Optimizing Ideal Time in Stochastic Assembly Line Balancing through Chance-Constrained via K-means Algorithm

To tackle the assembly line balancing problem (ALBP), we present an integer mathematical formulation that integrates both the stochastic nature of task times and equipment selection by task. The proposed approach aims to optimize task allocation along the production line, considering variations in task durations and the specific equipment requirements for each task. Our main goal is to minimize the total ideal time while ensuring efficient equipment utilization and maintaining a balanced workload across workstations. We achieve this through the development of a mathematical model that assigns tasks to workstations, considering both stochastic task times and equipment needs. To address the complexity of the problem, we propose a clustering method based on K-means technique, allowing for efficient grouping of tasks based on their characteristics and equipment requirements. Through iterative improvement of task assignments, our objective is to attain a near-optimal solution that minimizes both the ideal assembly time and equipment requirements. Hence, enhancing overall production line efficiency. Initial experimentation and validation using datasets provide insights into the effectiveness of our approach, demonstrating significant improvements in efficiency and cost savings across production line operations.