Genetically optimised disassembly sequence for automotive component reuse

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.

Original languageEnglish
Pages (from-to)5409-5417
Number of pages9
JournalExpert Systems with Applications
Volume39
Issue number5
DOIs
Publication statusPublished - Apr 2012

Fingerprint

Genetic algorithms
Planning
Recovery
Heuristic methods
Reusability
Automotive industry
Mathematical operators
Sustainable development
Life cycle
Systems analysis
Engines
Ecodesign

Keywords

  • Design for disassembly
  • Disassembly
  • Disassembly sequence planning
  • Genetic algorithms
  • Optimisation
  • Reuse

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

@article{bc7e1704c4d84a6ea06217e282b06b39,
title = "Genetically optimised disassembly sequence for automotive component reuse",
abstract = "Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.",
keywords = "Design for disassembly, Disassembly, Disassembly sequence planning, Genetic algorithms, Optimisation, Reuse",
author = "Go, {T. F.} and {Abd. Wahab}, Dzuraidah and {Ab Rahman}, {Mohd Nizam} and Rizauddin Ramli and Aini Hussain",
year = "2012",
month = "4",
doi = "10.1016/j.eswa.2011.11.044",
language = "English",
volume = "39",
pages = "5409--5417",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "5",

}

TY - JOUR

T1 - Genetically optimised disassembly sequence for automotive component reuse

AU - Go, T. F.

AU - Abd. Wahab, Dzuraidah

AU - Ab Rahman, Mohd Nizam

AU - Ramli, Rizauddin

AU - Hussain, Aini

PY - 2012/4

Y1 - 2012/4

N2 - Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.

AB - Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.

KW - Design for disassembly

KW - Disassembly

KW - Disassembly sequence planning

KW - Genetic algorithms

KW - Optimisation

KW - Reuse

UR - http://www.scopus.com/inward/record.url?scp=84855874288&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855874288&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2011.11.044

DO - 10.1016/j.eswa.2011.11.044

M3 - Article

AN - SCOPUS:84855874288

VL - 39

SP - 5409

EP - 5417

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 5

ER -