Flexible optimisation approach for material handling in a smart shopfloor

• Main Author: Ong, Sean Sheng Ming
• Other Authors: Chen Chun-Hsien
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/149104
• Institution: Nanyang Technological University

Since 2010, the manufacturing and industry started to undergo its fourth industrial revolution which was termed as industry 4.0. It was the industrial transformation that includes real-time data exchange between objects in a system, ability to create digital simulation models of real-world systems and the automation of manufacturing processes such as the use of Automated Guided Vehicles (AGVs) in material handling. Besides the used of autonomous machines in material handling, there were studies carried out to investigate the use of metaheuristic optimisation algorithms originally used to solve Vehicle Routing Problems (VRP) to plan the routes for the AGVs on the shopfloor with minimal to no human intervention. With the manufacturing operations management incorporating new subsystems such as real-time machine and job status due to the technology development in industry 4.0, the idea of using these algorithms for flexible manufacturing scheduling and optimisation becomes more practical. Flexible optimisation is needed to keep up with dynamic conditions on the shopfloor such as allocating newly arrived tasks or reallocating tasks due to breakdowns while accounting for the AGVs current location and schedule. This will improve efficiency of the shopfloor and ensure tasks are completed on time despite any disruptions. In this paper, 3 different algorithms were compared with one another on their performance in solving the proposed material handling model with the Solomon benchmark tests. The algorithms are Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO) and Ruin & Recreate (R&R). The R&R algorithm was deemed to have the best performance based on running time, cost, and consistency of obtaining their best result, respectively. It was then improved on to be able to update routes of the AGVs while it is carrying out its tasks without needing it to return to the depot first. Several cases representing the different dynamic conditions of the shopfloor were then tested on the algorithm. It was shown to be more efficient as compared to if the system had to wait for the AGVs to return to the depot before new tasks were allocated to them.