Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

Over the past half-century, Buffer Allocation Problem (BAP) in production lines has remained a topic of continuous interest and research. In this context, BAP refers to determining optimal allocation buffers along line maximize efficiency productivity. This paper presents novel approach address unreliable serial lines. The objective is rate line, which directly influences its overall performance profitability. proposed introduces an adaptive simulation-optimization methodology, APSO, that leverages particle swarm optimization (PSO) algorithm. PSO metaheuristic technique inspired by behavior bird flocking or fish schooling, where particles explore solution space find solutions. novelty lies integrating jumping strategy into algorithm’s velocity equation. incorporates logarithmic exponential functions equation algorithm, utilizing dynamic parameters. modification enables algorithm quickly converge towards (or very close to) By incorporating strategy, enhances exploration-exploitation balance, efficiently navigating complex spaces overcoming local optima. To evaluate effectiveness method, extensive numerical experiments are conducted using various instances lines, ranging from 3 100 machines. Additionally, benchmark algorithms existing literature employed for comparison purposes. obtained results these serve as empirical evidence demonstrate accuracy approach. indicate outperforms regarding quality.