A genetic algorithm for the multi-objective optimization of mixed-model assembly line based on the mental workload

Zhao, Xinyi; Hsu, Chia‐Yu; Chang, Pei‐Chann; Li, Li

doi:10.1016/j.engappai.2015.03.005

Cited by 33 publications

(14 citation statements)

References 31 publications

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“…The implementation is tested on a case study in Dalian City, China. Zhao et al (2016) implemented a GA to minimize the mental workload of human operators in the mixed-model assembly line based on many factors such as the assembly complexity and operator experience. The motivation of this work is to reduce the errors resulting from human mental fatigue and to improve the efficiency of the assembly line.…”

Section: Ga Implementationmentioning

confidence: 99%

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Cio

Vadean

et al. 2020

AIEDAM

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The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.

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Section: Ga Implementationmentioning

confidence: 99%

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Cio

Vadean

et al. 2020

AIEDAM

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“…Metaheuristic algorithms is a stochastic optimizer programming that is capable of solving multi-objective optimization problems. The algorithms can manage multi-objective problems with a set of possible solutions simultaneously [5]. The algorithms can find the near-optimal solution in a single run as compared to traditional techniques, which need to be executed in a series of separate runs.…”

Section: Introductionmentioning

confidence: 99%

Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Razali¹,

Rashid²,

Make³

2020

JMMST

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Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem which requires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP is conducted using four popular metaheuristics for this problem, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Three categories of test problem (small, medium, and large) were used ranging from 8 to 100 number of tasks. For computational experiment, MATLAB software is used in investigate the metaheuristic algorithms performances to optimize the designated objective functions. The results reveal that ACO algorithm performed better in term of finding the best fitness functions when dealing with a large number of tasks. Averagely, it has produces better solution quality than PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO more superior in term of processing time compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%. Hence, future research directions such as using the actual manufacturing assembly line data to test the algorithm performances are likely to happen.

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“…The reason for errors, crashes, accidents and disasters made by human can be due to unbalance mental workload resulting in overload and underload situations exposing operators to approach or exceed the redlines of their performance (Xie and Salvendy, 2000[ 36 ]; Paxion et al, 2014[ 27 ]; Young et al, 2015[ 39 ]; Wascher et al, 2016[ 33 ]). On the other hand, the balance in the workload reduces the human error and increases the task performance of operators (Xie and Salvendy, 2000[ 36 ]; Yu et al, 2016[ 40 ]; Zhao et al, 2016[ 41 ]). Therefore, the concept of mental workload and mechanism of its effect on task performance in different human-machine systems is considered by practitioners and researchers in a variety of cognitive activities, such as conventional driving (Allahyari et al, 2014[ 1 ]; Hassanzadeh-Rangi et al, 2014[ 18 ]; Yan et al, 2019[ 37 ]), automated driving (Ko and Ji, 2018[ 22 ]), train driving (Balfe et al, 2017[ 6 ]), nuclear power plants (Choi et al, 2018[ 12 ]), advanced surgery programs (Cavuoto et al, 2017[ 9 ]), air traffic monitoring (Dasari et al, 2017[ 14 ]), control rooms (Melo et al, 2017[ 24 ]), workplace activities (Chen et al, 2017[ 11 ]), information technologies (Buettner, 2017[ 7 ]) and other complex human-machine systems (Xiao et al, 2015[ 35 ]).…”

Section: Introductionmentioning

confidence: 99%

Human-based dynamics of mental workload in complicated systems

Jafari

Zaeri

Jafari

et al. 2019

EXCLI Journal; 18:Doc501; ISSN 1611-2156

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A genetic algorithm for the multi-objective optimization of mixed-model assembly line based on the mental workload

Cited by 33 publications

References 31 publications

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Human-based dynamics of mental workload in complicated systems

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