An Agent-Based Modeling for Improving Truck Productivity in Construction sites with Traffic Congestion

Truck productivity is recognized as being a major cause of cost and time overruns in earthmoving operation. Traffic congestions in large construction projects might negatively affect truck productivity. This paper presents an intelligent agent-based model to improve truck productivity in traffic congestion conditions. The model is based on reinforcement leaning theory, specially adapted to the problem. The development is carried out using MATLAB. A work example is provided to show the applicability and efficiency of the proposed model. The paper proves model s accuracy. This paper provides an artificial intelligence solution for truck productivity; therefore the paper contributes to automation in construction.

کلیدواژه ها:

Productivity ، agent-based modeling ، traffic congestion ، construction projects.