Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites

Abstract

This research introduces a comprehensive scheme to tackle the Mixed-Model Assembly Line Balancing Problem (MALBPLW) within manufacturing contexts. The primary aim is to optimize assembly line task assignments by integrating both the learning effect and worker prerequisites. The learning effect recognizes the enhanced efficiency of workers over time due to learning and experience. A novel mathematical model and solution approach are proposed, encompassing factors like cycle time, task interdependencies, worker classifications, and the learning effect. The model endeavors to minimize the overall costs related to both workers and workstations while simultaneously maximizing production efficiency. Experimental assessments are conducted to evaluate the efficacy of this proposed approach. Diverse manufacturing scenarios are inspected, comparing and analyzing cost reductions and production efficiency. The outcomes highlight the effectiveness of this approach in achieving enhanced cost-effectiveness and resource utilization in contrast to conventional methods. This study contributes significantly to advancing assembly line balancing and production planning techniques by presenting a pragmatic framework for optimizing resource usage and reducing costs in manufacturing environments. The knowledge extracted from these discoveries can significantly assist professionals in the industry seeking to improve manufacturing processes and strengthen competitiveness.