Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach

Geiger, Christopher D.; Uzsoy, Reha; Aytuğ, Haldun

doi:10.1007/s10951-006-5591-8

Cited by 173 publications

(117 citation statements)

References 40 publications

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“…From 2005 to 2009, GP is applied to more production scheduling problems and researchers become interested in improving the performance of GP. New representations and genetic operators were proposed to cope with specific scheduling problems [38]. Experiments to compare different GP methods were also conducted [59,73].…”

Section: Genetic Programming For Production Schedulingmentioning

confidence: 99%

“…GP has been applied in a wide range of production scheduling problems, ranging from single machine scheduling [30,38,59,100,142], parallel machine scheduling [31,60], to (flexible) job shop scheduling [42,53,59,63,79,81,88,89,95,102, Chen et al [26] Durasevic et al [31] Freitag and Hildebrandt [35] Hart and Sim [42] Karunakaran et al [63] Park et al [115] Park et al [116] Riley et al [123] Branke et al [19] Mei and Zhang [78] Karunakaran et al [64] Masood et al [75] Mei et al [79] Nguyen [84] Nguyen et al [99] Production Scheduling Problems 104,135,136]. Most machines considered in these problems are the same in terms of capability (eligibility to handle a job) and assumptions (e.g.…”

Section: Production Scheduling Problemsmentioning

confidence: 99%

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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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Section: Genetic Programming For Production Schedulingmentioning

confidence: 99%

Section: Production Scheduling Problemsmentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

View full text Add to dashboard Cite

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“…In the optimisation field, genetic programming hyper-heuristics have also been shown to operate on single problem domains. Examples of single domains where generative hyper-heuristics have been applied are 2D bin packing [19], and job shop scheduling [52]. Note that here we refer to the term hyper-heuristics in the context of heuristic generation rather than heuristic selection.…”

Section: Problem Spacementioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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“…Therefore, it is necessary to study the self-learning model. At present, there are many associated researches, but most of them focus on how students should learn English by themselves, master the appropriate learning skills and efficiently use some modern technology and equipment to help them doing self-study [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of an Online English Self-learning Platform

Liu

2017

Int. J. Emerg. Technol. Learn.

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Abstract-This paper discusses the English learning methods and identifies the problems in English teaching and self-learning in China, and then based on the VB platform, it proposes building a platform to facilitate communication between teachers, learners and other participants. At the same time, it adopts the object-oriented development approach to analyze the construction method for the online English self-learning platform and uses UML language to design this platform. At last, this paper uses general testing methods to test the interfaces, functions, performances and security of the VB-based online English selflearning platform and analyzes the performances and deficiencies of the platform based on test results. Keywords-online English; self-learning; B/S; system development IntroductionInterest plays an essential role in English self-learning. Under the guidance of interest, students will take initiatives to search and learn English knowledge. Stimulated by strong interest, a student's nervous system will be very active, and their attention, active thinking and memory will be significantly improved, and as a result, they can receive information well and learn things better [1][2][3]. In order to achieve this purpose, this paper designs an online English self-learning platform, and discusses its application in detail.English learning requires students to take the initiative to learn and have a strong interest in this language. The desire for knowledge can significantly improve students' learning efficiency. If students are aware of this and intentionally carry out such self-learning activities, they will achieve better results [4][5][6]. Under the selflearning model, students will have a more positive understanding of learning and develop a strong passion for knowledge, and in the process, their learning abilities and skills will also improve [7]. Whenever encountering any problem, students will tend to think by themselves rather than learn passively under the teacher's supervision under the traditional model. According to practical experience, this model can significantly increase students' interest and prevent them from getting tired of learning under the traditional learning model. Therefore, it is necessary to study the self-learning model. At present, there are many associated researches, but most of them focus on 4

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Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach

Cited by 173 publications

References 40 publications

Genetic programming for production scheduling: a survey with a unified framework

Genetic programming for production scheduling: a survey with a unified framework

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Design and Application of an Online English Self-learning Platform

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