Transportation Disruption Management Using Joint Delay and Cancellation Optimization
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Solution Overview
Problem
Current disruption management systems in transportation, particularly during irregular operations (IROPs), lack the capability to reschedule operations based on comprehensive constraints like gating, arrival, and departure rates, leading to inadequate handling of cancellations and delays, resulting in significant financial losses and customer disruptions.
Innovation Solution
The HEAT system employs an advanced optimization model that provides joint delay and cancel solutions, incorporating gating, arrival, and departure rate constraints, along with an intelligent user interface to efficiently manage disruptions by rescheduling flights and crew operations in real-time, minimizing impact on passengers and crew.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing disruption management systems use delay-only solutions considering only gating constraints, then the system operation is simple, but the capability to reschedule operations based on comprehensive constraints (gating, arrival, departure rates) is insufficient
Solution Approach 1:
The patent combines multiple constraint types (gating constraints, arrival rate constraints, departure rate constraints) into a unified optimization model that simultaneously considers all these factors when determining delay and cancellation decisions, rather than handling them separately as in existing systems
Solution Approach 2:
The optimization model serves multiple functions: it determines both delay and cancellation decisions, incorporates multiple constraint types, and optimizes for multiple objectives (passenger inconvenience, operational feasibility, cost minimization) within a single integrated system
2Reliability
If cancellations and delays are considered independently in existing systems, then the analysis for each decision is simpler, but the overall disruption management becomes inadequate and complex
Solution Approach 1:
The patent merges the previously separate cancellation and delay decision processes into a single joint optimization model that evaluates both decision types simultaneously, considering their interdependencies and trade-offs within a unified framework
Solution Approach 2:
The optimization model uses adjustable parameters and weights to balance different objectives (passenger inconvenience, operational constraints, cost factors) allowing the system to adapt to different disruption scenarios and priorities without changing the fundamental decision structure
3Productivity
If flights are delayed or cancelled without comprehensive optimization, then operational decisions are made quickly, but significant financial losses occur and customer satisfaction deteriorates
Solution Approach 1:
The optimization model performs preliminary analysis and evaluation of multiple delay and cancellation scenarios before making final decisions, calculating the impact on passengers, operations, and costs in advance to select the optimal course of action that minimizes losses
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate the impact of delay and cancellation decisions on multiple factors (passenger inconvenience, operational constraints, cost implications) and uses this information to refine and optimize subsequent decisions
Data Source
AI summary
A system and method that includes displaying a graphical depiction of projected demand, with values of the projected demand calculated using a constraint, over a future period of time; displaying, over the graphical depiction, a selectable graphical element at a first position that is associated with a first value of the constraint; and wherein the selectable graphical element is adjustable to change the value of the constraint; receiving an indication that the selectable graphical element has been adjusted to a second position associated with a second value of the constraint; and updating the graphical depiction using updated values for projected demand over the future period of time. Projected demand may be part of transportation-related data, on which basis a recommended management plan for a disruption may be identified, the plan being identified with a network model and including strategic travel leg delays and strategic travel leg cancellations.