job assignment model

• Google OR-Tools
• Español – América Latina
• Português – Brasil
• Tiếng Việt

Solving an Assignment Problem

This section presents an example that shows how to solve an assignment problem using both the MIP solver and the CP-SAT solver.

In the example there are five workers (numbered 0-4) and four tasks (numbered 0-3). Note that there is one more worker than in the example in the Overview .

The costs of assigning workers to tasks are shown in the following table.

The problem is to assign each worker to at most one task, with no two workers performing the same task, while minimizing the total cost. Since there are more workers than tasks, one worker will not be assigned a task.

MIP solution

The following sections describe how to solve the problem using the MPSolver wrapper .

Import the libraries

The following code imports the required libraries.

Create the data

The following code creates the data for the problem.

The costs array corresponds to the table of costs for assigning workers to tasks, shown above.

Declare the MIP solver

The following code declares the MIP solver.

Create the variables

The following code creates binary integer variables for the problem.

Create the constraints

Create the objective function.

The following code creates the objective function for the problem.

The value of the objective function is the total cost over all variables that are assigned the value 1 by the solver.

Invoke the solver

The following code invokes the solver.

Print the solution

The following code prints the solution to the problem.

Here is the output of the program.

Complete programs

Here are the complete programs for the MIP solution.

CP SAT solution

The following sections describe how to solve the problem using the CP-SAT solver.

Declare the model

The following code declares the CP-SAT model.

The following code sets up the data for the problem.

The following code creates the constraints for the problem.

Here are the complete programs for the CP-SAT solution.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2023-01-02 UTC.

• MapReduce Algorithm
• Linear Programming using Pyomo
• Networking and Professional Development for Machine Learning Careers in the USA
• Predicting Employee Churn in Python
• Airflow Operators

Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

• First, prefix name of what this variable represents.
• Second is the list of all the variables.
• Third is the lower bound on this variable.
• Fourth variable is the upper bound.
• Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

• Solving Blending Problem in Python using Gurobi
• Transshipment Problem in Python Using PuLP

You May Also Like

Understanding Random Forest Classification and Building a Model in Python

K-Means Clustering

Data Visualization using Matplotlib

Welcome to maxusknowledge.com

Contact Us:

Email: [email protected]

Optimizing Job Assignments with Python: A Greedy Approach

In this article, we will learn the skill of job assignment which is a very important topic in the field of Operations Research. For this, we will utilize Python programming language and the Numpy library for the same. We will also solve a small case on a job assignment.

Job assignment involves allocating tasks to workers while minimizing overall completion time or cost. Python’s greedy algorithm, combined with NumPy, can solve such problems by iteratively assigning jobs based on worker skills and constraints, enabling efficient resource management in various industries.

Recommended: Maximizing Cost Savings Through Offshore Development: A Comprehensive Guide

Recommended: Delivery Route Optimization using Python: A Step-by-Step Guide

What is a Job Assignment?

Let us understand what a job assignment is with an example. In our example, three tasks have to be completed. Three workers have different sets of skills and take different amounts of time to complete the above-mentioned tasks. Now our goal is to assign the jobs to the workers to minimize the period of completing the three tasks.

Now, we solve the above problem using the concepts of Linear programming. Now there are certain constraints as well, each worker can be assigned only a single job at a time. Our objective function is the sum of all the time taken by the workers and minimize it. Let us now solve this problem using the power of the Numpy library of Python programming language.

Let us now look at the output of the problem.

From the output, we can see that The assignment is complete and optimized. Let us now look at a small case and understand the job assignment further.

A Real-World Job Assignment Scenario

Continuing with the example of assigning workers some jobs, in this case, a company is looking to get some work done with the help of some freelancers. There are 15 jobs and we have 10 freelancers. We have to assign jobs to workers in such a way, that we minimize the time as well as the cost of the whole operation. Let us now model this in the Python programming language.

This problem is solved using the greedy algorithm. In short, the greedy algorithm selects the most optimal choice available and does not consider what will happen in the future while making this choice. In the above code, we have randomly generated data on freelancer details. Let us now look at the output of the code.

Thus, we complete our agenda of job assignment while minimizing costs as evidenced by the output.

Assigning jobs optimally is crucial for maximizing productivity and minimizing costs in today’s competitive landscape. Python’s powerful libraries like NumPy make it easy to implement greedy algorithms and solve complex job assignment problems, even with larger sets of jobs and workers. How could you adapt this approach to accommodate dynamic changes in job requirements or worker availability?

Recommended: Splitting Lists into Sub-Lists in Python: Techniques and Advantages

Recommended: Object Detection with OpenCV: A Step-by-Step Tutorial

Assignment Problem: Meaning, Methods and Variations | Operations Research

After reading this article you will learn about:- 1. Meaning of Assignment Problem 2. Definition of Assignment Problem 3. Mathematical Formulation 4. Hungarian Method 5. Variations.

Meaning of Assignment Problem:

An assignment problem is a particular case of transportation problem where the objective is to assign a number of resources to an equal number of activities so as to minimise total cost or maximize total profit of allocation.

The problem of assignment arises because available resources such as men, machines etc. have varying degrees of efficiency for performing different activities, therefore, cost, profit or loss of performing the different activities is different.

Thus, the problem is “How should the assignments be made so as to optimize the given objective”. Some of the problem where the assignment technique may be useful are assignment of workers to machines, salesman to different sales areas.

Definition of Assignment Problem:

ADVERTISEMENTS:

Suppose there are n jobs to be performed and n persons are available for doing these jobs. Assume that each person can do each job at a term, though with varying degree of efficiency, let c ij be the cost if the i-th person is assigned to the j-th job. The problem is to find an assignment (which job should be assigned to which person one on-one basis) So that the total cost of performing all jobs is minimum, problem of this kind are known as assignment problem.

The assignment problem can be stated in the form of n x n cost matrix C real members as given in the following table:

Let us explore all approaches for this problem.

Solution 1: Brute Force  

We generate n! possible job assignments and for each such assignment, we compute its total cost and return the less expensive assignment. Since the solution is a permutation of the n jobs, its complexity is O(n!).

Solution 2: Hungarian Algorithm  

The optimal assignment can be found using the Hungarian algorithm. The Hungarian algorithm has worst case run-time complexity of O(n^3).

Solution 3: DFS/BFS on state space tree  

A state space tree is a N-ary tree with property that any path from root to leaf node holds one of many solutions to given problem. We can perform depth-first search on state space tree and but successive moves can take us away from the goal rather than bringing closer. The search of state space tree follows leftmost path from the root regardless of initial state. An answer node may never be found in this approach. We can also perform a Breadth-first search on state space tree. But no matter what the initial state is, the algorithm attempts the same sequence of moves like DFS.

Solution 4: Finding Optimal Solution using Branch and Bound  

The selection rule for the next node in BFS and DFS is “blind”. i.e. the selection rule does not give any preference to a node that has a very good chance of getting the search to an answer node quickly. The search for an optimal solution can often be speeded by using an “intelligent” ranking function, also called an approximate cost function to avoid searching in sub-trees that do not contain an optimal solution. It is similar to BFS-like search but with one major optimization. Instead of following FIFO order, we choose a live node with least cost. We may not get optimal solution by following node with least promising cost, but it will provide very good chance of getting the search to an answer node quickly.

There are two approaches to calculate the cost function:  

• For each worker, we choose job with minimum cost from list of unassigned jobs (take minimum entry from each row).
• For each job, we choose a worker with lowest cost for that job from list of unassigned workers (take minimum entry from each column).

In this article, the first approach is followed.

Let’s take below example and try to calculate promising cost when Job 2 is assigned to worker A. 

Since Job 2 is assigned to worker A (marked in green), cost becomes 2 and Job 2 and worker A becomes unavailable (marked in red). 

Now we assign job 3 to worker B as it has minimum cost from list of unassigned jobs. Cost becomes 2 + 3 = 5 and Job 3 and worker B also becomes unavailable. 

Finally, job 1 gets assigned to worker C as it has minimum cost among unassigned jobs and job 4 gets assigned to worker D as it is only Job left. Total cost becomes 2 + 3 + 5 + 4 = 14. 

Below diagram shows complete search space diagram showing optimal solution path in green. 

Complete Algorithm:  

Below is the implementation of the above approach:

Time Complexity: O(M*N). This is because the algorithm uses a double for loop to iterate through the M x N matrix.  Auxiliary Space: O(M+N). This is because it uses two arrays of size M and N to track the applicants and jobs.

Please Login to comment...

Similar reads.

• Branch and Bound

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Assignment Model | Linear Programming Problem (LPP) | Introduction

What is assignment model.

→ Assignment model is a special application of Linear Programming Problem (LPP) , in which the main objective is to assign the work or task to a group of individuals such that;

i) There is only one assignment.

ii) All the assignments should be done in such a way that the overall cost is minimized (or profit is maximized, incase of maximization).

→ In assignment problem, the cost of performing each task by each individual is known. → It is desired to find out the best assignments, such that overall cost of assigning the work is minimized.

For example:

Suppose there are 'n' tasks, which are required to be performed using 'n' resources.

The cost of performing each task by each resource is also known (shown in cells of matrix)

• In the above asignment problem, we have to provide assignments such that there is one to one assignments and the overall cost is minimized.

How Assignment Problem is related to LPP? OR Write mathematical formulation of Assignment Model.

→ Assignment Model is a special application of Linear Programming (LP).

→ The mathematical formulation for Assignment Model is given below:

→ Let, C i j \text {C}_{ij} C ij ​ denotes the cost of resources 'i' to the task 'j' ; such that

→ Now assignment problems are of the Minimization type. So, our objective function is to minimize the overall cost.

→ Subjected to constraint;

(i) For all j t h j^{th} j t h task, only one i t h i^{th} i t h resource is possible:

(ii) For all i t h i^{th} i t h resource, there is only one j t h j^{th} j t h task possible;

(iii) x i j x_{ij} x ij ​ is '0' or '1'.

Types of Assignment Problem:

(i) balanced assignment problem.

• It consist of a suqare matrix (n x n).
• Number of rows = Number of columns

(ii) Unbalanced Assignment Problem

• It consist of a Non-square matrix.
• Number of rows ≠ \not=  = Number of columns

Methods to solve Assignment Model:

(i) integer programming method:.

In assignment problem, either allocation is done to the cell or not.

So this can be formulated using 0 or 1 integer.

While using this method, we will have n x n decision varables, and n+n equalities.

So even for 4 x 4 matrix problem, it will have 16 decision variables and 8 equalities.

So this method becomes very lengthy and difficult to solve.

(ii) Transportation Methods:

As assignment problem is a special case of transportation problem, it can also be solved using transportation methods.

In transportation methods ( NWCM , LCM & VAM), the total number of allocations will be (m+n-1) and the solution is known as non-degenerated. (For eg: for 3 x 3 matrix, there will be 3+3-1 = 5 allocations)

But, here in assignment problems, the matrix is a square matrix (m=n).

So total allocations should be (n+n-1), i.e. for 3 x 3 matrix, it should be (3+3-1) = 5

But, we know that in 3 x 3 assignment problem, maximum possible possible assignments are 3 only.

So, if are we will use transportation methods, then the solution will be degenerated as it does not satisfy the condition of (m+n-1) allocations.

So, the method becomes lengthy and time consuming.

(iii) Enumeration Method:

It is a simple trail and error type method.

Consider a 3 x 3 assignment problem. Here the assignments are done randomly and the total cost is found out.

For 3 x 3 matrix, the total possible trails are 3! So total 3! = 3 x 2 x 1 = 6 trails are possible.

The assignments which gives minimum cost is selected as optimal solution.

But, such trail and error becomes very difficult and lengthy.

If there are more number of rows and columns, ( For eg: For 6 x 6 matrix, there will be 6! trails. So 6! = 6 x 5 x 4 x 3 x 2 x 1 = 720 trails possible) then such methods can't be applied for solving assignments problems.

(iv) Hungarian Method:

It was developed by two mathematicians of Hungary. So, it is known as Hungarian Method.

It is also know as Reduced matrix method or Flood's technique.

There are two main conditions for applying Hungarian Method:

(1) Square Matrix (n x n). (2) Problem should be of minimization type.

Suggested Notes:

Modified Distribution Method (MODI) | Transportation Problem | Transportation Model

Stepping Stone | Transportation Problem | Transportation Model

Vogel’s Approximation Method (VAM) | Method to Solve Transportation Problem | Transportation Model

Transportation Model - Introduction

North West Corner Method | Method to Solve Transportation Problem | Transportation Model

Least Cost Method | Method to Solve Transportation Problem | Transportation Model

Tie in selecting row and column (Vogel's Approximation Method - VAM) | Numerical | Solving Transportation Problem | Transportation Model

Crashing Special Case - Multiple (Parallel) Critical Paths

Crashing Special Case - Indirect cost less than Crash Cost

Basics of Program Evaluation and Review Technique (PERT)

Numerical on PERT (Program Evaluation and Review Technique)

Network Analysis - Dealing with Network Construction Basics

Construct a project network with predecessor relationship | Operation Research | Numerical

Graphical Method | Methods to solve LPP | Linear Programming

Basics of Linear Programming

Linear Programming Problem (LPP) Formulation with Numericals

All comments that you add will await moderation. We'll publish all comments that are topic related, and adhere to our Code of Conduct .

Want to tell us something privately? Contact Us

Post comment

Education Lessons

Stay in touch, [notes] operation research, [notes] dynamics of machinery, [notes] maths, [notes] science, [notes] computer aided design.

An official website of the United States government

Here’s how you know

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Take action

• Report an antitrust violation
• File adjudicative documents
• Find banned debt collectors
• View competition guidance
• Competition Matters Blog

New HSR thresholds and filing fees for 2024

View all Competition Matters Blog posts

We work to advance government policies that protect consumers and promote competition.

View Policy

Search or browse the Legal Library

Find legal resources and guidance to understand your business responsibilities and comply with the law.

Browse legal resources

• Find policy statements
• Submit a public comment

Vision and Priorities

Memo from Chair Lina M. Khan to commission staff and commissioners regarding the vision and priorities for the FTC.

Technology Blog

Consumer facing applications: a quote book from the tech summit on ai.

View all Technology Blog posts

Advice and Guidance

Learn more about your rights as a consumer and how to spot and avoid scams. Find the resources you need to understand how consumer protection law impacts your business.

• Report fraud
• Report identity theft
• Register for Do Not Call
• Sign up for consumer alerts
• Get Business Blog updates
• Get your free credit report
• Find refund cases
• Order bulk publications
• Consumer Advice
• Shopping and Donating
• Credit, Loans, and Debt
• Jobs and Making Money
• Unwanted Calls, Emails, and Texts
• Identity Theft and Online Security
• Business Guidance
• Advertising and Marketing
• Credit and Finance
• Privacy and Security
• By Industry
• For Small Businesses
• Browse Business Guidance Resources
• Business Blog

Servicemembers: Your tool for financial readiness

Visit militaryconsumer.gov

Get consumer protection basics, plain and simple

Visit consumer.gov

Learn how the FTC protects free enterprise and consumers

Visit Competition Counts

Looking for competition guidance?

• Competition Guidance

News and Events

Latest news, ftc finalizes changes to the health breach notification rule.

View News and Events

Upcoming Event

Older adults and fraud: what you need to know.

View more Events

Sign up for the latest news

Follow us on social media

-->   -->   -->   -->   -->  

Playing it Safe: Explore the FTC's Top Video Game Cases

Learn about the FTC's notable video game cases and what our agency is doing to keep the public safe.

Latest Data Visualization

FTC Refunds to Consumers

Explore refund statistics including where refunds were sent and the dollar amounts refunded with this visualization.

About the FTC

Our mission is protecting the public from deceptive or unfair business practices and from unfair methods of competition through law enforcement, advocacy, research, and education.

Learn more about the FTC

Meet the Chair

Lina M. Khan was sworn in as Chair of the Federal Trade Commission on June 15, 2021.

Chair Lina M. Khan

Looking for legal documents or records? Search the Legal Library instead.

• Cases and Proceedings
• Premerger Notification Program
• Merger Review
• Anticompetitive Practices
• Competition and Consumer Protection Guidance Documents
• Warning Letters
• Consumer Sentinel Network
• Criminal Liaison Unit
• FTC Refund Programs
• Notices of Penalty Offenses
• Advocacy and Research
• Advisory Opinions
• Cooperation Agreements
• Federal Register Notices
• Public Comments
• Policy Statements
• International
• Office of Technology Blog
• Military Consumer
• Consumer.gov
• Bulk Publications
• Data and Visualizations
• Stay Connected
• Commissioners and Staff
• Bureaus and Offices
• Budget and Strategy
• Office of Inspector General
• Careers at the FTC

Fact Sheet on FTC’s Proposed Final Noncompete Rule

• Competition
• Office of Policy Planning
• Bureau of Competition

The following outline provides a high-level overview of the FTC’s proposed final rule :

• Specifically, the final rule provides that it is an unfair method of competition—and therefore a violation of Section 5 of the FTC Act—for employers to enter into noncompetes with workers after the effective date.
• Fewer than 1% of workers are estimated to be senior executives under the final rule.
• Specifically, the final rule defines the term “senior executive” to refer to workers earning more than $151,164 annually who are in a “policy-making position.”
• Reduced health care costs: $74-$194 billion in reduced spending on physician services over the next decade.
• New business formation: 2.7% increase in the rate of new firm formation, resulting in over 8,500 additional new businesses created each year.
• This reflects an estimated increase of about 3,000 to 5,000 new patents in the first year noncompetes are banned, rising to about 30,000-53,000 in the tenth year.
• This represents an estimated increase of 11-19% annually over a ten-year period.
• The average worker’s earnings will rise an estimated extra $524 per year. 

The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the  FTC on social media , read  consumer alerts  and the  business blog , and  sign up to get the latest FTC news and alerts .

Press Release Reference

Contact information, media contact.

Victoria Graham Office of Public Affairs 415-848-5121

Breaking News

I’m a hand model in nyc — i’m earning $30k a year just holding things on camera.

• View Author Archive
• Follow on Twitter
• Get author RSS feed

Thanks for contacting us. We've received your submission.

She’s top of her field, hands down.

New York beauty Alexandra Berrocal is as hardworking as any other model in the Big Apple — except that a day on the job doesn’t require much heavy lifting for the Brooklyn resident, who earns $30,000 a year simply by showing off her hands.

“It’s a very niche industry,” the 37-year-old told The Post. “There’s not that many people that know it’s even a real thing.”

“A lot of times, I’ll tell my friends like what I did for a gig and they’re, like, ‘What? You got paid to pour coffee into a cup or, like, squeeze product into your hands and rub it around like that?'” she added.

Berrocal started modeling around 2019, meting out her mitts to brands like YSL, Microsoft, Brandon Blackwood, Macy’s, Zales, Shake Shack, Kiss Nails and Serena Williams Jewelry, landing these gigs by finding “parts modeling agencies” to represent her.

“I submitted [my portfolio], and then my agent got back to me in, like, 10 minutes, and I was, like, wow, that’s only the favor of the Lord right there,” Berrocal said.

Although she earns $30,000 a year, Berrocal has a full-time job, too — ironically enough, in the footwear design industry.

Berrocal’s gigs aren’t consistent, but she doesn’t bite the hand that feeds her: She’s made anywhere from $750 for a five-hour shoot to $1,200 for a 40-minute shoot.

“One month can be two to three times, and then another month could be, like, 10 times,” she added. “You never know.”

In the four years she’s been passing her palms around, Berrocal said she learned that brands prefer even skin complexions, slender fingers, nice nail beds and clear skin without tattoos or scars — unless the company is willing to cover it with makeup.

Most importantly, she believes having small metacarpi has allowed her to book more castings.

“I actually have small hands, but I feel like it works in my favor because I mostly do a lot of beauty product shoots. And I think because my hand is so small, I make the product look bigger,” she said.

She has explored other sectors of parts modeling, attending to casting calls for her feet, eyes, arms, legs and shoulders — but it seems she’s already discovered her golden goose, or geese.

“I’ve done two casting calls for feet which I’m not surprised I didn’t get because I don’t think I have nice feet, but my agent asked me to go so I went,” she joked.

Online, Berrocal, who goes by @iamalexberrocal on  Instagram  and  TikTok , is hands-on while showcasing her unique career as a grabbers girl.

“Hand modeling is all about doing the simple actions that we usually do, [such as] picking up a product, putting something down, but making it look pretty,” the New York native said in a  TikTok post  on Sunday. 

As for upkeep, the Brooklynite keeps her nails well manicured and in a short almond shape — unless she is advised otherwise for a shoot.

She protects her money-makers by always staying moisturized.

“Before I go to sleep, I lather on some shea butter and that’s it. I don’t use any special lotions. I just use 100% shea butter,” she said.

And those with their eye on a carpal career, Berrocal says there are some key things to remember, such as wearing gloves when washing the dishes.

“If you wash your hands, make sure to put lotion on immediately after, because your hands are one of the first, places that show aging,” she said.

Berrocal said she works overtime to keep her dainty dukes in pristine condition since she hopes to one day be all hands on deck.

“I would love for it to be my career,” she gushed. “I’d like to do hand modeling for as long as I can.”

Share this article:

Advertisement

Tornado Crosses Over Nebraska Freeway, Wild Video Shows

Bikini Babes In Cowboy Hats ... Well Hay There Hollywood Hotties!

Zendaya's 'Challengers' Smash at Box Office, Audience Reviews More Mixed

Miranda Lambert Good Genes or Good Docs?!

Harry Jowsey Announces Skin Cancer Diagnosis

Kamala harris' secret service agent attacked supervisor, off assignment, kamala harris secret service agent off assignment ... attacked supervisor.

Kamala Harris ' Secret Service team faced a violent threat this week, having to defend one of their agents ... from one of their own!!!

Secret Service representative Anthony Guglielmi tells TMZ ... at around 9 AM Monday, a U.S. Secret Service special agent supporting the Vice President’s departure from Joint Base Andrews started to display signs his colleagues found distressing.

We're told they removed the agent from the assignment and summoned medical personnel. The Veep wasn't around when the incident occurred, and it did not affect her takeoff.

A federal source with direct knowledge tells us at least one of the distressing signs displayed by the agent was obvious ... as the agent attacked the detail's supervising agent.

Other outlets are reporting the agent who attacked the supervisor was reportedly handcuffed after the incident.

Harris was reportedly on her way from Joint Base Andrews in Maryland to New York City ... where she was scheduled to appear on "The Drew Barrymore Show."

The Vice President was reportedly notified about the fight, though she hasn't made any public statement about the incident.

• Share on Facebook

related articles

White House Hosts Rich, Powerful and Sexy for Lavish State Dinner

Joe Biden Again Reads Teleprompter Instruction During Speech, 'Pause'

Old news is old news be first.