A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes

Wang, Hui; Sheng, Buyun; Lu, Qibing; Yin, Xiyan; Zhao, Feiyu; Lu, Xincheng; Luo, Ruiping; Fu, Gaocai

doi:10.1007/s00500-020-05347-z

“…In the future, we will explore in at least two directions based on proposed method. The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided Generative Adversarial Dehazing Network

Zhang

¹

,

Song

²

2021

Preprint

0

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Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

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“…In the future, we will explore in at least two directions based on proposed method. The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

Zhang

¹

,

Qian

²

,

Song

³

2021

Soft Comput

4

0

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Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

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“…Nevertheless, there are a few exceptions. Some authors [37,38,85,[87][88][89][90][91][92] schedule transport and take into account the energy required to load/unload vehicles, and others [93][94][95][96][97][98] consider the energy required to transport the jobs between geographically distributed facilities.…”

Section: Pp = Maxmentioning

confidence: 99%

“…The transportation imposes its own additional energy requirements, as vehicles need to move around the shop floor. Transportation may also lead to a more energy-consuming schedule due to the required synchronization of machines and vehicles [37,87,88,90,94]. The vast majority of the studies either disregard transportation or consider that transport time is included in the processing time; consequently, a job can be processed on the next machine immediately after its completion on the previous one.…”

Section: Additional Scheduling Problemsmentioning

confidence: 99%

“…The performance of machines degrades, naturally, as a result of aging and wear, which leads to a decrease in performance (quality and speed) and reliability (faults and failures). Thus, some authors consider preventive maintenance, which has to be scheduled together with the manufacturing operations [8,73,87,105]. The purpose of such integration is twofold: to maintain the performance of machines at an acceptable level (which also reduces the energy consumption), and to minimize the energy consumed by maintenance activities.…”

Section: Additional Scheduling Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

Fernandes

¹

,

Homayouni

²

,

Fontes

³

2022

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Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes

Cited by 43 publications

References 39 publications

GGADN: Guided Generative Adversarial Dehazing Network

GGADN: Guided Generative Adversarial Dehazing Network

GGADN: Guided generative adversarial dehazing network

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

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