Crew Reassignment Optimization Using Mixed Integer Programming
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Solution Overview
Problem
In industries like travel and hospitality, unforeseen events often render service positions unavailable, leading to disruptions in crew assignments and compensation entitlements, which existing systems struggle to manage efficiently.
Innovation Solution
A system utilizing a client module and optimizer to receive and process status data, identify broken legs, calculate crew entitlements, and perform mixed-integer programming to reassign crew members through deadhead tables, ensuring compliance with business and governmental rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems are used to manage crew assignments during disruptions, then system complexity is reduced, but reassignment efficiency and compensation accuracy deteriorate
Solution Approach 1:
The system segments the crew reassignment problem into distinct components: identifying broken legs, determining affected crew members, calculating compensation entitlements, and generating reassignment options. This segmentation allows each component to be processed independently through mixed-integer programming, improving reassignment efficiency while managing system complexity through modular problem decomposition.
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between the disruption event and the reassignment outcome. This intermediary processes the complex calculations of crew entitlements and generates optimized reassignment solutions, thereby improving efficiency without requiring the entire system to become more complex.
2Manufacturing precision
If manual methods are used for crew reassignment, then system complexity is minimized, but reassignment accuracy and compliance with rules deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the mixed-integer programming model evaluates reassignment options against business rules and governmental regulations, then adjusts solutions accordingly. This feedback loop ensures high reassignment accuracy and compliance while the automated nature of the feedback process manages system complexity through algorithmic rather than manual procedures.
Solution Approach 2:
The patent utilizes parameter changes in the mixed-integer programming model to dynamically adjust reassignment solutions based on varying constraints and rules. By changing parameters such as compensation thresholds, eligibility criteria, and operational constraints, the system achieves high accuracy in reassignment while managing complexity through mathematical optimization rather than complex procedural logic.
3Reliability
If rapid reassignment is performed without optimization, then response time is reduced, but compensation fairness and operational integrity deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating compensation entitlements and identifying affected crew members immediately when a disruption occurs. This preliminary processing allows the mixed-integer programming optimization to focus only on generating reassignment options, thereby maintaining operational integrity through thorough optimization while minimizing additional reassignment time.
4Measurement precision
If comprehensive crew entitlement calculations are performed, then compensation fairness is improved, but processing time increases
Solution Approach 1:
The patent extracts the compensation calculation function as a separate, dedicated component within the optimization system. By taking out the entitlement calculation from the general reassignment process and handling it through specific formulas and rules in the mixed-integer programming model, the system achieves high compensation accuracy while minimizing processing time through efficient, specialized computation rather than general-purpose processing.
Data Source
AI summary
A system and method for allocating crew is disclosed. A plurality of legs are identified, wherein at least two legs of the plurality of legs are broken. A first crew and a first sequence associated with the first leg of the at least two broken legs are identified. A second crew and a second sequence associated with the second leg of the at least two broken legs is identified. A plurality of new sequences for the first crew and the second crew using deadheads is generated. A solution for each crew is generated using a mixed integer program.