Air cargo yield management system for utilizing booking profiles and unconstrained demand

• US 8,321,252 B2
• Filed: 01/31/2012
• Issued: 11/27/2012
• Est. Priority Date: 07/12/2001
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Gliozzi, Stefano, Marchetti, Alberto Maria
• Primary Examiner(s): Jarrett, Scott L
• Application Number:
  US13/362,150
• Publication Number:
  US 20120143647A1
• Time in Patent Office: 301 Days
• Field of Search: 705/7.12
• US Class Current: 705/7.12
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change

Abstract

A yield management method and system are provided for enhancing revenue obtained from a given cargo flight capacity; the capacity being defined by the weight and volume of freights that may be transported. A set of historical profiles of capacity reserved by each category is stored for previous instances of the flight. Corresponding potential profiles are estimated independently for the weight and volume. The estimation is carried out taking into consideration the corresponding capacity currently reserved by the category for a future instance of the flight; and this result is emphasized according to a corresponding unconstrained demand for the category. A series of historical scenarios of the demand at the departure of previous instances of the flight are obtained from the potential profiles, and a stochastic model is applied to the historical scenarios according to the corresponding probabilities for calculating the optimised weight and volume authorizations for each category.

Claims

1. A computer system for managing air cargo yield for optimising a yield parameter resulting from assigning an offered capacity offered by a future instance of a cargo flight to each one of a plurality of different categories of requests competing for the capacity, the computer system comprising:

a memory; and

a processor in communication with the memory, wherein the computer system is configured to perform a method, the method comprising;

separately defining the offered capacity by a weight capacity variable and by a volume capacity variable, the weight variable and the volume variable independent relative to each other;

storing a set of historical profiles for each one of a plurality of previous instances of the cargo flight, the set including a historical profile of a historical value of the weight variable and the volume variable reserved by each category,

assigning a probability to each previous instance of the cargo flight,

estimating a potential profile of a potential value of the weight variable and the volume variable from each historical profile according to a corresponding current value of the capacity variable reserved by the category for the future instance of the cargo flight and according to a corresponding unconstrained demand of the weight variable and the volume variable for the category in the previous instance of the cargo flight,

defining a historical scenario for each previous instance of the cargo flight, the historical scenario including a final potential capacity variable from each corresponding potential profile, the final potential variable comprising a final potential weight capacity variable and a final potential volume capacity variable;

determining an authorisation to allocate the offered capacity for the weight variable and the volume variable of each category in the future instance of the cargo flight by applying a stochastic model to the historical scenarios according to the corresponding probabilities;

providing a determination to a display of a computing unit of an acceptance or a rejection of a request for the cargo flight based on the authorization; and

wherein each historical profile and each potential profile include a plurality of corresponding snapshots of the reserved capacity variable and of the potential capacity variable, respectively, the estimating the potential profile comprising;

estimating an opening coefficient for each period comprised between two consecutive snapshots, the opening coefficient being indicative of a percentage of time during which the category was open in the period,

calculating an emphasis value for each period as a weighted mean of a gradient in the period of the reserved capacity variable for the category in a subset of the corresponding historical profiles,

estimating a potential gradient for each period as a linear interpolation between the gradient, and

constructing the potential profile from a time corresponding to the current time by integrating the potential gradients starting from the corresponding current capacity variable.

2. The computer system of claim 1, wherein the estimating the potential profile further comprises:

verifying whether at least one result of the integration in each snapshot for each category is not strictly positive, and

setting each potential capacity variable of the category in the snapshot to zero if the verification is affirmative.

3. The computer system of claim 1, wherein the estimating the potential profile further comprises reconciling the potential capacity variables for each category in the snapshot to a reference value of a logic relation there between.

4. The computer system of claim 3, wherein the reconciling comprises:

verifying whether the logic relation between the potential capacity variables for the category in the snapshot is included between a first limit defined by the logic relation between the reserved capacity variables for the category in the snapshot and a second limit defined by the logic relation between the potential capacity variables for the category in a preceding snapshot, and

updating the potential capacity variables for the category in the snapshot for correcting the corresponding logic relation to the closest one of the first limit and the second limit.

5. The computer system of claim 1, wherein the assigning the probability comprises:

determining a first coefficient depending on a difference between a temporal period associated with the previous instance of the cargo flight and a temporal period associated with the future instance of the cargo flight,

determining a second coefficient depending on a space between a current time and an occurrence time of the previous instance of the cargo flight,

calculating a weight by combining the first coefficient and the second coefficient,

calculating the probability by normalising the weight.

6. The computer system of claim 5, wherein the calculating the weight consists of calculating a weighted sum of the first coefficient and the second coefficient.

7. The computer system of claim 1, wherein the method further comprises:

determining a historical unit value of the yield parameter for each category in each previous instance of the cargo flight if available,

estimating the historical unit yield parameter for each category in the other previous instances of the cargo flight as a weighted mean of the corresponding historical unit yield parameters available,

estimating a potential unit value of the yield parameter for each category in each previous instance of the cargo flight from the corresponding historical unit yield parameter, and

calculating a potential value of the yield parameter for each category in each previous instance of the cargo flight multiplying the corresponding potential unit yield parameter by the corresponding potential capacity variable, the potential yield parameter being included in the corresponding historical scenario.

8. The computer system of claim 7, wherein the estimating each potential unit yield parameter comprises:

determining a current unit value of the yield parameter for the corresponding category in the future instance of the cargo flight, and

calculating each potential unit yield parameter as a sum of the corresponding historical unit yield parameter and current unit yield parameter weighted according to a corrective factor.

9. The computer system of claim 8, wherein the calculating the potential unit yield parameter further comprises:

determining a further first coefficient depending on a difference between the current time and a planned occurrence time of the future instance of the cargo flight,

determining a further second coefficient depending on an increment of the at least one potential capacity variable with respect to the at least one current capacity variable for the category,

calculating the corrective factor by combining the further first coefficient and the further second coefficient, and

calculating the potential yield parameter as a sum of the historical yield parameter and the current yield parameter weighted according to the corrective factor.

10. The computer system of claim 8, wherein the method further comprises:

calculating a weighted mean value of the potential yield parameter for each capacity variable of each category,

determining a nesting order of the categories for each capacity variable according to the corresponding weighted mean potential yield parameters.

11. The computer system of claim 10, wherein the determining each nesting order comprises:

providing an input nesting order of the categories for each capacity variable, and

updating each input nesting order by ranking the categories having at lest one scenario with each component thereof that is strictly positive.

12. The computer system of claim 1, wherein the determining the authorisations comprises:

providing an aggressiveness parameter indicative of an attitude to the risk,

defining a first portion of a target function, the first portion calculating the yield parameter by assigning the offered capacity with a nesting policy,

defining a second portion of the target function, the second portion calculating the yield parameter by assigning the offered capacity with an out of nesting policy,

defining the target function as a sum of the first portion and the second portion weighted according to the aggressiveness parameter, and

calculating the authorisations by optimising the target function.

13. The computer system of claim 12, wherein the defining the second portion comprises defining an independent component of the second portion for each capacity variable.

14. The computer system of claim 1, wherein the method further comprises providing a user-defined scenario with a corresponding probability, the stochastic model being further applied to the user-defined scenario according to the corresponding probability.

15. The computer system of claim 1, wherein the plurality of capacity variables consists of a weight and a volume, and the yield parameter consists of a revenue.

16. The computer system of claim 1, wherein

the determining the authorization comprises employing a partial nesting policy with two sets of independent authorizations relating to the weight variable and the volume variable.

17. The computer system of claim 16, wherein the method further comprises allocating the offered capacity by comparing the request simultaneously to the offered capacity for the weight variable and the volume variable.

18. The computer system of claim 16, wherein the allocating comprises allocating the offered capacity relative to the categories by two sets of independent allocations, each of said allocations using a partial nesting policy.