Warehouse Trolley Control Using Predictive Mission Assignment
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Solution Overview
Problem
Current warehouse handling systems fail to optimize product flow globally due to passive detection of trolley positions and lack of real-time data integration, leading to inefficient mission assignment and suboptimal route planning.
Innovation Solution
A computer-implementable method that continuously acquires real-time data on trolley positions and handling operations, applies a predictive model to simulate ideal operating conditions, and assigns missions to minimize overall performance time by actively detecting trolley positions and evaluating all operations simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If passive position detection based on mission completion is used, then system complexity is reduced, but position accuracy and real-time awareness deteriorate
Solution Approach 1:
The patent implements active feedback mechanisms where trolleys continuously report their position to the central control unit through onboard sensors and communication systems. This real-time feedback enables the control system to maintain accurate knowledge of all trolley positions, which is essential for optimizing mission assignment and avoiding conflicts between multiple trolleys operating simultaneously in the warehouse.
2Productivity
If individual trolley optimization is performed, then local efficiency is improved, but global optimization capability deteriorates
Solution Approach 1:
The patent merges the optimization decisions for all trolleys into a single centralized control system that considers the state and mission assignments of all trolleys simultaneously. This global optimization approach assigns missions to multiple trolleys in coordination, considering their respective positions, capabilities, and current tasks, thereby optimizing overall warehouse productivity rather than just individual trolley efficiency.
3Productivity
If real-time data acquisition and predictive modeling are implemented, then handling flow optimization is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The patent applies predictive modeling to forecast future trolley positions and mission completion times based on current data. This preliminary action allows the central control unit to anticipate the state of the warehouse and proactively optimize mission assignments before actual changes occur, improving handling flow efficiency by preparing optimized schedules in advance rather than reacting to real-time conditions alone.
Data Source
AI summary
A computer-implementable method for controlling the handling of products in a warehouse is performed by acquiring a first item of data identifying a real-time position of a plurality of trolleys (A, B, C) inside the warehouse and a second item of data identifying a plurality of operations to be performed (1-9). A predictive model is applied to such data, generating a simulation of the handling of the trolleys (A, B, C) inside the warehouse during the performance of respective handling operations. As a function of such a simulation, a unique association is determined between each trolley and a respective group of operations to be performed (1-9) such as to minimise the overall time for the performance of the plurality of handling operations, which is then communicated to the individual trolleys (A, B, C). The present invention further relates to a system configured to carry out a method for controlling the handling of products in a warehouse.