View source for 17644382. ORDERING FLIGHT TAKEOFFS AND LANDINGS BASED ON PASSENGER COMPLAINT PROPENSITIES simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

=ORDERING FLIGHT TAKEOFFS AND LANDINGS BASED ON PASSENGER COMPLAINT PROPENSITIES=


==Organization Name==

[[:Category:INTERNATIONAL BUSINESS MACHINES CORPORATION|INTERNATIONAL BUSINESS MACHINES CORPORATION]]


[[Category:INTERNATIONAL BUSINESS MACHINES CORPORATION]]

==Inventor(s)==

[[:Category:Herbert Scott Mcfaddin of Yorktown Heights NY (US)|Herbert Scott Mcfaddin of Yorktown Heights NY (US)]][[Category:Herbert Scott Mcfaddin of Yorktown Heights NY (US)]]

[[:Category:Youssef Drissi of Peekskill NY (US)|Youssef Drissi of Peekskill NY (US)]][[Category:Youssef Drissi of Peekskill NY (US)]]

[[:Category:Markus Ettl of Yorktown Heights NY (US)|Markus Ettl of Yorktown Heights NY (US)]][[Category:Markus Ettl of Yorktown Heights NY (US)]]

[[:Category:Anna Lisa Gentile of San Jose CA (US)|Anna Lisa Gentile of San Jose CA (US)]][[Category:Anna Lisa Gentile of San Jose CA (US)]]

[[:Category:Petar Ristoski of San Jose CA (US)|Petar Ristoski of San Jose CA (US)]][[Category:Petar Ristoski of San Jose CA (US)]]

[[:Category:Chek Keong Tan of Danbury CT (US)|Chek Keong Tan of Danbury CT (US)]][[Category:Chek Keong Tan of Danbury CT (US)]]

[[:Category:Wei Sun of Scarsdale NY (US)|Wei Sun of Scarsdale NY (US)]][[Category:Wei Sun of Scarsdale NY (US)]]

==ORDERING FLIGHT TAKEOFFS AND LANDINGS BASED ON PASSENGER COMPLAINT PROPENSITIES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17644382 titled 'ORDERING FLIGHT TAKEOFFS AND LANDINGS BASED ON PASSENGER COMPLAINT PROPENSITIES

==Simplified Explanation==

The abstract describes a computer-based method for generating a prioritized list of craft departures from a known origin point. This is done by considering both the operational cost and the predicted passenger satisfaction cost. The method uses historical data on passengers, including craft operations and associated passenger complaints and satisfaction. It trains a passenger satisfaction prediction model based on this data and calculates the predicted passenger satisfaction cost for each craft departure. The final ordered list is generated by combining the operational cost and the computed predicted passenger satisfaction cost.

* Collects historical data on passengers, craft operations, complaints, and satisfaction
* Trains a passenger satisfaction prediction model using the collected data
* Calculates the predicted passenger satisfaction cost for each craft departure
* Generates an ordered list of craft departures based on a combination of operational cost and predicted passenger satisfaction cost

== Potential Applications ==
* Airline industry: Optimizing flight schedules based on both operational cost and passenger satisfaction
* Transportation services: Determining the best departure options for buses, trains, or other modes of transportation

== Problems Solved ==
* Lack of a comprehensive method to prioritize craft departures based on both operational cost and passenger satisfaction
* Difficulty in considering passenger satisfaction as a factor in scheduling decisions

== Benefits ==
* Improved decision-making in craft departure scheduling
* Enhanced passenger satisfaction by considering their preferences and complaints
* Potential cost savings by optimizing craft departures based on both operational and passenger satisfaction costs


==Original Abstract Submitted==

A computer-implemented method for generating an ordered list of craft departures from a known origin point based on an operational cost and a predicted passenger satisfaction cost. The method collects historical data about one or more passengers, wherein the historical data comprises one or more craft operations and associated passenger complaint and satisfaction data. The method further trains a passenger satisfaction prediction model based on the collected historical data and computes the predicted passenger satisfaction cost for each of the craft departures based on the trained passenger satisfaction prediction model. The method further generates an ordered list of craft departures based on a combination of the operational cost and the computed predicted passenger satisfaction cost.



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