Dynamic wAGV Scheduling for Real-Time Port Task Insertion
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Solution Overview
Problem
Current scheduling algorithms for autonomous waterborne inter-terminal transportation between terminals in large ports are inefficient due to their static nature and failure to consider dynamic changes, complex geographical layouts, and the specific requirements of waterway transportation, such as non-Euclidean distances and real-time task adjustments.
Innovation Solution
A dynamic scheduling model based on combinatorial optimization and mixed integer programming, combined with a rolling horizon framework and tabu search heuristic, to optimize paths for waterborne autonomous guided vessels (wAGVs) in real-time, considering performance indexes like distance, time windows, and capacity constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If exact algorithms such as dynamic programming are used to solve scheduling problems, then optimal solutions can be obtained, but computational time becomes too long and is only suitable for small-scale static scheduling problems
Solution Approach 1:
The patent applies dynamics by transitioning from static exact algorithms to a dynamic heuristic approach. The rolling horizon method continuously updates scheduling decisions as new information becomes available, while the tabu search heuristic adapts its search strategy based on the current solution state. This dynamic approach allows the system to handle large-scale dynamic scheduling problems in real-time while maintaining solution quality.
Solution Approach 2:
The patent changes the parameter of solution quality from requiring exact optimality to accepting quasi-optimality. By using heuristic algorithms with controlled randomness and adaptive search strategies, the system achieves sufficiently good solutions much faster than exact algorithms, trading a small amount of solution precision for significant computational efficiency gains.
2Productivity
If heuristic algorithms are used for dynamic scheduling, then computational efficiency is improved, but only quasi-optimal solutions can be produced
Solution Approach 1:
The patent applies preliminary action through the rolling horizon method, which pre-calculates schedules for a finite time horizon in advance. By looking ahead and planning multiple steps forward rather than making myopic one-step decisions, the heuristic algorithm achieves better solution quality while maintaining computational efficiency. This preliminary planning allows the system to anticipate future constraints and opportunities.
Solution Approach 2:
The patent incorporates feedback mechanisms in the tabu search algorithm, where the search process learns from previous iterations and adjusts its strategy accordingly. The tabu list prevents cycling by remembering recently visited solutions, and the adaptive mechanisms use feedback from the objective function to guide the search toward better regions of the solution space, improving solution quality without sacrificing efficiency.
3Ease of operation
If existing scheduling models are used for land transportation, then scheduling can be performed, but they are not applicable to waterborne transportation with complex geographical layouts and non-Euclidean distances
Solution Approach 1:
The patent applies local quality by customizing the distance metric and routing constraints specifically for waterborne transportation. Instead of using generic Euclidean distances appropriate for land transportation, the model incorporates water-specific characteristics such as non-Euclidean waterway distances, terminal-specific access constraints, and navigation requirements. This localized adaptation makes the scheduling model truly applicable to waterborne environments.
Solution Approach 2:
The patent achieves universality by creating a flexible scheduling framework that can handle multiple transportation modes and geographical configurations. The model is designed to be adaptable to different port layouts, waterway networks, and terminal types, making it universally applicable to various waterborne transportation scenarios while maintaining the core optimization logic.
4Device complexity
If static scheduling models are used, then implementation is simpler, but they cannot handle dynamically arriving transportation tasks and real-time adjustments
Solution Approach 1:
The patent transforms the static scheduling model into a dynamic one through the rolling horizon approach. Instead of creating a complete schedule at the beginning and executing it without changes, the model continuously re-optimizes schedules as new transportation tasks arrive and as the system state evolves. This dynamic capability allows the system to handle real-time adjustments while maintaining manageable complexity through structured re-planning.
Data Source
AI summary
The present application discloses is a scheduling method and system for fully autonomous waterborne inter terminal transportation, and belongs to the field of transportation. The method includes: establishing a dynamic scheduling model for waterborne Autonomous guided vessels (wAGVs); quickly inserting the dynamically arriving transportation tasks into all the existing wAGV paths, calculating the insertion cost and selecting the path and position with the lowest insertion cost to obtain updated initial paths; improving the initial path using a heuristic algorithm based on tabu search to obtain quasi-optimal wAGV paths; executing the scheduling wAGV paths.