Productivity and delays assessment for concrete batch plant‐truck mixer operations

Abstract

Current research focuses on assessing productivity, cost, and delays for concrete batch plant (CBP) operations using Artificial Neural Network (ANN) methodology. Data were collected to assess cycle time, delays, cost of delays, cost of delivery, productivity, and price/m³ for the CBP. Two ANN models were designated to represent the CBP process considering many CBP variables. Input variables include delivery distance, concrete type, and truck mixer's load. Output variables include the assessment of cycle time, cost of delays, delivery cost, productivity, and price/m³. The ANN outputs have been validated to show the ANN's robustness in assessing the CBP output variables. The average validity percent for the ANN outputs is 96.25%. A Time‐Quantity (TQ) chart is developed to assess the time required for both truck mixers and the CBP to produce a specified quantity of concrete. Charts have been developed to predict cycle time/truck, delays/truck, cost of delays/truck, cost of delivery/m³, and price/m³.

Keywords:

• Artificial Neural Network (ANN)
• productivity
• cost analysis
• cycle time
• modeling
• concrete batch plant (CBP)
• truck mixer

Acknowledgements

The writers wish to express their gratitude and appreciation to the concrete production companies, which generously allowed us to collect data and access their sites. The cooperation and assistance of plants' managers and operators are also gratefully acknowledged and appreciated.