Solving the energy-efficient Robotic Mixed-Model Assembly Line balancing problem using a Memory-Based Cuckoo Search Algorithm

Belkharroubi, Lakhdar; Yahyaoui, Khadidja

doi:10.1016/j.engappai.2022.105112

Cited by 13 publications

(1 citation statement)

References 30 publications

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“…The CS algorithm attracts attention due to its simplicity and efficiency over GA because of using the Lévy flights instead of isotropic random walks [40]. Recently proposed CS algorithm [41] for an energy-efficient robotic mixed-model assembly line balancing problem outperforms GA in terms of obtained objective values. In [42], the CS algorithm employs theoretical anomaly generated by a single structure and different field data sets to estimate the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Algorithms to Optimal Parameters Estimation of a Model of Two-Stage Anaerobic Digestion of Corn Steep Liquor

Roeva

Chorukova

2022

Applied Sciences

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Anaerobic Digestion (AD) of wastewater for hydrogen production is a promising technology resulting in the generation of value-added products and the reduction of the organic load of wastewater. The Two-Stage Anaerobic Digestion (TSAD) has several advantages over the conventional single-stage process due to the ability to control the acidification phase in the first bioreactor, preventing the overloading and/or the inhibition of the methanogenic population in the second bioreactor. To carry out any process research and process optimization, adequate mathematical models are required. To the best of our knowledge, no mathematical models of TSAD have been published in the literature so far. Therefore, the authors’ motivation is to present a high-quality model of the TSAD corn steeping process for the sequential production of H2 and CH4 considered in this paper. Four metaheuristics, namely Genetic Algorithm (GA), Firefly Algorithm (FA), Cuckoo Search Algorithm (CS), and Coyote Optimization Algorithm (COA), have been adapted and implemented for the first time for parameter identification of a new nonlinear mathematical model of TSAD of corn steep liquor proposed here. The superiority of some of the algorithms has been confirmed by a comparison of the observed numerical results, graphical results, and statistical analysis. The simulation results show that the four metaheuristics have achieved similar results in modelling the process dynamics in the first bioreactor. In the case of modelling the second bioreactor, a better description of the process dynamics trend has been obtained by FA, although GA has acquired the lowest value of the objective function.

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Section: Introductionmentioning

confidence: 99%

Metaheuristic Algorithms to Optimal Parameters Estimation of a Model of Two-Stage Anaerobic Digestion of Corn Steep Liquor

Roeva

Chorukova

2022

Applied Sciences

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Using parallel metaheuristics to solve a parallel U-shaped robotic mixed-model assembly line balancing and sequencing problem

Farsi,

Mokhtarzadeh,

Rabbani

et al. 2024

Soft Comput

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Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption

Soysal-Kurt,

İşleyen,

Gökçen

2024

Flex Serv Manuf J

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As technology advances, the integration of robots in the assembly line has become widespread. While robots offer numerous benefits, such as increased productivity and improved product quality, they also result in higher energy usage. Finding the optimal line balance while considering energy consumption is a challenging task in a robotic assembly line that produces multiple product models in a mixed sequence. This paper addresses the mixed-model parallel robotic assembly line balancing and model sequencing (MPRALB/S) problem. The objectives of this problem are to minimize cycle time and energy consumption. The authors have not found any existing research on this topic in the literature. To solve the MPRALB/S problem, a modified non-dominated sorting genetic algorithm II (MNSGA-II) is developed. Since there is no existing benchmark data for the MPRALB/S problem, new datasets are generated for this study. The MPRALB/S problem is illustrated through a numerical example. The performance of MNSGA-II is evaluated with non-dominated sorting genetic algorithm II (NSGA-II) and restarted simulated annealing through commonly used performance metrics including hypervolume ratio (HVR), ratio of non-dominated solutions (RP) and generational distance (GD). According to the results of the computational study, MNSGA-II outperforms NSGA-II in approximately 81% of the problem instances for HVR, 71% for RP, and 76% for GD. The results show that MNSGA-II is an effective approach for solving the MPRALB/S problem and achieves competing performance compared to other algorithms.

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Solving the energy-efficient Robotic Mixed-Model Assembly Line balancing problem using a Memory-Based Cuckoo Search Algorithm

Cited by 13 publications

References 30 publications

Metaheuristic Algorithms to Optimal Parameters Estimation of a Model of Two-Stage Anaerobic Digestion of Corn Steep Liquor

Metaheuristic Algorithms to Optimal Parameters Estimation of a Model of Two-Stage Anaerobic Digestion of Corn Steep Liquor

Using parallel metaheuristics to solve a parallel U-shaped robotic mixed-model assembly line balancing and sequencing problem

Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption

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