Comparisons of metaheuristic algorithms for unrelated parallel machine weighted earliness/tardiness scheduling problems

Arık, Oğuzhan Ahmet

doi:10.1007/s12065-019-00305-7

Cited by 14 publications

(12 citation statements)

References 17 publications

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“…To evaluate and show the efficiency of proposed solution approaches, we apply 3 metaheuristic algorithms from the literature. These are ABC, SA, and GA algorithms proposed by Arık (2020) and FR&R algorithm proposed by Lin et al (2016). The study of Arık (2020) investigated the same problem without optimizing the start times of machines and it is a recent study.…”

Section: Experimental Studymentioning

confidence: 99%

“…Beyranvand et al (2012) improve the quadratic programming model of Plateau and Rios-Solis (2010) for the same problem. Arık (2020) proposes a Simulated Annealing (SA), an Artificial Bee Colony (ABC), and a Genetic Algorithm (GA) for the problem. Experimental results in his study show that ABC among all proposed metaheuristics outperforms other metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results in his study show that ABC among all proposed metaheuristics outperforms other metaheuristics. To our best knowledge, far the unrelated parallel machine environment, the best metaheuristics reported so are ABC of Arık (2020) and the best mathematical model is reported as quadratic programming model of Beyranvand et al (2012).…”

Section: Introductionmentioning

confidence: 99%

“…Despite being common in practice, the paper son unrelated machine scheduling receives relatively less attention. The known approaches are quadratic programming (Beyranvand et al, 2012) and general-purpose solution approaches such as GA, SA, and ABC (Arık, 2020). Our paper contributes unrelated parallel machine scheduling by proposing a solution construction and improvement algorithm considering known properties of the optimal schedule for the problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Weighted earliness/tardiness parallel machine scheduling problem with a common due date

Arık

Schutten

Topan

2022

Expert Systems with Applications

Self Cite

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Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weighted earliness/tardiness parallel machine scheduling problem with a common due date

Arık

Schutten

Topan

2022

Expert Systems with Applications

Self Cite

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“…In [241] the authors consider the problem of minimising the weighted tardiness and earliness criterion with a common due date. The authors tested three metaheuristics, ABC, GA, and SA to examine which kind of metaheuristic type (swarm intelligence, evolutionary algorithm, single solution algorithm) achieves the best results.…”

Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling problem (UPMSP), which is only one of the many different problem types that exist, found its application in many areas like production industries or distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods are gaining more attention for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with this problem. During that time, many different problem variants, solution methods, or other interesting research directions were considered. However, no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP. The goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. The research was systematised and classified into several categories to enable an easy overview of the different problem and solution variants. Additionally, current trends and possible future research directions are also shortly outlined.

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Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Xia

Guo

et al. 2022

Landslides

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Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility.

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Comparisons of metaheuristic algorithms for unrelated parallel machine weighted earliness/tardiness scheduling problems

Cited by 14 publications

References 17 publications

Weighted earliness/tardiness parallel machine scheduling problem with a common due date

Weighted earliness/tardiness parallel machine scheduling problem with a common due date

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

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