AGV Transfer Point Routing for Dynamic Logistics Re-Optimization
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Solution Overview
Problem
Existing logistics systems with autonomous transport vehicles (AGVs) fail to efficiently adapt to unscheduled status changes and traffic jams due to unscheduled behavior at transfer points, leading to inefficiencies and potential traffic congestion.
Innovation Solution
A logistics center with a controller that integrates a self-learning AI to create optimized order and transport plans, dynamically adjust routes and transfer points, and incorporate unscheduled status messages to enhance efficiency, using a centralized control system for heterogeneous fleets and building services technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static boundary conditions are used for transfer points and transfer times in optimization, then the optimization process is simpler, but efficiency losses occur when handovers are not possible due to defects or unscheduled behavior
Solution Approach 1:
The patent applies dynamics by transforming static boundary conditions into dynamic, adaptable parameters. The optimization process now considers multiple alternative handover points and dynamically selects among them based on real-time status messages from AGVs. This allows the system to adapt to unscheduled events like defects or traffic jams, resolving the contradiction between optimization simplicity and operational efficiency.
Solution Approach 2:
The system implements feedback by continuously monitoring status messages from AGVs during transport operations. When unscheduled status messages indicate problems at transfer points (such as defects or traffic jams), the feedback triggers re-optimization of transport plans to select alternative handover points. This feedback mechanism enables the system to maintain high efficiency while managing optimization complexity through event-driven adjustments.
2Loss of time
If alternative handover points are not considered in the transport plan, then the planning process is faster, but traffic jams cannot be avoided when handover at the planned point is not possible
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing multiple alternative transport plans with different handover points before actual transport operations begin. When unscheduled status messages indicate problems at the originally planned handover point, the system can immediately switch to a pre-prepared alternative plan without time-consuming re-optimization. This resolves the contradiction by investing time upfront in creating alternatives that enable fast, reliable responses to unexpected events.
Solution Approach 2:
The system changes parameters by maintaining multiple transport plan variants with different handover point configurations. Instead of relying on a single static plan, the system keeps several plans with varying parameters (different handover points, routes, and timing) ready for selection. This parameter diversity allows the system to quickly adapt to unscheduled events while keeping the overall planning process efficient.
3Device complexity
If the controller does not re-execute optimization when status messages deviate from expected status, then the control system is simpler, but the system cannot adapt to unscheduled events and defects
Solution Approach 1:
The patent implements feedback by establishing a monitoring mechanism that compares expected status messages with actual status messages from AGVs. When deviations are detected (indicating unscheduled events like defects or traffic jams), the feedback triggers selective re-execution of the optimization process. This feedback-driven approach balances control system complexity with adaptability, re-optimizing only when necessary rather than continuously or never.
Solution Approach 2:
The control system applies dynamics by transitioning from a static, fixed optimization approach to a dynamic, condition-based re-optimization approach. The system adapts its level of optimization re-execution based on real-time operational conditions indicated by status message deviations. This dynamic behavior enables the system to maintain simplicity during normal operations while becoming highly adaptable when unscheduled events occur.
Data Source
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AI summary
The invention relates to a logistics center with stationary conveyor technology (10) with several transfer points (10a, 10b, 10c) and a fleet of autonomous transport vehicles (AGVs) (12a, 12b, 12c) for transporting goods from or to the transfer points (10a, 10b, 10c) of the conveyor technology (10) and a controller (16) for controlling the conveyor technology (10) and the autonomous transport vehicles, wherein the controller (16) is designed to create an optimized order and transport plan in an optimization process, to assign orders to the AGVs (12a, 12b, 12c) according to the optimized order and transport plan and to send instructions for travel routes to the AGVs (12a, 12b, 12c), to receive and process status messages from the AGVs and in the event of unscheduled status messages from one or more AGVs (12a, 12b, 12c) to carry out the optimization process and to change or create a new optimized order and transport plan.It is proposed that the controller (16) is integrated into an Extended Warehouse Management (EWM) system and accesses data from the Extended Warehouse Management (EWM) system and includes a self-learning AI that takes unscheduled status messages into account in future optimization processes.