1371/journal.pone.0282312.t006
DOI:
10.1371/journal.pone.0282312.t007
DOI:
10.1371/journal.pone.0282312.s001
DOI:
10.1371/journal.pone.0282312.s002
DOI:
10.1371/journal.pone.0282312.s003
DOI:
10.1371/journal.pone.0282312.s004
DOI:
10.1371/journal.pone.0282312.s005
DOI:
10.1371/journal.pone.0282312.r001
DOI:
10.1371/journal.pone.0282312.r002
DOI:
10.1371/journal.pone.0282312.r003
DOI:
10.1371/journal.pone.0282312.r004
DOI:
10.1371/journal.pone.0282312.r005
DOI:
10.1371/journal.pone.0282312.r006
DOI:
10.1371/journal.pone.0282312.r007
DOI:
10.1371/journal.pone.0282312.r008
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2023
ZDB Id:
2267670-3
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