International Journal of Performance Analysis in Sport, 2021, 17(3): 289-298 doi: 10.23940/ijpe.21.03.p4.289298

Original article

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

Anil Kumar Gulivindala,a,*, M.V.A. Raju Bahubalendrunia, S.S.V. Prasad Varupalaa, and Chandrasekar Ravib

Department of Mechanical Engineering, National Institute of Technology, Puducherry,609609, India

Department of Computer Science Engineering, National Institute of Technology, Puducherry, 609609, India

*Corresponding Author(s): Corresponding author. E-mail address: anilgulivindala@gmail.com Corresponding author. E-mail address: anilgulivindala@gmail.com

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.

Keywords： disassembly sequence planning ; EMA-PSO algorithm ; disassembly predicates ; optimality function

PDF (546KB) Metadata Related articles Export EndNote| Ris| Bibtex

Cite this article

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. International Journal of Performance Analysis in Sport, 2021, 17(3): 289-298 doi:10.23940/ijpe.21.03.p4.289298

Reference

FaccioM., PersonaA., SgarbossaF. and ZaninG.

Sustainable SC through the complete reprocessing of end-of-life products by manufacturers: A traditional versus social responsibility company perspective

European Journal of Operational Research, 233(2), pp.359-373, 2014.

BahubalendruniM.R.and VarupalaV.P.

Disassembly Sequence Planning for Safe Disposal of End-of-Life Waste Electric and Electronic Equipment

National Academy Science Letters, pp.1-5, 2020.

MengK., LouP., PengX. and PrybutokV.

An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

International Journal of Production Research, 54(18), pp.5567-5593, 2016.

SmithS., SmithG. and ChenW.H.

Disassembly sequence structure graphs: An optimal approach for multiple-target selective disassembly sequence planning

Advanced engineering informatics, 26(2), pp.306-316, 2012.

KhederM., TriguiM. and AifaouiN.

Disassembly sequence planning based on a genetic algorithm. Proceedings of the Institution of Mechanical Engineers

Part C: Journal of Mechanical Engineering Science, 229(12), pp.2281-2290, 2015.

TsengY.

J., Yu, F.Y. and Huang, F.Y. A green assembly sequence planning model with a closed-loop assembly and disassembly sequence planning using a particle swarm optimization method

The International Journal of Advanced Manufacturing Technology, 57(9), pp. 1183-1197, 2011.

ZhongL., YouchaoS., GabrielO.E.and HaiqiaoW.

Disassembly sequence planning for maintenance based on metaheuristic method

Aircraft Engineering and Aerospace Technology, 2011.

ZhangX.F.and ZhangS.Y.

Product cooperative disassembly sequence planning based on branch-and-bound algorithm

The International Journal of Advanced Manufacturing Technology, 51(9-12), pp. 1139-1147, 2010.

LiuX., PengG., LiuX. and HouY.

Disassembly sequence planning approach for product virtual maintenance based on improved max-min ant system

The International Journal of Advanced Manufacturing Technology, 59(5), pp.829-839, 2012.

TianG., ZhouM. and ChuJ.

A chance constrained programming approach to determine the optimal disassembly sequence

IEEE Transactions on Automation Science and Engineering, 10(4), pp. 1004-1013, 2013.

YuB., WuE., ChenC., YangY., YaoB.Z.and LinQ.

A general approach to optimize disassembly sequence planning based on disassembly network: A case study from automotive industry

Advances in Production Engineering & Management, 12(4), pp.305-320, 2017.

ZhangX.

F., Yu, G., Hu, Z.Y., Pei, C.H. and Ma, G.Q. Parallel disassembly sequence planning for complex products based on fuzzy-rough sets

The International Journal of Advanced Manufacturing Technology, 72(1-4), pp.231-239, 2014.

GunjiA.B., DeepakB.B.B.

V.L., Bahubalendruni, C.R.and Biswal, D.B.B. An optimal robotic assembly sequence planning by assembly subsets detection method using teaching learning-based optimization algorithm

IEEE Transactions on Automation Science and Engineering, 15(3), pp. 1369-1385, 2018.

LiuJ., ZhouZ., PhamD. T., Xu, W., Ji, C. and Liu, Q., 2020.

Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing

Robotics and Computer-Integrated Manufacturing, 61, p.101829.

PistolesiF. and LazzeriniB.

TeMA: a tensorial memetic algorithm for many-objective parallel disassembly sequence planning in product refurbishment

IEEE Transactions on Industrial Informatics, 15(6), pp.3743-3753, 2019.

PrakashP.K.S., CeglarekD. and TiwariM.K.

Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem

The International Journal of Advanced Manufacturing Technology, 60(9-12), pp. 1125-1137, 2012.

TianG., RenY., FengY., ZhouM., ZhangH. and TanJ.

Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony

IEEE Transactions on Industrial Informatics, 15(4), pp.2456-2468, 2018.

PornsingC. and WatanasungsuitA. Discrete particle swarm optimization for disassembly sequence planning. In 2014 IEEE International Conference on Management of Innovation and Technology, pp.480-485, 2014.

XuJ., ZhangS.Y.and FeiS.M.

Product remanufacture disassembly planning based on adaptive particle swarm optimization algorithm

Journal of Zhejiang University (Engineering Science), 45(10), pp. 1746-1752, 2011.

LiuZ.F., YangD.J.and GuG.G.

Disassembly sequence planning based on particle swarm-simulated annealing optimization

Journal of Hefei University of Technology (Natural Science), 2, 2011.

BahubalendruniM.R., BiswalB.B., and DeepakB.L.

Optimal robotic assembly sequence generation using particle swarm optimization

Journal of Automation and Control Engineering, 4(2),2016.

ZhouZ., LiuJ., PhamD.

T., Xu, W., Ramirez, F.J., Ji, C. and Liu, Q. Disassembly sequence planning: Recent developments and future trends. Proceedings of the Institution of Mechanical Engineers

Part B: Journal of Engineering Manufacture, 233(5), pp. 1450-1471, 2019.

BahubalendruniM.R.and BiswalB.B.

A review on assembly sequence generation and its automation. Proceedings of the Institution of Mechanical Engineers

Part C: Journal of Mechanical Engineering Science, 230(5), pp.824-838, 2016.

GulivindalaA.K., BahubalendruniM.R., VarupalaS. V. P. and SankaranarayanasamyK.

A Heuristic Method with a Novel Stability Concept to Perform Parallel Assembly Sequence Planning by Subassembly Detection

Assembly Automation, 40(5), pp.779-787, 2020.

GulivindalaA.K., MattaV.R.and BahubalendruniM.R.

Disassembly Sequence Planning Methodology for EOL Products Through a Computational Approach. In Innovative Product Design and Intelligent Manufacturing Systems

pp.723-731, 2020.

GrebenkovD.S.and SerrorJ.

Following a trend with an exponential moving average: Analytical results for a Gaussian model

Physica A: Statistical Mechanics and its Applications, 394, pp.288-303, 2014.

PederseM. E. H. Good parameters for particle swarm optimization. Technical Report, No. HL1001, Hvass Laboratories, Copenhagen, Denmark, 2010.

XuW., TangQ., LiuJ., LiuZ., ZhouZ. and PhamD.T.

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Robotics and Computer-Integrated Manufacturing, 62, p. 101860, 2020.

/

〈 〉

Copyright © 2016-2020 International Journal of Performability Engineering, All Rights Reserved.
Maintained by Beijing Magtech Co. Ltd