Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in semiconductor manufacturing, which is characterized by high complexity and dynamics. Many dispatching rules have been found, which perform ll on different scenarios, hover no rule has been found, which outperforms other rules across various objectives. To tackle this drawback, approaches, which select dispatching rules depending on the current system conditions, have been proposed. Most of these use learning techniques to switch beten rules regarding the current system status. Since the study of Rasmussen [1] has shown that Gaussian processes as a machine learning technique have outperformed other techniques like neural networks under certain conditions,  propose to use them for the selection of dispatching rules in dynamic scenarios. Our analysis has shown that Gaussian processes perform very ll in this field of application. Additionally,  shod that the prediction quality Gaussian processes provide could be used successfully.
['Bernd Scholz-Reiter', 'Jens Heger', 'Torsten Hildebrandt']
I am writing a research paper on a idea Gaussian Processes for Dispatching Rule Selection in Production Scheduling: Comparison of Learning Techniques
[{"from": "human", "value": "I am writing a research paper on a idea Gaussian Processes for Dispatching Rule Selection in Production Scheduling: Comparison of Learning Techniques"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in semiconductor manufacturing, which is characterized by high complexity and dynamics. Many dispatching rules have been found, which perform ll on different scenarios, hover no rule has been found, which outperforms other rules across various objectives. To tackle this drawback, approaches, which select dispatching rules depending on the current system conditions, have been proposed. Most of these use learning techniques to switch beten rules regarding the current system status. Since the study of Rasmussen [1] has shown that Gaussian processes as a machine learning technique have outperformed other techniques like neural networks under certain conditions,  propose to use them for the selection of dispatching rules in dynamic scenarios. Our analysis has shown that Gaussian processes perform very ll in this field of application. Additionally,  shod that the prediction quality Gaussian processes provide could be used successfully."}]