Partial disassembly sequence planning based on Pareto ant colony algorithm

Yu-fei, Xing; Liu, Qiang

doi:10.1109/ccdc.2016.7531852

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…A direct selection of target parts (high value or high impact parts) can be used (Ren et al, 2017). In addition, the optimal disassembly depth can be the result of a single (Go et al, 2012;Yeh, 2011) or multi-objective (Alfaro-Algaba and Ramirez, 2020;Ren et al, 2020;Wu et al, 2022;Yu-fei and Qiang, 2016) optimization.…”

Section: Disassembly Typesmentioning

confidence: 99%

Challenges and Prospects of Automated Disassembly of Fuel Cells for a Circular Economy

Assadi¹,

Goes²,

Baazouzi³

et al. 2023

Preprint

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Section: Disassembly Typesmentioning

confidence: 99%

Challenges and Prospects of Automated Disassembly of Fuel Cells for a Circular Economy

Assadi¹,

Goes²,

Baazouzi³

et al. 2023

Preprint

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“…Given the indices for evaluating a disassembly sequence, such as the disassembly time, cost and profit, the optimal disassembly sequence is searched with the rule-based reasoning (Smith et al , 2016), case-based reasoning (Yu et al , 2022) methods and intelligent optimization algorithms. The intelligent optimization algorithms include ant colony optimization (Liu et al , 2012; Xing et al , 2016), teaching-learning-based optimization (Xia et al , 2014; Xia et al , 2016), particle swarm optimization (PSO) (Li et al , 2013; Lu et al , 2011) and genetic algorithm (GA) (Deng et al , 2020; Kheder et al , 2015). Immune algorithm (IA) has applied to many problems and showed good performance.…”

Section: Introductionmentioning

confidence: 99%

Selective disassembly sequence optimization based on the improved immune algorithm

Wang

2023

RIA

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Purpose The purpose of this paper is to improve the automation of selective disassembly sequence planning (SDSP) and generate the optimal or near-optimal disassembly sequences. Design/methodology/approach The disassembly constraints is automatically extracted from the computer-aided design (CAD) model of products and represented as disassembly constraint matrices for DSP. A new disassembly planning model is built for computing the optimal disassembly sequences. The immune algorithm (IA) is improved for finding the optimal or near-optimal disassembly sequences. Findings The workload for recognizing disassembly constraints is avoided for DSP. The disassembly constraints are useful for generating feasible and optimal solutions. The improved IA has the better performance than the genetic algorithm, IA and particle swarm optimization for DSP. Research limitations/implications All parts must have rigid bodies, flexible and soft parts are not considered. After the global coordinate system is given, every part is disassembled along one of the six disassembly directions –X, +X, –Y, +Y, –Z and +Z. All connections between the parts can be removed, and all parts can be disassembled. Originality/value The disassembly constraints are extracted from CAD model of products, which improves the automation of DSP. The disassembly model is useful for reducing the computation of generating the feasible and optimal disassembly sequences. The improved IA converges to the optimal disassembly sequence quickly.

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Parallel disassembly sequence planning using improved ant colony algorithm

Yu-fei

2021

Int J Adv Manuf Technol

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Partial disassembly sequence planning based on Pareto ant colony algorithm

Cited by 4 publications

References 6 publications

Challenges and Prospects of Automated Disassembly of Fuel Cells for a Circular Economy

Challenges and Prospects of Automated Disassembly of Fuel Cells for a Circular Economy

Selective disassembly sequence optimization based on the improved immune algorithm

Parallel disassembly sequence planning using improved ant colony algorithm

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