Most deterministic optimization models use average values of nondeterministic variables as their inputs. It is, therefore,
expected that a model that can accept the distribution of a random variable, while this may involve some more computational
complexity, would likely produce better results than the model using the average value. Artificial neural
network (ANN) is a standard technique for solving complex stochastic problems. In this research, ANN and adaptive
neuro-fuzzy inference system (ANFIS) have been implemented for modeling and optimizing product distribution in a multiechelon
transshipment system. Two inputs parameters, product demand and unit cost of shipment, are considered
nondeterministic in this problem. The solutions of ANFIS and ANN were compared to that of the classical transshipment
model. The optimal total cost of distribution using the classical model within the period of investigation was 6,332,304.00.
In the search for a better solution, an ANN model was trained, tested, and validated. This approach reduced the cost to
4,170,500.00. ANFIS approach reduced the cost to 4,053,661. This implies that 34% of the current operational cost was
saved using the ANN model, while 36% was saved using the ANFIS model. This suggests that the result obtained from the
ANFIS model also seems marginally better than that of the ANN. Also, the ANFIS model is capable of adjusting the values
of input and output variables and parameters to obtain a more robust solution.