Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment

Đurasević, Marko; Jakobović, Domagoj

doi:10.1007/s10710-017-9302-3

Cited by 44 publications

(36 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Table 12, we can observe that there are seven test scenarios (9,25,29,41,45,57,61) where the dominating proportion on the total dominance of the evolved SPs equals 0. These scenarios can be described as (X,X,N,X,80,2).…”

Section: C(p B) =mentioning

confidence: 99%

“…In the context of solving various types of shop scheduling problems, many machine learning approaches have been applied on this subject [21]. These methods include evolutionary learning [22], gaussian processes [23], imitation learning [24], data mining [25], reinforcement learning [26], artificial neural-networks [27], fuzzy logic [28], ensemble learning [29], and artificial immune networks [30]. However, most of them belong to the category of supervised learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For solving the parallel machine scheduling problem, Ðurasević et al [39] proposed several different GP methods for evolving priority functions, like dimensionally aware GP, GP with iterative dispatching rules and GEP. He [40] also investigated four different ensemble learning approaches to improve the performance of GP for evolving dispatching rules, including simple ensemble combination, BagGP, BoostGP, and cooperative coevolution.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

2019

View full text Add to dashboard Cite

Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the nondominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios. INDEX TERMS Scheduling, flexible job shop, hyper-heuristic, multi-objective, genetic programming. NOMENCLATURE MO-DFJSP multi-objective dynamic flexible job shop scheduling problem MAR machine assignment rule JSR job sequencing rule SP scheduling policy GEP genetic expression programming CCGP cooperative coevolution genetic programming with two sub-populations TTGP genetic programming with single population that a GP individual contains two sub-trees NSGAII nondominated sorting genetic algorithm II SPEA2 strength Pareto evolutionary algorithm 2

show abstract

Section: C(p B) =mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

2019

View full text Add to dashboard Cite

show abstract

“…The slack time SL is shown in equation 18, where CT is the current time. The idle time of a job is presented in equation (19), and the waiting time of a machine is shown in equation 20OBT = max(ORT, MT) ð17Þ…”

Section: Improved Gepmentioning

confidence: 99%

“…Initial attempts have been made to produce hybrid rules manually, which is often time-consuming and not adaptive. In recent years, promising strides have been made to generate hybrid rules using artificial intelligence algorithms such as genetic programming, 19 gene expression programming (GEP), 20,21 and other data mining algorithms. GEP 22,23 was proposed by Ferreira in 2001.…”

Section: Introductionmentioning

confidence: 99%

Dynamic flexible job shop scheduling method based on improved gene expression programming

Zhang

Zhou

Peng

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

Dynamic scheduling is one of the most important key technologies in production and flexible job shop is widespread. Therefore, this paper considers a dynamic flexible job shop scheduling problem considering setup time and random job arrival. To solve this problem, a dynamic scheduling framework based on the improved gene expression programming algorithm is proposed to construct scheduling rules. In this framework, the variable neighborhood search using four efficient neighborhood structures is combined with gene expression programming algorithm. And, an adaptive method adjusting recombination rate and transposition rate in the evolutionary progress is proposed. The test results on 24 groups of instances with different scales show that the improved gene expression programming performs better than the standard gene expression programming, genetic programming, and scheduling rules.

show abstract

Constructing Ensembles of Dispatching Rules for Multi-objective Problems

Đurasević

Planinić

Gil-Gala

et al. 2022

Bio-Inspired Systems and Applications: From Robotics to Ambient Intelligence

View full text Add to dashboard Cite

Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment

Cited by 44 publications

References 36 publications

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling method based on improved gene expression programming

Constructing Ensembles of Dispatching Rules for Multi-objective Problems

Contact Info

Product

Resources

About