Open AccessLanding gear disassembly sequence planning using multi-level constraint matrix ant colony algorithm
Author(s) -
Ekene Gabriel Okafor,
Paul Jemitola,
Osichinaka Ubadike,
M.T. Abba,
G. Sule,
Mokhtari Mohammed
Publication year - 2021
Publication title -
nigerian journal of technology
Language(s) - English
Resource type - Journals
eISSN - 2467-8821
pISSN - 0331-8443
DOI - 10.4314/njt.v40i4.15
Subject(s) - sequence (biology) , constraint (computer aided design) , ant colony optimization algorithms , algorithm , ant colony , genetic algorithm , matrix (chemical analysis) , mathematical optimization , engineering , computer science , real time computing , mathematics , composite material , biology , mechanical engineering , genetics , materials science
The life cycle of most complex engineering systems is greatly a function of maintenance. Generally, most maintenance operation usually requires the removal of failed part. Disassembly sequence planning is an optimization program that seeks to identify the optimal sequence for the removal of the failed part. Most studies in this area usually, use single constraint matrix while implementing varied complex algorithm to identify the optimal sequence that saves time associated with carrying out maintenance operation. The used of single constraint matrix typically has the drawback of computer higher storage requirement as well as time consumption. To address this problem, this study proposes Multi-Level Constraint Matrix Ant Colony Algorithm (MLCMACA). MLCMACA efficiency was validated using complex aircraft landing gear systems in comparison with genetic algorithms. The result shows MLCMACA superior performance from the perspective of reduced search time and faster tracking of optimal disassembly sequence. Hence is recommended for handling of disassembly sequence planning problems.