Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line

Ullah, Saif; Guan, Zailin; Liu, Weiqi; Wang, Baoxi; Zhang, Chaoyong

doi:10.1007/s00170-014-6153-4

Cited by 43 publications

(19 citation statements)

References 46 publications

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“…Although there exist a number of optimization algorithms, they might not be able to solve the considered problem directly and hence this research mainly re-implements the methods applied to RUALBP and other assembly line balancing problems. The methods considered for comparative study are: simulated annealing algorithm (SA) (Khorasanian et al, 2013), particle swarm optimization (PSO1) (Hamta et al, 2013), particle swarm optimization (PSO2) (Li et al, 2016a), genetic algorithm (GA) (Levitin et al, 2006), teaching-learning-based optimization algorithm (TLBO) (Tang et al, 2015), artificial bee colony algorithm (ABC) (Saif et al, 2014), discrete cuckoo search (DCS) (Li et al, 2017a).…”

Section: Experimental Designmentioning

confidence: 99%

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

Janardhanan

Ashour

et al. 2019

Neural Comput & Applic

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Modern assembly line systems utilize robotics to replace human resources to achieve higher level of automation and flexibility. This work studies the task assignment and robot allocation in a robotic U-shaped assembly line. Two new mixed integer programming linear models are developed to minimize the cycle time when the number of workstations is fixed. Recently developed migrating birds optimization (MBO) algorithm is employed and improved to solve large-sized problems. Problemspecific improvements are also developed to enhance the proposed algorithm including modified consecutive assignment procedure for robot allocation, iterative mechanism for cycle time update, new population update mechanism and diversity controlling mechanism. An extensive comparative study is carried out to test the performance of the proposed algorithm, where seven high-performing algorithms recently reported in the literature are re-implemented to tackle the considered problem. The computational results demonstrate that the developed models are capable to achieve the optimal solutions for small-sized problems, and the proposed algorithm with these proposed improvements achieves excellent performance and outperforms the compared ones.

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Section: Experimental Designmentioning

confidence: 99%

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

Janardhanan

Ashour

et al. 2019

Neural Comput & Applic

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“…To test the performance of the proposed C-PSO, five other competitive algorithms are selected, including PSO algorithm, a GA, 29 artificial bee colony (ABC) algorithm, 30 SA algorithm, 31 and a co-evolutionary genetic algorithm (C-GA). 29 PSO algorithm shares the same procedure as the C-PSO except for utilizing best individual of the other sub-swarm, and PSO has only one swarm.…”

Section: Experimental Designmentioning

confidence: 99%

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Janardhanan

Tang

et al. 2016

Advances in Mechanical Engineering

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Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the twosided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial-hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed coevolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

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“…There are two main problems in mixed model assembly lines, one is sequencing of different models, and the other is balancing of assembly line. Saif et al [15] proposed the multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line to overcome these problems.…”

Section: Introductionmentioning

confidence: 99%

“…The quality loss functions were used to quantify noncomparable and possibly conflicting performance criteria in their study. In the research by Bryan et al [15], an innovative method for the concurrent design of a product portfolio and its corresponding assembly system was presented, which could lead to the minimum of oversupply in differentiating modules and the maximum of the efficiency in the assembly line. There are two main problems in mixed model assembly lines, one is sequencing of different models, and the other is balancing of assembly line.…”

Section: Introductionmentioning

confidence: 99%

The Method for Risk Evaluation in Assembly Process based on the Discrete-Time SIRS Epidemic Model and Information Entropy

Dai

et al. 2019

Entropy

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In the past decade years, much attention has been attached on assembly process reliability in manufacturing system, because the quality and cost of product are highly determined by assembly process. However, existing research on reliability in assembly are mainly focused on study of size deviation propagation. In this paper, the method for risk evaluation in assembly process based on the discrete-time SIRS epidemic model and information entropy was proposed. Firstly, aiming at the issue of assembly process optimization, innovative solutions are proposed from the perspectives of reliability and cost by decomposing the assembly into general path and rework path. Secondly, the propagation mechanism of defects in optimal assembly approach were studied through combining the infectious disease model and information entropy. According to the bifurcation phenomenon in the SIRS model, the entropy increment of assembly process ∆H base when defect emergence occurs is calculated. Thirdly, the information entropy increment of optimal assembly approach ∆H is used to evaluate the assembly risk by comparing with the ∆H base . Finally, a case study of assembly risk evaluation for the oil pump was presented to verify the advantage of this method.

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Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line

Cited by 43 publications

References 46 publications

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

The Method for Risk Evaluation in Assembly Process based on the Discrete-Time SIRS Epidemic Model and Information Entropy

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