'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
The effectiveness of logistic network design and
management for complex and geographically distributed
production systems can be measured in terms of direct
logistic costs and in terms of supply chain production
performance. The management of transportation logistics,
for instance, involves difficult trade-offs among capacity
utilization, transportation costs, and production
variability often leading to the identification of multiple
logistic solutions. This paper defines and compares three
different modeling approaches to systematically assess
each identified logistic alternative in terms of actual
transportation costs and expected production losses. The
first modeling approach examined in the paper is a
mathematical model which provides the statistical basis
for estimating costs and risks of production losses in
simple application cases. The second model is a
stochastic, discrete event simulation model of bulk
maritime transportation specifically designed to capture
the dynamic interactions between the logistic network and
the production facilities. The third one is an AI-based
model implemented as a modular architecture of Artificial
Neural Networks (ANNs). In such an architecture each
network establishes a correlation between the logistic
variables relevant to a specific sub-problem and the
corresponding supply chain costs. Preliminary testing of
the three models shows the relative effectiveness and
flexibility of the ANN-based model; it also shows that
good approximation levels may be attained when either
the mathematical model or the simulation model are used
to generate accurate ANN training data sets for each
transportation/production sub-proble