PENGEMBANGAN ALGORITMA METAHEURISTIK ANT COLONY OPTIMIZATION UNTUK PERANCANGAN LINTASAN PERAKITAN PRODUK CAMPURAN DAN PERAKITANNYA MENGGUNAKAN KOLABORASI MANUSIA-ROBOT

• Main Author: Abi Hamid, Moh.
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/68397
• Institution: Institut Teknologi Bandung

The development of manufacturing technology in the era of globalization is dynamic and fast. Among the technologies is utilizing human-robot collaboration on the assembly line. PT JVC Electronics Indonesia is an electronic manufacturing company that has implemented collaborative robots in its assembly operation to increase productivity and efficiency. The proliferation of product customization requires flexibility across the assembly line. Thus, human-robot collaboration is needed. Redesigning an assembly line is also required due to a product's short product life cycle. Therefore, it is necessary to develop an algorithm to solve the mixed-model assembly line balancing problem utilizing human-robot collaboration. The algorithm should produce feasible solutions with efficient computational time. This study aims to develop an ant colony optimization (ACO) algorithm for mixed-model assembly line balancing problems utilizing human-robot collaboration. The ACO algorithm is a population-solution-based algorithm that uses all existing populations to search for a solution. Evaluation of the proposed algorithm utilizes a full factorial design against the objective function value. Based on the results of experiments, three parameters have a significant effect, namely number of iterations (It), colony size (Cs), and Importance rate of resource allocation cost (?). The ACO algorithm produces optimal solutions for data of 11 and 17 tasks. On the other hand, a gap of 24,42% for data of 19, 25, 35 and 43 tasks. Only local-optimum is achieved for data 61 and 75. In addition, the efficiency of computation time from the ACO algorithm is 89.07% compared with the analytical method.