RETRACTED ARTICLE: Research on control strategy and policy optimal scheduling based on an improved genetic algorithm

Xia, Jing; Yan, Yongcai; Ji, Lei

doi:10.1007/s00521-021-06415-7

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…). The time when the job arrives on the shop floor is the operation ready time of the first operation of job (labeled as ) and is referred to as the release time of the job [21]. A job also has a due date .…”

Section: Problem Definitions For Djssmentioning

confidence: 99%

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Sitahong

Yuan

et al. 2022

Processes

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Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28%, 12.57%, and 15.62% in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.

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Section: Problem Definitions For Djssmentioning

confidence: 99%

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Sitahong

Yuan

et al. 2022

Processes

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“…GAs have been extensively applied in job scheduling [19,27,[30][31][32]. However, most GA applications have not been well explained [33].…”

Section: Existing Xai Techniques For Explaining Ga Applications In Jo...mentioning

confidence: 99%

Novel XAI techniques for explaining GA applications in job scheduling

Chen¹,

Wang

2022

Preprint

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Many evolutionary artificial intelligence (AI) technologies have been applied to assist job scheduling in manufacturing. Among them, genetic algorithms (GAs) are one of mainstream methods. However, GA applications in this field may not be easy to understand or communicate, especially to factory workers without relevant background knowledge, preventing widespread acceptance of such applications. To address this problem, the concept of explanatory AI (XAI) has been proposed. This study first reviews existing XAI techniques for explaining GA applications in job scheduling. Based on the review results, the problems faced by existing XAI techniques are summarized. To solve these problems, this study proposes several novel XAI techniques, including decision tree-based interpretation, dynamic transformation and contribution diagrams, and improved bar charts. To illustrate the effectiveness of the proposed methodology, it has been applied to a case in the literature. According to the experimental results, the proposed methodology can make up for the deficiencies of existing XAI methods in processing high-dimensional data and visualizing the contribution of feasible solutions, thereby satisfying all the requirements for an effective XAI technique for explaining GA applications in job scheduling. Furthermore, the proposed methodology can be easily extended to explain other evolutionary AI applications such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC) in job scheduling.

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“…We also found that there are many deficiencies in the endurance monitoring of Chinese athletes. A core problem at the current stage is that scientific research and training are not closely integrated [8][9][10][11]. Coaches have limited awareness and level of mastering scientific knowledge and using science and technology.…”

Section: Introductionmentioning

confidence: 99%

Endurance Monitoring Method for Rock Climbers Based on Multisensor FDA Model

Dai

2022

Mathematical Problems in Engineering

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The quality of sports performance mainly depends on the scientific degree of sports training. The best monitoring and control of the whole process of sports training is an important guarantee for improving the scientific degree of training. For the scientific monitoring of sports training process, many scholars have carried out research from many aspects. Current research still finds that athletes still have many problems with the protection of their own physical endurance. (1) The monitoring of the sports training process by coaches and athletes is still mainly spontaneous. The monitoring of athletes has not yet formed a system. (2) At present, the monitoring of athletes is mainly carried out from a single discipline or a certain field, and there is no systematic monitoring system. (3) There are many common problems in the monitoring items of the training process in the existing research. Existing studies have relatively little content on specific characteristics. In this paper, a dynamic monitoring system for the training process of elite rock climbers is established, and a training operation mode for rock climbers is established. (1) This paper sorts out the various subsystems of the training system. The monitoring system of sports training process mainly includes target system, personnel organization system, and software and hardware system. In addition, the sports training system proposed in this paper also includes monitoring content system, implementation system, and evaluation system. The sports training process monitoring system also includes a subsystem for monitoring the development of athletic ability of athletes. We take on the training load condition monitoring subsystem; daily physical function state and psychological state monitoring subsystem; and physical and mental health monitoring subsystem. (2) In addition, this paper establishes an operation mode dominated by “scientific research leaders.” The model includes three parts: “Administrative Managers, Scientific Research Leaders, and Coaches.” (3) Finally, this paper establishes five basic guiding ideologies for the evaluation of training process monitoring results. This paper establishes three reference standards for the evaluation of monitoring indicators.

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RETRACTED ARTICLE: Research on control strategy and policy optimal scheduling based on an improved genetic algorithm

Cited by 17 publications

References 4 publications

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Novel XAI techniques for explaining GA applications in job scheduling

Endurance Monitoring Method for Rock Climbers Based on Multisensor FDA Model

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