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DCM: D Number Extended Cognitive Map. Application on Location Selection in SCM
Author(s) -
Lian Zhou,
Fuyuan Xiao
Publication year - 2019
Publication title -
international journal of computers communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2019.5.3585
Subject(s) - fuzzy cognitive map , computer science , resilience (materials science) , perspective (graphical) , outsourcing , selection (genetic algorithm) , supply chain , analytic network process , process (computing) , supply chain management , adjacency list , multiple criteria decision analysis , operations research , cognitive map , risk analysis (engineering) , cognition , fuzzy logic , fuzzy set , fuzzy number , business , artificial intelligence , mathematics , analytic hierarchy process , marketing , physics , algorithm , thermodynamics , operating system , neuroscience , biology
Offshore outsourcing is a widely used management technique for performing business functions with the aim of reducing labor and transportation costs. The selection of locations has a significant influence on the supply chain’s resilience and qualities, but the influence of multiple external factors on the supply chain’s performance in local places in a complex and uncertain environment has not been examined. In this study, we investigated the influence of external factors in a highly uncertain and complicated situation in which relationships between external factors and supply chain resilience are complicated. Furthermore, we proposed a novel model to select locations from a comprehensive perspective. Specifically, the fuzzy cognitive map (FCM) is utilized to simulate the dynamic influence process where the adjacency is aggregated by D numbers. The weights of different resilience capabilities are considered from the perspective of maximizing benefits by using the decision-making trial and evaluation laboratory-analytic network processes (DEMATEL-ANP) model. By comparing the distance to the ideal solutions, we selected the best alternative location. Our results differ from the general case, which reveals that the weights of different capabilities influence selections.

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