Term 252

Real-Time Construction Logistics Platform with Dynamic Scheduling

Project Type: Self-Initiated

Project Description

Construction sites are complex and dynamic environments where the involvement of multiple parties increases the risk of delays, congestion, and poor coordination. The flow of vehicles through site gates represents a critical control point, as even minor delays can propagate into larger disruptions. Traditional construction management is often static and heavily dependent on human decision making, limiting its ability to respond quickly. As a result, construction sites frequently experience longer waiting times, severe bottlenecks, and increased project cost This project aims to design a real-time construction operations platform that supports dynamic scheduling and improved site flow. The primary purpose of the platform is to enhance operational coordination and logistics within the construction site. It is designed to support adaptive planning, allowing the platform to operate in parallel with ongoing construction activities. The project is currently under development; therefore, no direct results are available at this stage. The technical approach for developing the platform is based on representing construction operations and logistics as an event-driven system using a discrete-event framework. Key events such as equipment arrivals, queue formation, and congestion are identified as triggers for state changes within the system. The platform updates the schedule upon the occurrence of each event to enable faster responses and direct corrective actions. Heuristic and rule-based logic are modeled to support responsive scheduling decisions. The expected outcomes of this project are to provide an efficient operational framework that supports real-time decision making, minimizes waiting times, and improves site flow. The proposed platform is intended to be applicable to real construction sites. Future work will focus on continued development of the system, including systematic verification and validation stages.


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Project Team

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Faisal Abdullah Almutairi
ICS
Anass Madkar Alqahtani
Anass Madkar Alqahtani
ISE
Hassan Khalid Misbah
Hassan Khalid Misbah
CIE
Hamoud Khalid Aljadidi
Hamoud Khalid Aljadidi
ISE
Suliman Abdullah Lami
Suliman Abdullah Lami
CE
Abdullah Meshhen Almeshhen
Abdullah Meshhen Almeshhen
ICS

Team Coach

DR. Omar Hammad
DR. Omar Hammad
Assistant Professor

Info. & Computer Science Dept.

SDAIA-KFUPM Joint Research Center for Artificial Intelligence