SmartGantt – An intelligent system for real time rescheduling based on relational reinforcement learning

Palombarini, Jorge A.; Martínez, Ernesto

doi:10.1016/j.eswa.2012.02.176

Cited by 26 publications

(12 citation statements)

References 37 publications

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“…(4) On the theoretical side, we will consider to apply the RL algorithm to problems related to game theory: traffic (Khamisa and Gomaaa, 2014), combat and security (Trejo et al, 2015). (5) Finally, other business area where the RL algorithm can be used is in manufacturing systems, in this case the data collected can be used to learn to improve the production schedule considering the demand (Palombarini and Martinez, 2012 …”

Section: Discussionmentioning

confidence: 99%

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Sánchez

Clempner

Poznyak

2015

Engineering Applications of Artificial Intelligence

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Section: Discussionmentioning

confidence: 99%

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Sánchez

Clempner

Poznyak

2015

Engineering Applications of Artificial Intelligence

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“…Extensive RL-based research, including Refs. [51][52][53][54], has been reported for distinct applicable scenarios. In Ref.…”

Section: Job Dispatching and Schedulingmentioning

confidence: 99%

“…In Ref. [52], a relational RL approach is proposed to obtain policies for efficiently rescheduling production plans, which is able to handle abnormal and unplanned events such as inserting an arriving order. In Ref.…”

Section: Job Dispatching and Schedulingmentioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

284

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Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

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“…The agent‐based approach can provide an efficient solution to the scheduling problem, and it can tackle uncertainties in a real process. As a promising alternative for solving scheduling problems, a great number of agent‐based techniques have been presented (see examples). Correspondingly, various toolkits and frameworks have been developed to facilitate the software development and administration of agent‐based applications …”

Section: Introductionmentioning

confidence: 99%

Efficient scheduling method of complex batch processes with general network structure via agent‐based modeling

2013

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A novel efficient agent-based method for scheduling network batch processes in the process industry is proposed. The agent-based model is based on the resource-task network. To overcome the drawback of localized solutions found in conventional agent-based methods, a new scheduling algorithm is proposed. The algorithm predicts the objective function value by simulating another cloned agent-based model. Global information is obtained, and the solution quality is improved. The solution quality of this approach is validated by detailed comparisons with the mixed-integer programming (MIP) methods. A solution close to the optimal one can be found by the agent-based method with a much shorter computational time than the MIP methods. As a scheduling problem becomes increasingly complicated with increased scale, more specifications, and uncertainties, the advantages of the agent-based method become more evident. The proposed method is applied to simulated industrial problems where the MIP methods require excessive computational resources.A task is selected at the time point of (a) t 1 and (b) t 2 .

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SmartGantt – An intelligent system for real time rescheduling based on relational reinforcement learning

Cited by 26 publications

References 37 publications

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Efficient scheduling method of complex batch processes with general network structure via agent‐based modeling

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