Exponential Moving Average Modelled Particle Swarm Optimization Algorithm for Efficient Disassembly Sequence Planning towards Practical Feasibility

doi:10.23940/ijpe.21.03.p4.289298

Abstract

Abstract:

The application of artificial intelligent (AI) algorithms in disassembly sequence planning (DSP) has attracted a lot of research attention recently due to their effectiveness at solving combinatorial problems. Particle swarm optimization (PSO) is the most widely preferred AI algorithm for obtaining an optimal solution for the DSP problem. However, the solutions generated from traditional PSO have limitations due to its converging nature at local optima. In this research, an attempt has been made to improve the workability of PSO by integrating it with the exponential moving average (EMA) method. The optimality function is designed to reduce disassembly effort by considering tool changes, gripper changes and directional changes as parameters. A case study has been performed by testing the proposed EMA-PSO method on the 11-part industrial product. Obtained results are revealed that the diversity control is greatly achieved by the operators employed in the disassembly attributes. The effectiveness of the proposed EMA-PSO method is confirmed by making a comparative assessment with traditional PSO and other existent AI methods at different population sizes.

Key words: disassembly sequence planning, EMA-PSO algorithm, disassembly predicates, optimality function

Anil Kumar Gulivindala, M.V.A. Raju Bahubalendruni, S.S.V. Prasad Varupala, and Chandrasekar Ravi. Exponential Moving Average Modelled Particle Swarm Optimization Algorithm for Efficient Disassembly Sequence Planning towards Practical Feasibility [J]. Int J Performability Eng, 2021, 17(3): 289-298.

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