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

Abstract

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