Rail Yard Block Stacking Using Time-Space Network Optimization
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Solution Overview
Problem
The manual process of assigning train blocks to classification tracks in a railroad merchandise yard is inefficient and often leads to suboptimal decisions, resulting in increased time, switching operations, and fuel consumption.
Innovation Solution
A time-space network based multi-objective system that utilizes multiple optimization models to automatically assign train blocks to classification tracks, considering various constraints and objectives, such as bowl capacity, switch distance, and pull lead utilization, to optimize the stacking and swing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual process is used to assign train blocks to classification tracks, then ease of operation is maintained, but productivity decreases and time required increases
Solution Approach 1:
The patent replaces the manual mechanical decision-making process with an automated computer-based optimization system. The system uses mathematical models and algorithms to automatically assign train blocks to classification tracks, eliminating the need for manual intervention while significantly improving assignment efficiency and reducing time required for train assembly operations.
Solution Approach 2:
The optimization system performs self-service by automatically analyzing train block characteristics, bowl capacity constraints, and switching operation requirements to generate optimal assignment decisions. The system independently processes complex optimization problems without requiring human operators to manually evaluate each scenario, thereby improving productivity while maintaining operational control.
2Loss of time
If manual decisions are made about stacking and swinging, then operational flexibility is maintained, but time required and fuel consumption increase
Solution Approach 1:
The system performs preliminary optimization calculations before actual train block assignments are executed. By pre-determining optimal stacking and swinging decisions based on future train block arrivals and bowl capacity constraints, the system eliminates time-consuming manual decisions during critical operational windows, significantly reducing the time required to form outbound trains.
Solution Approach 2:
The patent substitutes manual operational decisions with automated optimization algorithms that calculate optimal stacking and swinging strategies. The system uses mathematical models to determine the most efficient assignments, replacing human judgment with computational precision that reduces both time loss and fuel consumption through optimized switching operations.
3Productivity
If multiple optimization models are used to assign train blocks, then productivity improves, but device complexity increases
Solution Approach 1:
The optimization system is segmented into multiple specialized optimization models, each handling specific aspects of train block assignment. The first optimization model handles basic train block to classification track assignment, while subsequent models address stacking, swinging, and capacity constraints. This segmentation allows each model to be optimized for its specific function, improving overall productivity while managing complexity through modular architecture.
Solution Approach 2:
The optimization system employs a universal computational framework that can execute multiple optimization models sequentially. The system integrates various optimization algorithms within a unified platform, allowing it to handle different assignment scenarios, stacking configurations, and capacity constraints through a single multi-functional system rather than requiring separate independent systems.
4Productivity
If automated optimization system is implemented, then switching operations decrease, but measurement and detection complexity increases
Solution Approach 1:
The optimization system incorporates feedback mechanisms that continuously monitor bowl capacity, train block arrivals, and switching operation execution. By receiving real-time feedback on system state and comparing it against optimized plans, the system can detect deviations and adjust assignments dynamically. This feedback approach simplifies monitoring by providing structured, algorithm-based detection rather than complex manual surveillance.
Data Source
AI summary
A method for assigning train blocks at a railroad merchandise yard includes determining, using a first optimization model and outbound train schedule data, a first list of train block assignments for a planning horizon. The method further includes determining whether an unassigned train block volume from the first optimization model is greater than zero. The method further includes displaying the first list of train block assignments generated by the first optimization model on an electronic display in response to determining that the unassigned train block volume from the first optimization model is not greater than zero. The method further includes, in response to determining that the unassigned train block volume from the first optimization model is greater than zero: determining and then displaying on the electronic display a second list of train block assignments for the planning horizon using a second optimization model and the outbound train schedule data.