ANT COLONY OPTIMIZATION ALGORITHM DEVELOPMENT FOR ASSEMBLY LINE BALANCING WITH HUMAN-ROBOT COLLABORATION

• Main Author: Sri Rizki Sihotang, Intan
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/79292
• Institution: Institut Teknologi Bandung

In Industry 4.0, automation systems have been applied in assembly line balancing to improve production line efficiency and company productivity. One of the automation improvements in manufactory is involving human-robot collaboration to assemble tasks in workstations. Tasks assignment with human-robot collaboration research has been conducted using an analytical approach. Nevertheless, this research has weaknesses in a limited number of tasks that can be solved due to the computation time that increases exponentially. The proposed method to solve line balancing with human-robot collaboration is the ACO metaheuristic algorithm. ACO algorithm was developed based on research that studied tasks allocation in a two-sided line balancing. Several sections were modified in this ACO algorithm such as modifications in the components of the ant probability formula, objective function formula, parameter reduction, and the addition of several constraints due to multi-resources. The ACO algorithm will be developed to minimize the total relevant cost by considering the number of humans, robots, and robotic tools used. Each use of the resources will impact the amount of investment cost, operational cost, and benefits. ACO algorithm outputs are station tasks allocation, the objective function, and the algorithm computation time. The proposed ACO algorithm results 3.6% average solution difference with computation time efficiency reaches 60% comparing to the optimal solution.