Method of predicting carrying time in automatic warehouse system
AssigneeSamsung Electronics Co., Ltd.
InventorKwang-Young Im
Life of this patent
Estimating the term…
Abstract
A method of predicting a carrying time in an automatic warehouse system. Input variables of a real automatic warehouse system are set in a virtual automatic warehouse system. The virtual automatic warehouse system is modeled based upon the set input variables. An optimal weighting value is set according to the modeling. A predicted carrying time is calculated based upon data input corresponding to the set variables and past history data related to a moving time(s) in the automatic warehouse system. An error between the predicted carrying time and a real carrying time in the automatic warehouse system is calculated. An optimal carrying path in the automatic warehouse system is selected according to the predicted carrying time if the error is within a preset allowable range.
Claims11
A method of controlling an automatic warehouse system in a logistics system, comprising:
- setting input carry time variables of the automatic warehouse system in a virtual automatic warehouse system;
- modeling the automatic warehouse system in the virtual automatic warehouse system based upon the set carry time variables;
- setting an optimal weighting value according to the modeling;
- calculating a predicted carrying time based upon the optimal weighting value, input data corresponding to the set carry time variables and past history data related to carrying times in the automatic warehouse system;
- calculating an error between the predicted carrying time and a real carrying time in the automatic warehouse system;
- and
- selecting an optimal carrying path in the automatic warehouse system according to the predicted carrying time if the error is within a preset allowable range.
An automatic warehouse system receiving a carrying command from a logistics control system, the automatic warehouse system comprising:
- a programmed computer processor in response to the carrying command calculating a predicted carrying coefficient by modeling the automatic warehouse system applying a neural networks theory to carrying-time-influencing variables, and applying the predicted carrying coefficient to the logistics control system, thereby selecting an optimal carrying path for the carrying command.
An automatic warehouse system receiving a carrying command from a logistics control system, the automatic warehouse system comprising:
- a programmed computer processor generating a virtual moving coefficient based upon carry-time-influencing variables of the automatic warehouse system and a neural networks theory, and applying the virtual moving coefficient to the logistics control system, thereby selecting an optimal carrying path for the carrying command.
A method of controlling an automatic warehouse system in a logistics system, comprising:
- setting an optimal weighting value by applying a neural networks theory to carrying-time-influencing variables of the automatic warehouse system;
- calculating a predicted carrying time based upon the optimal weighting value, input data of the carrying-time-influencing variables and past history data of carrying times;
- and
- selecting an optimal carrying path in the automatic warehouse system according to the predicted carrying time.
An automatic warehouse system, comprising:
- a programmed computer processor modeling a virtual automatic warehouse system to be approximate to the automatic warehouse system based upon input and output carry time variables of the automatic warehouse system using a neural networks control theory, and selecting an optimal carrying path in the automatic warehouse system based upon the modeling.
A logistics control system, comprising:
- a logistics controller;
- and
- an automatic warehouse system being controlled by the logistics controller, the automatic warehouse system comprising:
- a programmed computer processor modeling a virtual automatic warehouse system to be approximate to the automatic warehouse system based upon input and output carry time variables of the automatic warehouse system using a neural networks control theory, and finding an optimal carrying path in the automatic warehouse system based upon the modeling, thus allowing optimal logistics flow between the logistics controller and the automatic warehouse system.
Dependencies are inferred from the claim text.