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If f is a member of the exponential family, we get a FMLR models [48, 49].
In the FMLR model we consider the latent component-indicator variables [mathematical expression not reproducible], to classify each single observation:
Parameters of FMLR models can be efficiently estimated through the EM algorithm .
The most important and difficult step in building FMLR model is to determine K, the number of components.
To select an optimal model, we apply the FMLR model having an increasing number of components from 1 to 4 to fit, and apply Bayesian Information Criterion (BIC) as the indicator to select the most appropriate number of components.
FMLR method will be applied to solve the blackbox problem in the following part.
The results of the FMLR model calculation are forecasted resource real needs and the recommended resource configuration quantity interval [[??].sup.t] = ([[bar.y].sup.t], [e.sup.t]) at the time t of a specific service outlet.
Instead of traditional simple rules for human resource allocation by a fixed ratio that each service engineer is responsible for 15 excavators on average dynamic adjustment method with FMLR method is applied to minimize the fluctuation of service resource utilization ratio.
The calculated result of FMLR will offer a reference interval for the planning demand number.
The method of improved FMLR keeps the advantage of visualizing correlation coefficients between factors and optimization objects.
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- FML, RC, APR, CBP, TWM, and ACE