A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem

Rashid, Mohd Fadzil Faisae Ab

doi:10.1108/aa-11-2016-143

Cited by 32 publications

(19 citation statements)

References 45 publications

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“…The Gray Wolf Optimization (GWO) is inspired from social life of wolves and follows four dominant hierarchies such as alpha, beta, gamma and omega which are leaders, subordinates to alpha, subordinates to alpha and beta, and scapegoat role correspondingly [33]. The main aim of using this optimization algorithm can be noted as follow in order to avoid premature convergence of ACO.…”

Section: Cw-dfaco Protocolmentioning

confidence: 99%

“…The objective of DFACO hybridization with Gray Wolf Optimization (GWO) is balancing the path search. In this experimental study we consider three best solutions as alpha, beta and gamma ants as leaders' trials in order to update global pheromone for better exploration in search space [33]. In this aim, a supplementary value of pheromone (w ij ) which obtained from Eq.…”

Section: Cw-dfaco Protocolmentioning

confidence: 99%

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Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Ebadinezhad

2021

SN Appl. Sci.

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This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

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Section: Cw-dfaco Protocolmentioning

confidence: 99%

Section: Cw-dfaco Protocolmentioning

confidence: 99%

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Ebadinezhad

2021

SN Appl. Sci.

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“…The sequence changes once in the installation direction and 2 times in the installation tools. Table vise assembly (Rashid, 2017). Table 9: Optimal assembly sequences generated by the ontology-based approach.…”

Section: Subassembly Local Assembly Sequencementioning

confidence: 99%

“…Optimal assembly sequences generated by the ontology-based approach Rashid (2017). used a hybrid algorithm based on Ant Colony and Grey Wolf optimizers, and the question-answer and precedence graph were used for the acquisition and presentation of assembly experience.…”

mentioning

confidence: 99%

Automatically generating assembly sequences with an ontology-based approach

Zhong

Jiang

Qin

et al. 2019

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Purpose The purpose of this paper is to present and develop an ontology-based approach for automatic generation of assembly sequences. Design/methodology/approach In this approach, an assembly sequence planning ontology is constructed to represent the structure and interrelationship of product geometry information and assembly process information. In the constructed ontology, certain reasoning rules are defined to describe the knowledge and experience. Based on the ontology with reasoning rules, the algorithm for automatically generating assembly sequences is designed and implemented. Findings The effectiveness of this approach is verified via applying it to generate the assembly sequences of a gear reducer. Originality/value The main contribution of the paper is presenting and developing an ontology-based approach for automatically generating assembly sequences. This approach can provide a feasible solution for the issue that mathematics-based assembly sequence generation approaches have great difficulty in explicitly representing assembly experience and knowledge.

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“…Li et al (2013) presented a HA, which combined a modified evolutionary direction operator with PSO, thus showing the effectiveness of the proposed HAs. Rashid (2017) proposed a HA based on ant colony optimization (ACO) and gray wolf optimizer to solve the ASP problem. This new HA successfully overcomes the premature convergence of ACO algorithm and finds optimal solution for ASP.…”

Section: Introductionmentioning

confidence: 99%

Optimizing assembly sequence planning using precedence graph-based assembly subsets prediction method

Zhang

Liu

Qiu

et al. 2019

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Purpose Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms. Design/methodology/approach A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated. Findings The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited. Practical implications The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications. Originality/value The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

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A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem

Cited by 32 publications

References 45 publications

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Automatically generating assembly sequences with an ontology-based approach

Optimizing assembly sequence planning using precedence graph-based assembly subsets prediction method

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