Evolutionary optimisation for robotic disassembly sequence planning and line balancing

Yuanjun Laili, Yongjing Wang, Yilin Fang, Duc Truong Pham

Research output: Chapter in Book/Report/Conference proceedingChapter


The performance of an evolutionary algorithm in solving disassembly sequence planning or disassembly line balancing greatly depends on six parts: the evolutionary operator; encoding scheme; solution selection and update strategy; population initialisation; solution maintenance; and terminal condition. This chapter introduces classical single-objective evolutionary algorithms (SOEAs) with typical evolutionary operators, and multi-objective evolutionary algorithms (MOEAs) with typical solution selection and update strategies. The chapter also elaborates on common encoding schemes. Typical settings on algorithm initialisation, solution maintenance and terminal conditions are introduced to help engineers to design efficient evolutionary algorithms for robotic disassembly optimisation problems.
Original languageEnglish
Title of host publicationOptimisation of Robotic Disassembly for Remanufacturing
PublisherSpringer, Cham
Number of pages26
ISBN (Electronic)9783030817992
ISBN (Print)9783030817985, 9783030818012
Publication statusPublished - Aug 2021

Publication series

NameSpringer Series in Advanced Manufacturing
PublisherSpringer, Cham
ISSN (Print)1860-5168
ISSN (Electronic)2196-1735


Dive into the research topics of 'Evolutionary optimisation for robotic disassembly sequence planning and line balancing'. Together they form a unique fingerprint.

Cite this