A new methodology for solving resource leveling
problems is introduced using Artificial Neural Networks
(ANN). This paper describes a new efficient and robust
approach which is unique to those utilized by traditional
heuristic and optimization resource leveling techniques.
The Resource Leveling Artificial Neural Network (RLANN)
exploits advantages of both Hopfield networks and competition-based
artificial neural networks. The universal scheme of the
RLANN is applicable to construction project networks produced
with Critical Path Method (CPM), in forms of either arrow
or precedence diagrams. The scheme is comprised of two
layers, an input and a competition layer, of artificial
node matrices fully connected by links. Solving mechanisms
inside the RLANN are based on an equation of motion and
a competition strategy that control the level of daily
resource usage. While the equation of motion governs activities
to be shifted within schedule constraints, the competition
process finds the best positions for the activities to
achieve optimum results. The approach is simple and can
be implemented on either a personal computer or a parallel
processing device. The solutions produced are comparable
to, or better than, those generated by other heuristic
or optimization techniques. This paper describes the development
of the RLANN, its solving mechanisms, and its uses in construction
resource leveling problems. The comparison of the result
of the RLANN to those of other traditional techniques is
also included. The conclusions highlight the applicability
of this model to other civil engineering problems.