Railway Scheduling Machine Optimizing Train Paths
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Solution Overview
Problem
Current railway scheduling methods are inefficient in minimizing travel times and avoiding deadlocks, particularly in large networks with many trains, as they require significant human intervention and can lead to prolonged engine idling and increased costs due to manual errors and computational demands.
Innovation Solution
A railway system with a scheduling machine that uses a model of the network to optimize train paths and timings, applying state data to determine controls for minimizing travel time and preventing deadlocks, utilizing processors and memory to transmit and display schedules, and apply control signals to traffic controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual scheduling methods are used, then human operators can make scheduling decisions, but the system suffers from prolonged computational times, manual errors, and increased costs
Solution Approach 1:
The patent replaces manual mechanical scheduling operations with an automated computer-based system. The scheduling machine uses software algorithms to automatically generate, optimize, and adjust train schedules, replacing the mechanical process of manual timetable creation and modification. This substitution eliminates manual errors while reducing the operational complexity burden on human staff.
Solution Approach 2:
The scheduling system implements self-service capabilities where the computer automatically detects disturbances, recalculates schedules, and generates optimized timetables without requiring continuous human intervention. The system serves itself by autonomously monitoring train positions, detecting delays, and adjusting schedules in real-time, thereby reducing the need for manual operational complexity.
2Loss of time
If traditional scheduling optimization is applied, then travel times can be reduced, but computational demands increase significantly
Solution Approach 1:
The patent segments the scheduling problem into manageable components by dividing the rail network into zones or sections and processing schedules in stages. Instead of optimizing the entire network simultaneously, the system breaks down complex scheduling tasks into smaller sub-problems that can be solved more efficiently with reduced computational power while still achieving overall travel time reduction.
Solution Approach 2:
The system applies partial optimization by focusing computational resources on critical path segments that most impact travel time, rather than optimizing every aspect of the schedule equally. By concentrating computational power on high-impact areas such as bottleneck sections or frequently delayed routes, the system achieves significant travel time reduction without requiring excessive overall computational resources.
3Productivity
If schedules are optimized for minimal travel time, then throughput increases, but the risk of deadlocks and safety issues increases
Solution Approach 1:
The scheduling system incorporates continuous feedback mechanisms that monitor train positions, signal states, and schedule adherence in real-time. This feedback loop allows the system to detect potential deadlock situations before they occur and automatically adjust schedules to prevent safety issues while maintaining high throughput. The feedback ensures that optimization does not compromise reliability.
Solution Approach 2:
The system applies preliminary anti-action by proactively identifying and preventing potential deadlock scenarios before they can affect productivity. The scheduling algorithm incorporates safety constraints and deadlock prevention rules that automatically block suboptimal scheduling decisions that could lead to safety issues, thereby maintaining both high throughput and reliability simultaneously.
Data Source
AI summary
A method is provided for operating a railway network having a number of trains, the method comprises operating a scheduling machine that is in communication with the railway network over a data communication system, to receive time separated state data defining states of the railway network at respective times. The scheduling machine accesses a model of the railway network that is stored in an electronic data source. The model defines locations in the railway network that allows for passing of trains and paths for journeys of each of the trains. The method includes operating the scheduling machine to apply the state data to the model to determine, at each of the respective times, controls associated with each trains' path for each of the trains. The scheduling machine is operated to determine the controls by optimizing an objective function for the trains, such as minimizing total travel time of the trains, taking into account the locations in the network, positions of the trains and paths of each of the trains. The controls are transmitted, via the data communication system, to control movement of the trains, for example by operation of railway network switches (points) and signal lights, through the railway network based on the controls.