Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA)

  • Authors:

  • Mukul Tripathi;Shubham Agrawal;Mayank Kumar Pandey;Ravi Shankar;M. K. Tiwari

  • Affiliations:

  • Department of Metallurgy and Materials Engineering, National Institute of Foundry and Forge Technology, Ranchi 834003, India;Department of Operations Research and Industrial Engineering, University of Texas at Austin, Austin, TX 78705, USA;Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur 721302, India;Department of Management Studies, Indian Institute of Technology, Delhi 110016, India;Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur 721302, India

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2009

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Abstract

Decrease in product life along with the advent of stringent regulations and environmental consciousness have led to increased concern for methodological product recovery through disassembly operations. This research proposes a fuzzy disassembly optimization model (FDOM) and is aimed at determining the optimal disassembly sequence as well as the optimal depth of disassembly to maximize the net revenue at the end-of-life (EOL) disposal of the product in the real world situations. In order to account for the uncertainty inherent in quality of the returned products, fuzzy control theory is incorporated in the problem environment for modeling the expected value of the recovered modules. Considering the computational complexity of the problem at hand, an innovative approach of Algorithm of Self-Guided Ants (ASGAs) is proposed for the same. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment techniques and analysis of variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization and Genetic Algorithm over which it always demonstrated better results thereby proving its superiority on the concerned problem.