An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance

Zhou, Guanghui; Chen, Zhenghao; Zhang, Chao; Chang, Fengtian

doi:10.1016/j.jclepro.2022.130541

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…About 14% of the papers reviewed propose DS approaches, which were implemented by resorting to heuristic methods (5 papers were published in 2018 or earlier, and 19 were published in and after 2019). DS has been implemented by using two main strategies to react to real-time events: full schedule reconstruction [21] and specific schedule changes addressing only the disrupted resources and impacted tasks (e.g., [57,92,123,124]).…”

Section: Heuristic Methodsmentioning

confidence: 99%

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

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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Section: Heuristic Methodsmentioning

confidence: 99%

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

2022

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“…This section takes DTMC as basis, on which a novel DTdriven bi-level adaptive control architecture enabled by an automatic programming method is proposed for DTMC. The proposed architecture aims to automatically map a batch of scheduled jobs generated by a dynamic scheduling strategy [23] into a group of executable control programs for autonomously controlling of DTMC. In addition, the architecture also provides an adaptive program adjustment mechanism to quickly adapt to dynamic changes in the scheduling scheme during the job execution process.…”

Section: Dt-driven Bi-level Adaptive Control Architecturementioning

confidence: 99%

“…; δint(t) is the internal deviation caused by the changes in production conditions of DTMC at time t, such as tool wear and device failure. The real-time deviation of δext(t) and δint(t) could be derived by a dynamic scheduling model [23]. Bi-level adaptive control architecture for DTMC.…”

Section: B Design Of Bi-level Architecturementioning

confidence: 99%

“…4, a hardware and software integrated development environment is constructed based on the reference framework of DTMC, which serves as the basis for BE-DTMC implementation. In addtion, an advanced production scheduling system and a dynamic scheduling model [23] are embedded into social space and knowledge space, respectively, to generate a scheduling/ rescheduling scheme that is taken as input for autonomously controlling of BE-DTMC. The onstruction details of the development environment could be found in [7], [10], [22].…”

Section: A Development Environmentmentioning

confidence: 99%

“…To bridge the gap, this paper takes a digital twin manufacturing cell (DTMC) introduced in our previous works [7,10,22] as basis, on which a novel DT-driven adaptive control mechanism enabled by an automatic programming method is proposed and embedded into DTMC. The proposed approach takes a real-time task scheduling scheme generated by a dynamic scheduling strategy proposed in our previous work [23] as input. Then, a batch of jobs in the scheduling scheme are automatically mapped into a group of control programs through a high-fidelity simulation process empowered by a behavior model network (BMN) and a set of event models (EMs) embedded in DTMC.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells

et al. 2022

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The booming personalized and customized demands of customers in Industry 4.0 pose great challenges for manufacturing enterprises in terms of flexibility and responsiveness. Nowadays, many effective dynamic scheduling approaches have been proposed for manufacturing systems to quickly respond to changes in customer demands, where, however, the implementation of an automatic programming method with high control accuracy and low control delay is still challenging. The above unaddressed issue brings about a lot of labor-intensive and time-consuming manual offline programming work when adjusting the scheduling scheme to meet dynamic customer demands, resulting in limited flexibility and responsiveness in current manufacturing systems. To bridge this gap, a bi-level adaptive control architecture enabled by an automatic programming method is proposed and embedded into a digital twin manufacturing cell (DTMC). The bi-level architecture aims to automatically map an input task scheduling scheme with a batch of jobs into a group of control programs through a behavior model network and a set of event models embedded in DTMC. It also provides an adaptive program modification mechanism to quickly adapt to the dynamic adjustment of the scheduling scheme caused by the changing of customer demands or production conditions, thus equipping DTMC with strong flexibility and responsiveness. Based on the bi-level architecture, a DTMC prototype system is developed, where its application and evaluation results demonstrate the feasibility and effectiveness of the proposed approach.

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Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review

Zhang,

Wang,

Zhou

et al. 2023

Advanced Engineering Informatics

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An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance

Cited by 16 publications

References 36 publications

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

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

A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells

Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review

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