Journal of Intelligent & Robotic Systems

, Volume 82, Issue 1, pp 69–79 | Cite as

Disassembly Sequencing Using Tabu Search

  • Mohammad Alshibli
  • Ahmed El Sayed
  • Elif KongarEmail author
  • Tarek M. Sobh
  • Surendra M. Gupta


End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included


Disassembly sequence Electronics disassembly End-of-life management Heuristics Optimization Robotics applications Tabu search 


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.University of BridgeportBridgeportUSA
  2. 2.Northeastern UniversityBostonUSA

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