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Job Assignment Problem using Branch And Bound

• N Queen Problem using Branch And Bound
• Traveling Salesman Problem using Branch And Bound

Let there be N workers and N jobs. Any worker can be assigned to perform any job, incurring some cost that may vary depending on the work-job assignment. It is required to perform all jobs by assigning exactly one worker to each job and exactly one job to each agent in such a way that the total cost of the assignment is minimized.

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.

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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:

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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:

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 .

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Assignment problem

The problem of optimally assigning $ m $ individuals to $ m $ jobs. It can be formulated as a linear programming problem that is a special case of the transport problem :

maximize $ \sum _ {i,j } c _ {ij } x _ {ij } $

$$ \sum _ { j } x _ {ij } = a _ {i} , i = 1 \dots m $$

(origins or supply),

$$ \sum _ { i } x _ {ij } = b _ {j} , j = 1 \dots n $$

(destinations or demand), where $ x _ {ij } \geq 0 $ and $ \sum a _ {i} = \sum b _ {j} $, which is called the balance condition. The assignment problem arises when $ m = n $ and all $ a _ {i} $ and $ b _ {j} $ are $ 1 $.

If all $ a _ {i} $ and $ b _ {j} $ in the transposed problem are integers, then there is an optimal solution for which all $ x _ {ij } $ are integers (Dantzig's theorem on integral solutions of the transport problem).

In the assignment problem, for such a solution $ x _ {ij } $ is either zero or one; $ x _ {ij } = 1 $ means that person $ i $ is assigned to job $ j $; the weight $ c _ {ij } $ is the utility of person $ i $ assigned to job $ j $.

The special structure of the transport problem and the assignment problem makes it possible to use algorithms that are more efficient than the simplex method . Some of these use the Hungarian method (see, e.g., [a5] , [a1] , Chapt. 7), which is based on the König–Egervary theorem (see König theorem ), the method of potentials (see [a1] , [a2] ), the out-of-kilter algorithm (see, e.g., [a3] ) or the transportation simplex method.

In turn, the transportation problem is a special case of the network optimization problem.

A totally different assignment problem is the pole assignment problem in control theory.

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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.

Procedure, Example Solved Problem | Operations Research - Solution of assignment problems (Hungarian Method) | 12th Business Maths and Statistics : Chapter 10 : Operations Research

Chapter: 12th business maths and statistics : chapter 10 : operations research.

Solution of assignment problems (Hungarian Method)

First check whether the number of rows is equal to the numbers of columns, if it is so, the assignment problem is said to be balanced.

Step :1 Choose the least element in each row and subtract it from all the elements of that row.

Step :2 Choose the least element in each column and subtract it from all the elements of that column. Step 2 has to be performed from the table obtained in step 1.

Step:3 Check whether there is atleast one zero in each row and each column and make an assignment as follows.

Step :4 If each row and each column contains exactly one assignment, then the solution is optimal.

Example 10.7

Solve the following assignment problem. Cell values represent cost of assigning job A, B, C and D to the machines I, II, III and IV.

Here the number of rows and columns are equal.

∴ The given assignment problem is balanced. Now let us find the solution.

Step 1: Select a smallest element in each row and subtract this from all the elements in its row.

Look for atleast one zero in each row and each column.Otherwise go to step 2.

Step 2: Select the smallest element in each column and subtract this from all the elements in its column.

Since each row and column contains atleast one zero, assignments can be made.

Step 3 (Assignment):

Thus all the four assignments have been made. The optimal assignment schedule and total cost is

The optimal assignment (minimum) cost

Example 10.8

Consider the problem of assigning five jobs to five persons. The assignment costs are given as follows. Determine the optimum assignment schedule.

∴ The given assignment problem is balanced.

Now let us find the solution.

The cost matrix of the given assignment problem is

Column 3 contains no zero. Go to Step 2.

Thus all the five assignments have been made. The Optimal assignment schedule and total cost is

The optimal assignment (minimum) cost = ` 9

Example 10.9

Solve the following assignment problem.

Since the number of columns is less than the number of rows, given assignment problem is unbalanced one. To balance it , introduce a dummy column with all the entries zero. The revised assignment problem is

Here only 3 tasks can be assigned to 3 men.

Step 1: is not necessary, since each row contains zero entry. Go to Step 2.

Step 3 (Assignment) :

Since each row and each columncontains exactly one assignment,all the three men have been assigned a task. But task S is not assigned to any Man. The optimal assignment schedule and total cost is

The optimal assignment (minimum) cost = ₹ 35

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SGAI-AI 2022: Artificial Intelligence XXXIX pp 19–33 Cite as

Job Assignment Problem and Traveling Salesman Problem: A Linked Optimisation Problem

• Akinola Ogunsemi 9 ,
• John McCall 9 ,
• Mathias Kern 11 ,
• Benjamin Lacroix 9 ,
• David Corsar 10 &
• Gilbert Owusu 11  
• Conference paper
• First Online: 05 December 2022

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13652))

Linked decision-making in service management systems has attracted strong adoption of optimisation algorithms. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems. This paper, therefore, investigates the linkages between two classical problems: job assignment problem and travelling salesman problem (JAPTSP) of a service chain system where service personnel perform tasks at different locations. We formulate a novel mathematical model from a linked optimisation perspective with objectives to minimise job cost and total travel distance simultaneously. We present three algorithmic approaches to tackling the JAPTSP: Nondominated Sorting Genetic Algorithm for Linked Problem (NSGALP), Multi-Criteria Ranking Genetic Algorithm for Linked Problem (MCRGALP), and Sequential approach. We evaluate the performance of the three algorithmic approaches on a combination of JAP and TSP benchmark instances. Results show that selecting an appropriate algorithmic approach is highly driven by specific considerations, including multi-objective base performance metrics, computation time, problem correlation and qualitative analysis from a service chain perspective.

• Service chain optimisation
• Linked optimisation problem
• Multi-criteria decision making
• Multi-objective optimisation

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Ibrahimov, M., Mohais, A., Schellenberg, S., Michalewicz, Z.: Evolutionary approaches for supply chain optimisation: part i: single and two-component supply chains. IJICC 5 (4), 444–472 (2012)

Article   MathSciNet   Google Scholar  

Bonyadi, M.R., Michalewicz, Z., Barone, L.: The travelling thief problem: The first step in the transition from theoretical problems to realistic problems. In: IEEE CEC. IEEE 2013 , 1037–1044 (2013)

Google Scholar  

Vieira, D.K.S., Soares, G.L., Vasconcelos, J.A., Mendes, M.H.S.: A genetic algorithm for multi-component optimization problems: the case of the travelling thief problem. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 18–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55453-2_2

Chapter   Google Scholar  

Xie, F., Potts, C.N., Bektaş, T.: Iterated local search for workforce scheduling and routing problems. JH, vol. 23, no. 6, pp. 471–500, 2017

Conti, G., Dow, A.: The impacts of covid-19 on health visiting services in england: Foi evidence for the first wave (2020)

Castillo-Salazar, A., Landa-Silva, D., Qu, R.: A survey of workforce scheduling and routing (2012)

Castillo-Salazar, J.A., Landa-Silva, D., Qu, R.: A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints (2015)

Camci, F.: The travelling maintainer problem: integration of condition-based maintenance with the travelling salesman problem. JORS 65 (9), 1423–1436 (2014)

Zhang, T., Gruver, W., Smith, M.H.: Team scheduling by genetic search. In: IPMM’99 (Cat. No. 99EX296), vol. 2. IEEE, pp. 839–844 (1999)

Assaf, M., Ndiaye, M.,: Multi travelling salesman problem formulation. In: 4th ICIEA. IEEE 2017 , 292–295 (2017)

Shuai, Y., Yunfeng, S., Kai, Z.: An effective method for solving multiple travelling salesman problem based on nsga-ii. SSCE 7 (2), 108–116 (2019)

Stolk, J., Mann, I., Mohais, A., Michalewicz, Z.: Combining vehicle routing and packing for optimal delivery schedules of water tanks. OR Insight 26 (3), 167–190 (2013)

Article   Google Scholar  

Chen, L., Langevin, A., Lu, Z.: Integrated scheduling of crane handling and truck transportation in a maritime container terminal. EJOR 225 (1), 142–152 (2013)

Article   MATH   Google Scholar  

Chen, T.-L., Cheng, C.-Y., Chen, Y.-Y., Chan, L.-K.: An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. IJPE 159 , 158–167 (2015)

Moons, S., Ramaekers, K., Caris, A., Arda, Y.: Integrating production scheduling and vehicle routing decisions at the operational decision level: a review and discussion. CIE 104 , 224–245 (2017)

Chu, P.C., Beasley, J.E.: A genetic algorithm for the generalised assignment problem. COR 24 (1), 17–23 (1997)

MathSciNet   MATH   Google Scholar  

Gerhard, R.: The traveling salesman: computational solutions for tsp applications. Lect. Notes Comput. Sci. 840 , 1–223 (1994)

Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE TEC 6 (2), 182–197 (2002)

Muhuri, P.K., Ashraf, Z., Lohani, Q.D.: Multiobjective reliability redundancy allocation problem with interval type-2 fuzzy uncertainty. IEEE TFS 26 (3), 1339–1355 (2017)

Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. In: Lecture Notes in Economics and Mathematical Systems, vol 186, pp. 58–191 Springer, Berlin, Heidelberg (1981). https://doi.org/10.1007/978-3-642-48318-9_3

Rahim, R., et al.: Topsis method application for decision support system in internal control for selecting best employees. In: JP: Conference Series, vol. 1028, no. 1. IOP Publishing, p. 012052 (2018)

Triantaphyllou, E., Shu, B., Sanchez, S.N., Ray, T.: Multi-criteria decision making: an operations research approach. EEEE 15 (1998), 175–186 (1998)

Luo, G., Wen, X., Li, H., Ming, W., Xie, G.: An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling. IJAMT 91 (9), 3145–3158 (2017)

Geetha, T., Muthukumaran, K.: An observational analysis of genetic operators. IJCA 63 (18), 24–34 (2013)

Legillon, F., Liefooghe, A., Talbi, E.-G.: Cobra: A cooperative coevolutionary algorithm for bi-level optimization. In: IEEE CEC. IEEE 2012 , 1–8 (2012)

Ibrahimov, M.: Evolutionary algorithms for supply chain optimisation. Ph.D. dissertation (2012)

Lin, C.K.Y.: Solving a location, allocation, and capacity planning problem with dynamic demand and response time service level. MPE, vol. (2014)

Ullrich, C.A.: Integrated machine scheduling and vehicle routing with time windows. EJOR 227 (1), 152–165 (2013)

Article   MathSciNet   MATH   Google Scholar  

Nourmohammadi, A., Zandieh, M.: Assembly line balancing by a new multi-objective differential evolution algorithm based on topsis. IJPR 49 (10), 2833–2855 (2011)

Beasley, J.E.: Or-library: distributing test problems by electronic mail. JORS 41 (11), 1069–1072 (1990)

Reinelt, G.: Tsplib95. IWR, Heidelberg 338 , 1–16 (1995)

Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE TEC 3 (4), 257–271 (1999)

Bezerra, L.C.T., López-Ibáñez, M., Stützle, T.: An empirical assessment of the properties of inverted generational distance on multi- and many-objective optimization. In: Trautmann, H. (ed.) EMO 2017. LNCS, vol. 10173, pp. 31–45. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54157-0_3

Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE TEC 7 (2), 117–132 (2003)

Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms — a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056872

Manson, J.A., Chamberlain, T.W., Bourne, R.A.: Mvmoo: Mixed variable multi-objective optimisation. JGO 80 (4), 865–886 (2021)

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Ogunsemi, A., McCall, J., Kern, M., Lacroix, B., Corsar, D., Owusu, G. (2022). Job Assignment Problem and Traveling Salesman Problem: A Linked Optimisation Problem. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XXXIX. SGAI-AI 2022. Lecture Notes in Computer Science(), vol 13652. Springer, Cham. https://doi.org/10.1007/978-3-031-21441-7_2

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More Teachers Are Using AI-Detection Tools. Here’s Why That Might Be a Problem

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As ChatGPT and similar technologies have gained prominence in middle and high school classrooms, so, too, have AI-detection tools. The majority of teachers have used an AI-detection program to assess whether a student’s work was completed with the assistance of generative AI, according to a new survey of educators by the Center for Democracy & Technology . And students are increasingly getting disciplined for using generative AI.

But while detection software can help overwhelmed teachers feel like they are staying one step ahead of their students, there is a catch: AI detection tools are imperfect, said Victor Lee, an associate professor of learning sciences and technology design and STEM education at the Stanford Graduate School of Education.

“They are fallible, you can work around them,” he said. “And there is a serious harm risk associated in that an incorrect accusation is a very serious accusation to make.”

A false positive from an AI-detection tool is a scary prospect for many students, said Soumil Goyal, a senior at an International Baccalaureate high school in Houston.

“For example, my teacher might say, ‘In my previous class I had six students come up through the AI-detection test,’” he said, although he’s unsure if this is true or if his teachers might be using this as a scare tactic. “If I was ever faced with a teacher, and in his mind he is 100 percent certain that I did use AI even though I didn’t, that’s a tough scenario. [...] It can be very harmful to the student.”

Schools are adapting to growing AI use but concerns remain

In general, the survey by the Center for Democracy & Technology, a nonprofit organization that aims to shape technology policy, with an emphasis on protecting consumer rights, finds that generative AI products are becoming more a part of teachers’ and students’ daily lives, and schools are adjusting to that new reality. The survey included a nationally representative sample of 460 6th through 12th grade public school teachers in December of last year.

Most teachers—59 percent—believe their students are using generative AI products for school purposes. Meanwhile, 83 percent of teachers say they have used ChatGPT or similar products for personal or school use, representing a 32 percentage point increase since the Center for Democracy & Technology surveyed teachers last year.

The survey also found that schools are adapting to this new technology. More than 8 in 10 teachers say their schools now have policies either that outline whether generative AI tools are permitted or banned and that they have had training on those policies, a drastic change from last year when many schools were still scrambling to figure out a response to a technology that can write essays and solve complex math problems for students.

And nearly three-quarters of teachers say their schools have asked them for input on developing policies and procedures around students’ use of generative AI.

Overall, teachers gave their schools good marks when it comes to responding to the challenges created by students using generative AI—73 percent of teachers said their school and district are doing a good job.

That’s the good news, but the survey data reveals some troubling trends as well.

Far fewer teachers report receiving training on appropriate student use of AI and how teachers should respond if they think students are abusing the technology.

• Twenty-eight percent of teachers said they have received guidance on how to respond if they think a student is using ChatGPT;
• Thirty-seven percent said they have received guidance on what responsible student use of generative AI technologies looks like;
• Thirty-seven percent also say they have not received guidance on how to detect whether students are using generative AI in their school assignments;
• And 78 percent said their school sanctions the use of AI detection tools.

Only a quarter of teachers said they are “very effective” at discerning whether assignments were written by their students or by an AI tool. Half of teachers say generative AI has made them more distrustful that students’ schoolwork is actually their own.

A lack of training coupled with a lack of faith in students’ work products may explain why teachers are reporting that students are increasingly being punished for using generative AI in their assignments, even as schools are permitting more student use of AI, the report said.

Taken together, this makes the fact that so many teachers are using AI detection software—68 percent, up substantially from last year—concerning, the report said.

“Teachers are becoming reliant on AI content-detection tools, which is problematic given that research shows these tools are not consistently effective at differentiating between AI-generated and human-written text,” the report said. “This is especially concerning given the concurrent increase in student disciplinary action.”

Simply confronting students with the accusation that they used AI can lead to punishment, the report found. Forty percent of teachers said that a student got in trouble for how they reacted when a teacher or principal approached them about misusing AI.

What role should AI detectors play in schools’ fight against cheating?

Schools should critically examine the role of AI-detection software in policing students’ use of generative AI, said Lee, the professor from Stanford.

“The comfort level we have about what is an acceptable error rate is a loaded question—would we accept one percent of students being incorrectly labeled or accused? That’s still a lot of students,” he said.

A false accusation could carry wide-ranging consequences.

“It could put a label on a student that could have longer term effects on the students’ standing or disciplinary record,” he said. “It could also alienate them from school, because if it was not AI produced text, and they wrote it and were told it’s bad, that is not a very affirming message.”

Additionally, some research has found that AI detection tools are more likely to falsely identify English learners’ writing as produced by AI.

Low-income students may also be more likely to get in trouble for using AI, the CDT report said because they are more likely to use school-issued devices. Nearly half the teachers in the survey agree that students who use school-provided devices are more likely to get in trouble for using generative AI.

The report notes that students in special education use generative AI more often than their peers and special education teachers are more likely to say they use AI-detection tools regularly.

Research is also finding that there are ways to trick AI detection systems, said Lee. And schools need to think about the tradeoffs in time and resources of keeping abreast with inevitable developments both in AI, AI-detection tools , and students’ skills at getting around those tools.

Lee said he sees why detection tools would be attractive to overwhelmed teachers. But he doesn’t think that AI detection tools should alone determine whether a student is improperly using AI to do their schoolwork. It could be one data point among several used to determine whether students are breaking any—what should be clearly defined—rules.

In Poland, Maine, Shawn Vincent is the principal of the Bruce Whittier middle school, serving about 200 students. He said that he hasn’t had too many problems with students using generative AI programs to cheat. Teachers have used AI-detection tools as a check on their gut instincts when they have suspicions that a student has improperly used generative AI.

“For example, we had a teacher recently who had students writing paragraphs about Supreme Court cases, and a student used AI to generate answers to the questions,” he said. “For her, it did not match what she had seen from the student in the past, so she went online to use one of the tools that are available to check for AI usage. That’s what she used as her decider.”

When the teacher approached the student, Vincent said, the student admitted to using a generative AI tool to write the answers.

Teachers are also meeting the challenge by changing their approaches to assigning schoolwork, such as requiring students to write essays by hand in class, Vincent said. And although he’s unsure about how to formulate policies to address students’ AI use, he wants to approach the issue first as a learning opportunity.

“These are middle school kids. They are learning about a lot of things this time in their life. So we try to use it as an educational opportunity,” he said. “I think we are all learning about AI together.”

Speaking from a robotics competition in Houston , Goyal, the high school student from Houston, said that sometimes he and his friends trade ideas for tricking AI-detection systems, although he said he doesn’t use ChatGPT to do the bulk of his assignments. When he uses it, it’s to generate ideas or check grammar, he said.

Goyal, who wants to work in robotics when he graduates from college, worries that some of his teachers don’t really understand how AI detection tools work and that they may be putting too much trust in the technology.

“The school systems should educate their teachers that their AI-detection tool is not a plagiarism detector [...] that can give you a direct link to what was plagiarized from,” he said. “It’s also a little bit like a hypocrisy: The teachers will say: Don’t use AI because it is very inaccurate and it will make up things. But then they use AI to detect AI.”

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Guest Essay

The Problem With Saying ‘Sex Assigned at Birth’

By Alex Byrne and Carole K. Hooven

Mr. Byrne is a philosopher and the author of “Trouble With Gender: Sex Facts, Gender Fictions.” Ms. Hooven is an evolutionary biologist and the author of “T: The Story of Testosterone, the Hormone That Dominates and Divides Us.”

As you may have noticed, “sex” is out, and “sex assigned at birth” is in. Instead of asking for a person’s sex, some medical and camp forms these days ask for “sex assigned at birth” or “assigned sex” (often in addition to gender identity). The American Medical Association and the American Psychological Association endorse this terminology; its use has also exploded in academic articles. The Cleveland Clinic’s online glossary of diseases and conditions tells us that the “inability to achieve or maintain an erection” is a symptom of sexual dysfunction, not in “males,” but in “people assigned male at birth.”

This trend began around a decade ago, part of an increasing emphasis in society on emotional comfort and insulation from offense — what some have called “ safetyism .” “Sex” is now often seen as a biased or insensitive word because it may fail to reflect how people identify themselves. One reason for the adoption of “assigned sex,” therefore, is that it supplies respectful euphemisms, softening what to some nonbinary and transgender people, among others, can feel like a harsh biological reality. Saying that someone was “assigned female at birth” is taken to be an indirect and more polite way of communicating that the person is biologically female. The terminology can also function to signal solidarity with trans and nonbinary people, as well as convey the radical idea that our traditional understanding of sex is outdated.

The shift to “sex assigned at birth” may be well intentioned, but it is not progress. We are not against politeness or expressions of solidarity, but “sex assigned at birth” can confuse people and creates doubt about a biological fact when there shouldn’t be any. Nor is the phrase called for because our traditional understanding of sex needs correcting — it doesn’t.

This matters because sex matters. Sex is a fundamental biological feature with significant consequences for our species, so there are costs to encouraging misconceptions about it.

Sex matters for health, safety and social policy and interacts in complicated ways with culture. Women are nearly twice as likely as men to experience harmful side effects from drugs, a problem that may be ameliorated by reducing drug doses for females. Males, meanwhile, are more likely to die from Covid-19 and cancer, and commit the vast majority of homicides and sexual assaults . We aren’t suggesting that “assigned sex” will increase the death toll. However, terminology about important matters should be as clear as possible.

More generally, the interaction between sex and human culture is crucial to understanding psychological and physical differences between boys and girls, men and women. We cannot have such understanding unless we know what sex is, which means having the linguistic tools necessary to discuss it. The Associated Press cautions journalists that describing women as “female” may be objectionable because “it can be seen as emphasizing biology,” but sometimes biology is highly relevant. The heated debate about transgender women participating in female sports is an example ; whatever view one takes on the matter, biologically driven athletic differences between the sexes are real.

When influential organizations and individuals promote “sex assigned at birth,” they are encouraging a culture in which citizens can be shamed for using words like “sex,” “male” and “female” that are familiar to everyone in society, as well as necessary to discuss the implications of sex. This is not the usual kind of censoriousness, which discourages the public endorsement of certain opinions. It is more subtle, repressing the very vocabulary needed to discuss the opinions in the first place.

A proponent of the new language may object, arguing that sex is not being avoided, but merely addressed and described with greater empathy. The introduction of euphemisms to ease uncomfortable associations with old words happens all the time — for instance “plus sized” as a replacement for “overweight.” Admittedly, the effects may be short-lived , because euphemisms themselves often become offensive, and indeed “larger-bodied” is now often preferred to “plus sized.” But what’s the harm? No one gets confused, and the euphemisms allow us to express extra sensitivity. Some see “sex assigned at birth” in the same positive light: It’s a way of talking about sex that is gender-affirming and inclusive .

The problem is that “sex assigned at birth”— unlike “larger-bodied”— is very misleading. Saying that someone was “assigned female at birth” suggests that the person’s sex is at best a matter of educated guesswork. “Assigned” can connote arbitrariness — as in “assigned classroom seating” — and so “sex assigned at birth” can also suggest that there is no objective reality behind “male” and “female,” no biological categories to which the words refer.

Contrary to what we might assume, avoiding “sex” doesn’t serve the cause of inclusivity: not speaking plainly about males and females is patronizing. We sometimes sugarcoat the biological facts for children, but competent adults deserve straight talk. Nor are circumlocutions needed to secure personal protections and rights, including transgender rights. In the Supreme Court’s Bostock v. Clayton County decision in 2020, which outlawed workplace discrimination against gay and transgender people, Justice Neil Gorsuch used “sex,” not “sex assigned at birth.”

A more radical proponent of “assigned sex” will object that the very idea of sex as a biological fact is suspect. According to this view — associated with the French philosopher Michel Foucault and, more recently, the American philosopher Judith Butler — sex is somehow a cultural production, the result of labeling babies male or female. “Sex assigned at birth” should therefore be preferred over “sex,” not because it is more polite, but because it is more accurate.

This position tacitly assumes that humans are exempt from the natural order. If only! Alas, we are animals. Sexed organisms were present on Earth at least a billion years ago, and males and females would have been around even if humans had never evolved. Sex is not in any sense the result of linguistic ceremonies in the delivery room or other cultural practices. Lonesome George, the long-lived Galápagos giant tortoise , was male. He was not assigned male at birth — or rather, in George’s case, at hatching. A baby abandoned at birth may not have been assigned male or female by anyone, yet the baby still has a sex. Despite the confusion sown by some scholars, we can be confident that the sex binary is not a human invention.

Another downside of “assigned sex” is that it biases the conversation away from established biological facts and infuses it with a sociopolitical agenda, which only serves to intensify social and political divisions. We need shared language that can help us clearly state opinions and develop the best policies on medical, social and legal issues. That shared language is the starting point for mutual understanding and democratic deliberation, even if strong disagreement remains.

What can be done? The ascendance of “sex assigned at birth” is not an example of unhurried and organic linguistic change. As recently as 2012 The New York Times reported on the new fashion for gender-reveal parties, “during which expectant parents share the moment they discover their baby’s sex.” In the intervening decade, sex has gone from being “discovered” to “assigned” because so many authorities insisted on the new usage. In the face of organic change, resistance is usually futile. Fortunately, a trend that is imposed top-down is often easier to reverse.

Admittedly, no one individual, or even a small group, can turn the lumbering ship of English around. But if professional organizations change their style guides and glossaries, we can expect that their members will largely follow suit. And organizations in turn respond to lobbying from their members. Journalists, medical professionals, academics and others have the collective power to restore language that more faithfully reflects reality. We will have to wait for them to do that.

Meanwhile, we can each apply Strunk and White’s famous advice in “The Elements of Style” to “sex assigned at birth”: omit needless words.

Alex Byrne is a professor of philosophy at M.I.T. and the author of “Trouble With Gender: Sex Facts, Gender Fictions.” Carole K. Hooven is an evolutionary biologist, a nonresident senior fellow at the American Enterprise Institute, an associate in the Harvard psychology department, and the author of “T: The Story of Testosterone, the Hormone That Dominates and Divides Us.”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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Cosmetology Manager - Temporary Assignment (TA)/Project Hire (PH)

Job summary:.

Through innovative storytelling and a culture of collaboration, Disney Live Entertainment creates, produces, and delivers remarkable and engaging entertainment experiences. From the intimate to the spectacular, our work can be seen at Disney theme parks, resort hotels, cruise ships, and other locations the world over. This diverse team – representing a wide variety of disciplines and talents from technical directors, writers and lighting designers to choreographers, cosmetologists, and music producers – brings magical worlds to life through technical expertise, performance excellence, incomparable ingenuity, unparalleled spectacle… and a dash of pixie dust.

The Cosmetology Manager will support the Disney Cruise Line. This position leads and mentors hourly Cosmetologists and is the point of contact for contractors who care for the various wigs, facial hair, and prosthetics used in the various Entertainment offerings aboard the ships. Candidates will have a passion for Guest and Cast service putting inclusion to the forefront of everything they do.

The Cosmetology Manager will report to the Area Manager-Cosmetology

This a temporary role with no guarantee of permanent placement.

What You Will Do

Lead, guide, and develop a successful team of hourly Cosmetologists through demonstrated leadership, coaching, and mentoring.

Provide a safe working environment while ensuring compliance with Company, State, and Federal regulations.

Build and maintain positive relationships with Entertainment and Costuming partners.

Represent and support business requests in a fast-paced environment.

Collaborate with Costume Designer, Production Teams, and Buyer to refine project scope as materials are selected and construction methods are refined

Monitor, analyze, and take action on inventory quantity, wig show quality, and expenses affecting annual financial targets.

Must clearly understand all wigs and makeup used in our various Entertainment offerings.

Must have a clear understanding of how to set and adjust priorities to satisfy partner needs and maintain show quality.

Must understand the company guidelines and how it aligns with hair and makeup.

Required Qualifications & Skills

1+ years experience in a Cosmetology or Entertainment related field

1+ years leading union employees

Experience with wig styling and production

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Experience supervising team workflows and assisting in prioritizing collision points

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Ability to travel both domestically and internationally up to 40% of the time 

Preferred Qualifications

Current Cosmetology license

Experience with synthetic wig styling and production

Experience with Walt Disney World Collective Bargaining agreements

Experience with maritime labor laws

Experience with prosthetics, airbrushing, and makeup application

Multi-lingual

High school degree or equivalent is required

Advanced degree in Costume/Hair and Make-up Design or Technology, Cosmetology, or related field is preferred

Additional Information

Benefits and Perks: Disney offers a rewards package to help you live your best life. This includes health and savings benefits, educational opportunities and special extras that only Disney can provide. Learn more about our benefits and perks at https://jobs.disneycareers.com/benefits .

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About Walt Disney Imagineering:

Founded in 1952 as WED Enterprises to design and build the world’s first theme park — Disneyland — Walt Disney Imagineering (WDI) is where imagination and creativity combine with cutting-edge technology to create unforgettable experiences. WDI is the creative force that imagines, designs and builds all Disney theme parks, resorts, attractions and cruise ships worldwide. Imagineering’s unique strength comes from its diverse global team of creative and technical professionals, who build on Disney’s legacy of storytelling to pioneer new forms of entertainment. The Imagineers who practice this unique blend of art and science work in more than 100 disciplines to shepherd an idea all the way from “blue sky” concept phase to opening day.

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The Walt Disney Company, together with its subsidiaries and affiliates, is a leading diversified international family entertainment and media enterprise with the following business segments: Disney Entertainment, ESPN, Disney Parks, and Experiences and Products. From humble beginnings as a cartoon studio in the 1920s to its preeminent name in the entertainment industry today, Disney proudly continues its legacy of creating world-class stories and experiences for every member of the family. Disney’s stories, characters and experiences reach consumers and guests from every corner of the globe. With operations in more than 40 countries, our employees and cast members work together to create entertainment experiences that are both universally and locally cherished.

This position is with Disney Entertainment Productions , which is part of a business we call Walt Disney Imagineering .

Disney Entertainment Productions is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, sexual orientation, gender identity, disability, protected veteran status or any other basis prohibited by federal, state or local law. Disney fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best stories and be relevant in a rapidly changing world.

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Operation Support

Advertised on behalf of.

Home based, NEPAL

Type of Contract :

Individual Contract

Starting Date :

01-May-2024

Application Deadline :

22-Apr-24 (Midnight New York, USA)

Post Level :

National Consultant

Duration of Initial Contract :

Time left :.

12d 23h 55m

Languages Required :

English  

Expected Duration of Assignment :

UNDP is committed to achieving workforce diversity in terms of gender, nationality and culture. Individuals from minority groups, indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the strictest confidence. UNDP does not tolerate sexual exploitation and abuse, any kind of harassment, including sexual harassment, and discrimination. All selected candidates will, therefore, undergo rigorous reference and background checks.

Background/Context

UN Women, grounded in the vision of equality enshrined in the Charter of the United Nations, works for the elimination of discrimination against women and girls; the empowerment of women; and the achievement of equality between women and men as partners and beneficiaries of development, human rights, humanitarian action and peace and security. Placing women’s rights at the center of all its efforts, UN Women leads and coordinates United Nations system efforts to ensure that commitments to gender equality and gender mainstreaming translate into action throughout the world. It provides strong and coherent leadership in support of Member States’ priorities and efforts, building effective partnerships with civil society and other relevant actors. In line with the United Nations Sustainable Development Cooperation Framework (2023-2027), and the UN Women Nepal’s current Strategic Note (SN) 2023-2027.

Reporting to the Operations Manager/Finance Associate, the national consultant supports a special focus on financial management with the ongoing partners’ advance and thorough review of the documents/vouchers for liquidation and advance to the respective partners. The consultant supports finance matters and general administration. The consultant ensures the quality and accuracy of documents/vouchers as per financial compliance. The National consultant works in close collaboration with the operations and programme staff/team to ensure consistent service delivery and to resolve complex financial-related issues and information delivery.

Duties and Responsibilities

Description of Responsibilities /Scope of Work

• Review and verify financial transactions, activities, and documentation/vouchers
• Support in preparation of Reports including tagging, facilitation for physical verification, equipment loaned to NCO personnel, disposal and auction, hand-over of the asset to partners.
• Support to partner selection process and capacity risk assessment of partners
• Provide advice and recommend solutions to a wide range of financial issues; 
• Support to prepare VAT reimbursements with adequate documentation for VAT claim submission to Inland Revenue Department, Government of Nepal
• Design, Facilitate and support organize training events and activities for Responsible Partners 
• Support to review of payment documents at UN Women
• Support and work with the program team to facilitate partners' activities and review of documents while required.
• Capacity building for partners and personnel on financial compliance
• Support OM and Finance associate and building an action plan for Audit for partners and also design the capacity building plan for the partners as per audit findings and recommendation
• Perform duties in full compliance with UN Women financial regulations and rules, policies and standard operating procedures, including internal controls.
• Perform duties as assign by supervisor

Consultant’s Workplace and Official Travel

This is an retainer consultancy based at a UN Women's Office with the option of remote working. There will be no travel-related expenses i.e. DSA, air ticket cost, etc. Only local transportation costs will be reimbursed for official purposes based on the certification by the operations manager.

Competencies

Required skills and experience.

Education and Certification:

• Completion of secondary education is required;
• Bachelor’s degree in Business, Finance or Public Administration or related fields is an asset.
• Accountancy /Finance/Audit Certification from an internationally recognized provider such CA, ACCA is an asset;

Experience:

• At least 5 years of progressively responsible experience in a related field;
• Experience in the usage of computers and office software packages (MS Word, Excel, etc.) is required;
• Experience working with the UN is an asset;
• Fluency in English is required.
• Knowledge of any other UN official is an asset.

How to Apply:

• Personal CV or P11 (P11 can be downloaded from: https://www.unwomen.org/sites/default/files/Headquarters/Attachments/Sections/About%20Us/Employment/UN-Women-P11-Personal-History-Form.doc )
• A cover letter (maximum length: 1 page)