SYSTEM AND METHOD FOR OPTIMIZING AIRPORT OPERATIONS

A method of optimizing ground operations at an airport. The method includes receiving input information regarding the airport where the input information at least includes turnaround process information for aircraft at the airport. An allocation of ground resources and manpower is determined at the airport with a ground resource and manpower model based on the input information. The allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model are iteratively optimized until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport. A report is generated based on the optimized allocation of ground resources and manpower.

FIELD

The present disclosure relates to optimizing an allocation of resources for turnaround activities at an airport.

BACKGROUND

Air traffic is experiencing a tremendous surge with new routes being added almost every day. While growth is a positive sign for aviation, this kind of growth also poses critical challenges like traffic congestion both in the airspace and the airport. Today, congestion and delays have become common situations resulting in high financial costs for the airliners. One way to reduce flight delays is to expand the airport infrastructure, but this takes years to implement successfully and involves high financial costs.

Flight delays not only cause inconvenience to the customer and the airport personnel but also impact airliner's operational costs and profit. An allocation of a given set of resources at the airport influences the airliner's schedule, operation costs, and profit. If delays are reduced, then the associated costs can be minimized. Furthermore, by optimizing turnarounds, airlines can enhance aircraft utilization, resulting in increased revenue. This necessitates the optimization of ground resources and manpower to maximize efficiency and productivity.

SUMMARY

A method of optimizing ground operations at an airport is disclosed herein. The method includes receiving input information regarding the airport where the input information at least includes turnaround process information for aircraft at the airport. An allocation of ground resources and manpower is determined at the airport with a ground resource and manpower model based on the input information. The allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model are iteratively optimized until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport. A report is generated based on the optimized allocation of ground resources and manpower.

In one or more embodiments of the method, the machine learning model is generated by a machine learning algorithm trained with a training dataset that includes historical operations information at the airport.

In one or more embodiments of the method, the historical operations information at the airport includes information regarding vehicle resource inventory, manpower inventory, and turnaround activities.

In one or more embodiments of the method, the training dataset includes the input information regarding the airport.

In one or more embodiments of the method, the report includes a turnaround procedures summary having a schedule of activities required to turnaround the aircraft at the airport.

In one or more embodiments of the method, the report includes a flight delay summary identifying a deficiency in resources that will result in a delay of at least one aircraft at the airport.

In one or more embodiments of the method, the report includes a flight schedule summary providing an optimized flight schedule for the airport based on the ground resources and manpower available at the airport.

In one or more embodiments of the method, the turnaround process information includes at least one of a vehicle resource inventory at the airport or a manpower inventory at the airport.

In one or more embodiments of the method, the turnaround process information includes turnaround activities for at least one aircraft at the airport.

In one or more embodiments of the method, the input information includes flight scheduling information for airplanes at the airport.

In one or more embodiments of the method, the input information includes weather conditions at the airport.

In one or more embodiments of the method, the input information includes at least one of an airport layout, airport resources, airport NOTAMS, or gate availability at the airport.

In one or more embodiments of the method, the optimized allocation of ground resources and manpower determined by the ground resource and manpower model is customizable with user preferences.

A non-transitory computer-readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method is disclosed herein. The method includes receiving input information regarding the airport where the input information at least includes turnaround process information for aircraft at the airport. An allocation of ground resources and manpower is determined at the airport with a ground resource and manpower model based on the input information. The allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model are iteratively optimized until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport. A report is generated based on the optimized allocation of ground resources and manpower.

In one or more embodiments, the machine learning model is generated by a machine learning algorithm trained with a training dataset that includes historical operations data at an airport.

In one or more embodiments, the training dataset includes the input information regarding an airport.

In one or more embodiments, the report includes a turnaround procedures summary having a schedule of activities required to turnaround the aircraft at the airport.

In one or more embodiments, the report includes a flight delay summary identifying a deficiency in resources that will result in a delay of at least one aircraft at the airport.

In one or more embodiments, the report includes a flight schedule summary providing an optimized flight schedule for an airport based on the ground resources and manpower available at the airport.

In one or more embodiments, the turnaround process information includes at least one of a vehicle resource inventory at the airport or a manpower inventory at the airport.

Some embodiments of the present disclosure are now described, by way of example only, and with reference to the accompanying drawings. The same reference number represents the same element or the same type of element on all drawings.

DESCRIPTION

The Figures and the following description illustrate specific exemplary embodiments of the disclosure. A person of ordinary skill in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the disclosure and are included within the scope of the disclosure. Furthermore, any examples described herein are intended to aid in understanding the principles of the disclosure and are to be construed as being without limitation to such specifically recited examples and conditions. As a result, the disclosure is not limited to the specific embodiments or examples described below but by the claims and their equivalents.

Various embodiments of the disclosure generally provide a system and method for optimizing airport operations. In particular, the system augments and optimizes turnaround ground operations (resources and manpower) to reduce turnaround time for aircraft. The system and method disclosed herein can also generate an optimized flight schedule based on available resources and manpower and improve predictions of resources and manpower required for a given flight schedule based on turnaround activities for each aircraft.

FIG.1illustrates an example method100of optimizing airport resources through utilizing a ground resource and manpower model (GRMM) at Block104. In one example, the GRMM (Block104) can perform a simulation of an allocation of ground resources and manpower for a given set of ground operations to determine an effectiveness in the allocation of resources. A system200(FIG.2) employs the disclosed method100to optimize the allocation of ground resources and manpower at an airport. This includes ground-based machinery/equipment and ground staff resources. The objective is to reduce airplane turnaround time and enhance resource allocation, leading to improved operational efficiency. One feature of implementing the disclosed method100is improved efficiency of airports without changes to infrastructure at the airport, which can be expensive and require years to plan and complete.

To generate the optimized allocation of ground resources and manpower with the method100, different types of input information are provided to the GRMM at Block104. In one example, the input information includes turnaround process information (Block102). The turnaround process information can include at least one of a vehicle resource inventory (Block102A), a manpower inventory (Block102B), or turnaround activities (Block102C).

The vehicle resource inventory can include the number of vehicles available at the airport for carrying out operations at the airport. The manpower inventory can include the number of ground staff along with their associated skills for carrying out operations at the airport. The turnaround activities can include activities for preparing an aircraft for the next flight, such as cleaning, fueling, and restocking of supplies. In one example, the input information (Block102) is provided to the GRMM (Block104) through internet of things (IoT) devices forming a connection, such as through the internet, with the GRMM (Block104) in the cloud server (Block106).

The GRMM (Block104) and the machine learning workspace (Block108) can also access flight scheduling information (Block112), airport weather information (Block114), and airport information (Block116). In the illustrated example, the flight scheduling information includes Standard Schedules Information Manual SSIM (Block112A) and live flight tracking information (Block112B). The airport information (Block116) includes an airport layout (Block116A), airport resources (Block116B), airport notice to air missions (NOTAMS) (Block116C), and gate availability (Block116D).

Furthermore, the machine learning workspace (Block108) can access historical turnaround operations information from Block110. The historical turnaround operations information (Block110) can include information regarding past allocations at the airport of the vehicle resource and the manpower along with the associated turnaround activities for the aircraft.

The turnaround process information (Block102), the flight scheduling information (Block112), the airport weather information (Block114), the airport information (Block116), and the historical turnaround operations information (Block110) can form a training dataset for the machine learning workspace (Block108). The training dataset can be utilized for training a machine learning algorithm to develop a machine learning model in the machine learning workspace (Block108) for iteratively optimizing an allocation of ground resources and manpower with the GRMM (Block104). In the illustrated example, the development of the machine learning model occurs in the cloud server (Block106). However, the machine learning model could be performed locally on a piece of hardware depending on the application.

Preferences (Block128) for the allocation of ground resources and manpower can be provided to the GRMM (Block104) to guide the GRMM in optimizing the allocation to meet a predetermined requirement of the user or airline. The preferences can also include a direction to use multiple skill levels of manpower resources, to train idle manpower resources, or other specific preferences for resource allocation.

The GRMM (Block104) can create a summary for each allocation of resources that can be used for comparison with the next iteration in the GRMM. The summary of ground staff resources can illustrate how manpower resources are allocated to a particular resource type and sum up the total manpower in different statuses such as in use, waiting, moving, idle, allocated, or unavailable to determine if the latest iteration is an improvement.

To generate the optimized allocation of ground resources and manpower, the machine learning model from the machine learning workspace (Block108) performs an iterative computation with the GRMM (Block104). Each iteration of the allocation of ground resources and manpower is evaluated at Block117. If the optimized allocation evaluated at Block117does not result in an improved allocation of resources, then a further iteration is performed with the machine learning workspace (Block108) and the GRMM (Block104). If the optimized allocation produces a better allocation at Block117, the method100proceeds to Block118to generate at least one report or visualizations based on the optimized allocation. In one example, the better allocation is reached when the optimized allocation of ground recourses and manpower generated reduces idling of at least one of ground resource or manpower at the airport when compared to a previous allocation. Furthermore, a predetermined threshold in reduction could be used to determine when to stop iterating between the machine learning workspace (Block108) and the GRMM (Block104).

The optimized allocation can be stored in a binary large object service. The trained machine learning model can also be stored for use in further improving the machine learning logic to build an image with mode and dependencies. In the illustrated example, reports from the optimized allocation of resources could include a customized section120with at least one of a “Turnaround Procedures Summary” (Block122), a “Flight Delay Summary” (Block124), or a “Flight Schedule Summary” (Block126).

The turnaround procedures summary122provides a proposed model that accurately predicts the manpower and resources required to smoothly execute a set of turnaround operations for a given duration of time. The flight delay summary124identifies deficiencies in resources that will result in a delay of at least one aircraft at the airport. The flight schedule summary126provides an optimized flight schedule for the airport based on the ground resources and manpower available at the airport.

The method100can also generate a flight schedule based on the available resources and accurately estimate the number of resources needed to carry out individual or multiple activities within a specific timeframe. This includes tasks such as efficiently managing aircraft turnarounds. The system200incorporating the method100is compatible with external tools, such as mobile devices, to improve resource allocation as will be discussed further below.

In one example implementation of the method100, an airliner has more manpower than required at a particular point of time which results in an inefficient usage of available resources by idling resources or having resources wait for prior process to complete. The method100described above, learns progressively from the interaction of GRMM (Block104) with the machine learning workspace (Block108) and the information stored in the cloud server (Block106) to improve the optimization of resources on each iteration. The method100is capable of identifying scenarios when there are idle resources and then by allocating those resources to various ongoing tasks that are currently being executed either in the resource area of expertise or at other areas of work resulting in faster execution of the current assigned task by increasing the skill set of manpower resource by training them. This further allows the method100to prepare and address unseen scenarios. One such scenario is when the airliner is facing a shortage of manpower of a particular skill set and determine how training idle manpower would remedy the situation in the future.

In another example implementation of the method100, the allocation of resources tends to increase a skillset of the manpower, when possible, instead of idling the manpower resource if the skillset of the available manpower resource does not meet the current need. In this case, an airliner may have a shortage of manpower resources of a particular skillset, such as fueling, catering, cargo etc. at any given point of time. With the method100, the airliner would increase the skillset of the available manpower resources due to the method100being able to assign idled manpower resources to learn new skillsets that will reduce delays or shortages in manpower of a given skillset in the future.

FIG.2illustrates the system200implementing the method100as software as a service over a Cloud Computing Service (CCS). With the example system200, users (Block202) are authenticated on the system200using Identify-as-a-service (IDaaS) (Block204) through a user device206, such as a tablet or smart phone. Once the users are successfully authenticated, the users are redirected to a user interface (Block208), such as a homepage on a web application, that appears on the user device206. From the interface (Block208), the user is directed to provide various types of input information or data (Block218), such as turnaround process information (See Block102), flight scheduling information (See Block112), airport weather (See Block114), or airport information (See Block116) as discussed above with the method100. This information can be temporarily stored in a file storage system (Block220) in a cloud server (Block214) with each file being given a unique identifier. In the illustrated example, the user is associated with a particular region (Block240-1) corresponding to at least one airport.

In one example, the interface (Block208) can also include various input fields, such as resource type, aircraft type, aircraft category, number of personnel to be allocated, manpower resource experience, skill set, shift, or etc. The input fields can also be populated with predetermined information files, such as files including the manpower resources, the flight schedule, or the turnaround process. In one example, a built in “turnaround process” file could be used when no “turnaround process” file or input information is provided by the user or airliner. Furthermore, the airliner can either alter the default “turnaround process” file or import a new one as desired.

The system200can also allow the user or airliner to input predetermined rules for assigning resources or allow the system200to assign predetermined rules. The system200can also provide preferences for resource allocation when generating the optimized allocation of ground resources and manpower, such as priority between international flights vs. domestic flights, cargo vs. passenger flights, or multi-leg vs. single-leg flights. Also, if the system200observes that there are additional resources or idle resources, the system200can automatically allocate those resources to other areas of work enabling manpower resources to gain enhanced skill sets through experience in varying fields.

The input fields accessible to the users can include a “manpower resource type” field which indicates various skills possessed by the ground staff. Also, the ground staffs level of expertise in each skill can be indicated by an “experience” field. The user will be able to interact with the system through the interface (Block208) to make changes to the input information before it is processed on the cloud server (Block214). Having the input information processed on the cloud server (Block214) reduces redundancy in calculations by eliminating the requirement to have each user perform the core logic and calculations of the GRMM (Block226) and machine learning workspace (Block224) as described above with respect to the method100. This also reduces the computing power required for the user devices (206).

Once all the required input information is provided to the system200, the system200directs the machine learning workspace (Block224) to perform an iterative computation with the GRMM (Block226) to generate the optimized allocation of ground resources and manpower. The machine learning model in the machine learning workspace (Block224) is trained as discussed above with respect to the method100.

Once the iterative computation is complete, the output results are stored in the file storage system (Block220). The trained models (Block222) can also be stored on the cloud server (Block214) for use in further improving the machine learning logic in the machine learning workspace (Block224). In particular, the trained machine learning models (Block222) can be shared with models from other regions (Block240-N) along line242.

The optimized allocation of ground resources and manpower can include at least one of a turnaround procedures summary, a flight delay summary, or a flight schedule summary as discussed above. At least one of these summaries can be provided to the user through an interactive data visualization (Block212), such as with PowerBI or Tableau, for analysis. The visualizations (Block212) can provide insights into how the manpower has been utilized, the flight delay status, and turnaround status, including summaries detailing the various aspects of manpower, aircraft, and resources be used or idled. This detailed status helps in decision making for better allocation of resources thus reducing the overall turnaround time resulting in fewer or shorter flight delays. One feature of the system200is that it can reduce deadlock of manpower unavailability for a particular skill set by allocating another manpower resource possessing the required skill set.

The above summaries can also illustrate the resource details and status of a given summary. The resource details represent the activity which is ongoing for an allocated resource type. Resource status can represent details of all resources allocated to a particular resource type thus deciding the total resources in different statuses such as in use, waiting, moving, idle, allocated and unavailable.

As the ground resource staff are largely mobile and are directed to various locations on a continuously updated timeframe, the system200operates largely through cloud server214with wireless communication to the mobile devices206, such as smart phones or tablets, for directing the ground resources.

The following Clauses provide example configurations of systems and methods for an example method100of optimizing airport resources ofFIG.1.

Clause 1: A method of optimizing ground operations at an airport, the method comprising: receiving input information regarding the airport, wherein the input information at least includes turnaround process information for aircraft at the airport; determining an allocation of ground resources and manpower at the airport with a ground resource and manpower model based on the input information; iteratively optimizing the allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport; and generating a report based on the optimized allocation of ground resources and manpower.

Clause 2: The method of clause 1, wherein the machine learning model is generated by a machine learning algorithm trained with a training dataset that includes historical operations information at the airport.

Clause 3: The method of any of clauses 1-2, wherein the historical operations information at the airport includes information regarding vehicle resource inventory, manpower inventory, and turnaround activities.

Clause 4: The method of any of clauses 1-3, wherein the training dataset includes the input information regarding the airport.

Clause 5: The method of any of clauses 1-4, wherein the report includes a turnaround procedures summary having a schedule of activities required to turnaround the aircraft at the airport.

Clause 6: The method of any of clauses 1-5, wherein the report includes a flight delay summary identifying a deficiency in resources that will result in a delay of at least one aircraft at the airport.

Clause 7: The method of any of clauses 1-6, wherein the report includes a flight schedule summary providing an optimized flight schedule for the airport based on the ground resources and manpower available at the airport.

Clause 8: The method of any of clauses 1-7, wherein the turnaround process information includes at least one of a vehicle resource inventory at the airport or a manpower inventory at the airport.

Clause 9: The method of any of clauses 1-8, wherein the turnaround process information includes turnaround activities for at least one aircraft at the airport.

Clause 10: The method of any of clauses 1-9, wherein the input information includes flight scheduling information for airplanes at the airport.

Clause 11: The method of any of clauses 1-10, wherein the input information includes weather conditions at the airport.

Clause 12: The method of any of clauses 1-11, wherein the input information includes at least one of an airport layout, airport resources, airport NOTAMS, or gate availability at the airport.

Clause 13: The method of any of clauses 1-12, wherein the optimized allocation of ground resources and manpower determined by the ground resource and manpower model is customizable with user preferences.

Clause 14: A non-transitory computer-readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising: receiving input information regarding the airport, wherein the input information at least includes turnaround process information for aircraft at the airport; determining an allocation of ground resources and manpower at the airport with a ground resource and manpower model based on the input information; iteratively optimizing the allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model until an optimized allocation of ground recourses and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport; and generating a report based on the optimized allocation of ground resources and manpower.

Clause 15: The computer-readable medium of clause 14, wherein the machine learning model is generated by a machine learning algorithm trained with a training dataset that includes historical operations data at an airport.

Clause 16: The computer-readable medium of any of clauses 14-15, wherein the training dataset includes the input information regarding an airport.

Clause 17: The computer-readable medium of any of clauses 14-16, wherein the report includes a turnaround procedures summary having a schedule of activities required to turnaround the aircraft at the airport.

Clause 18: The computer-readable medium of any of clauses 14-17, wherein the report includes a flight delay summary identifying a deficiency in resources that will result in a delay of at least one aircraft at the airport.

Clause 19: The computer-readable medium of any of clauses 14-18, wherein the report includes a flight schedule summary providing an optimized flight schedule for an airport based on the ground resources and manpower available at the airport.

Clause 20: The computer-readable medium of any of clauses 14-19, wherein the turnaround process information includes at least one of a vehicle resource inventory at the airport or a manpower inventory at the airport.