A genetic algorithm for optimizing space utilization in aircraft hangar shop

Xin Li, Z. X. Wang, Felix T.S. Chan, S. H. Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

This study considers the aircraft placement problem in aircraft hangar shops (AHS) encountered by aircraft service companies. AHSs usually have irregular shapes, and aircraft, too, have special shapes. Moreover, frequent operations involving moving aircraft in and out are complicated. For these reasons, aircraft placement is difficult. The present study deals with operations management in AHS to optimize space utilization by placing a greater number of aircraft, which would greatly benefit aircraft services companies. Herein, a novel genetic algorithm (GA) based approach is applied to optimize space utilization. To exactly express the problem, practical and operational principles, including both in AHS and in outdoor areas, are abstracted based on interviews with the staff of an aircraft service company. Then, the placement space is modeled in an x–y coordinate system. In addition, a two-dimensional geometry model for aircraft, consisting of seven parameters, is developed. Based on these works, a novel GA for solving the aircraft placement problem is developed. Finally, a practical instance with eight aircraft serviced by a company is tested. All eight aircraft are placed well by using the proposed approach. Compared to the previous scenario, where at most seven aircraft could be placed well, the proposed approach will greatly benefit aircraft service companies.

Original languageEnglish
Pages (from-to)1655-1675
Number of pages21
JournalInternational Transactions in Operational Research
Volume26
Issue number5
DOIs
Publication statusPublished - 1 Sep 2019

Keywords

  • aircraft hangar shops
  • aircraft placing
  • genetic algorithm
  • space utilization

ASJC Scopus subject areas

  • Business and International Management
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Management of Technology and Innovation

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