Simulation-Based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm

Sai Srinivas Nageshwaraniyer, Nurcin Celik, Young Jun Son, Roberto Lu

Research output: Chapter in Book/Report/Conference proceedingChapter


The aerospace industry plays a key role in today's U.S. economy with its total sales comprising 1.4% of the U.S. gross domestic product. The increasing competition in the aerospace industry between domestic and foreign competitors is motivating aerospace companies to seek for new and innovative ways to decrease the costs and lead-time for new product development and production by implementing flexible and integrated assembly systems. In this work, we present a simulationbased optimization framework for aircraft assembly planning using a self-guided ant colony algorithm (SGAC), where the objective is to achieve line balancing in the assembly of aircraft. More specifically, the goal is to minimize the number of workstations and maximize average utilization of resources in the stages of assembly. Based on the current state of the facility, the framework devises an optimal assembly plan. This plan is executed until the next time interval when the framework is again invoked to deliver the optimal assembly plan. The generality and validity of the proposed approach have been successfully tested for aircraft assembly planning under various conditions via a designed experiment.

Original languageEnglish (US)
Title of host publicationEvolutionary Computing in Advanced Manufacturing
PublisherJohn Wiley and Sons
Number of pages27
ISBN (Print)9780470639245
StatePublished - Aug 22 2011


  • Aircraft assembly
  • Ant colony algorithm
  • Dynamic planning
  • Shop floor control

ASJC Scopus subject areas

  • General Engineering


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