Leveraging bio-inspired knowledge-intensive optimization algorithm in the assembly line balancing problem

Abstract

With the increasing pressure from the market and the surge of “Industry 4.0,” staying competitive and relevant is becoming more and more difficult. The assembly line, which represents a long-term investment of the manufacturing industry, needs to be efficiently utilized. While assembly line balancing (ALB) problem had been studied for decades, oversights on the bottleneck resources could significantly impede its efficiency. In leveraging such information as part of the optimization problem, a contagious artificial immune network (CAIN) approach is proposed to simultaneously address ALB efficiency and bottleneck resources while achieving a truly balanced production line. A computational experiment conducted on benchmark data sets has demonstrated a proof-of-concept, where leveraging knowledge-intensive optimization approach had successfully produced high-quality solutions up to 100% improvement with statistically significant justification. Such findings may play an essential determinant in the manufacturing industry, whether being relevant or left out in the era of increasingly being information-driven.

Interactive demo

A looping preview of the line-balancing model: an artificial immune system assigns precedence-constrained tasks to workstations under a cycle-time limit, rebalancing to raise line efficiency.