A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest prob...

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Bibliographic Details
Published in:International journal of production research 2013-03, Vol.51 (5), p.1451-1463
Main Authors: Wu, J., Zhang, W.Y., Zhang, S., Liu, Y.N., Meng, X.H.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian approach
Decision making
distributed manufacturing
Manufacturing
manufacturing resource
Matrix
matrix-based representation
Multiple criteria decision making
Networks
Optimization
Planning
Resource allocation
Resource management
Studies
supply chain
Supply chain management
Supply chains
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Summary:Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2012.693966