An artificial neural network for resource leveling problems

Abstract

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.