Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network
This paper developed an evolutionary fuzzy hybrid neural network (EFHNN) to enhance the effectiveness of assessing subcontractor performance in the construction industry. The developed EFHNN combines neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN), which acts as the major inference engine and operates with alternating linear and non-linear NN layer connections. Fuzzy logic is employed to sandwich the HNN between a fuzzification and defuzzification layer. The authors developed and applied the EFHNN to assess subcontractors performance by fusing HNN, FL and GA. Enhancing subcontractor performance assessments are crucial in terms of providing to general contractors information on historical contractor performance essential to guiding a selection of appropriate subcontractors for a specific current or future subcontracting need. Results show that the proposed EFHNN may be deployed effectively to achieve optimal mapping of input factors and subcontractor performance output. Moreover, the performance of linear and non-linear (high order) neuron layer connectors in the EFHNN was significantly better than performances achieved by previous models that used singular linear NN.