in an assignment method problem

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:

• For each row of the matrix, find the smallest element and subtract it from every element in its row.
• Do the same (as step 1) for all columns.
• Cover all zeros in the matrix using minimum number of horizontal and vertical lines.
• Test for Optimality: If the minimum number of covering lines is n, an optimal assignment is possible and we are finished. Else if lines are lesser than n, we haven’t found the optimal assignment, and must proceed to step 5.
• Determine the smallest entry not covered by any line. Subtract this entry from each uncovered row, and then add it to each covered column. Return to step 3.

Try it before moving to see the solution

Explanation for above simple example:

  An example that doesn’t lead to optimal value in first attempt: In the above example, the first check for optimality did give us solution. What if we the number covering lines is less than n.

Time complexity : O(n^3), where n is the number of workers and jobs. This is because the algorithm implements the Hungarian algorithm, which is known to have a time complexity of O(n^3).

Space complexity :   O(n^2), where n is the number of workers and jobs. This is because the algorithm uses a 2D cost matrix of size n x n to store the costs of assigning each worker to a job, and additional arrays of size n to store the labels, matches, and auxiliary information needed for the algorithm.

In the next post, we will be discussing implementation of the above algorithm. The implementation requires more steps as we need to find minimum number of lines to cover all 0’s using a program. References: http://www.math.harvard.edu/archive/20_spring_05/handouts/assignment_overheads.pdf https://www.youtube.com/watch?v=dQDZNHwuuOY

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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 2024-08-06 UTC.

Assignment Problem

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The problem of optimally assigning m individuals to m jobs, so that each individual is assigned to one job, and each job is filled by one individual. The problem can be formulated as a linear-programming problem with the objective function measuring the (linear) utility of the assignment as follows:

The problem is a special form of the transportation problem and, as such,...

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Burkard, R., Dell’Amico, M., & Marterllo, S. (2009). Assignment problems . Philadelphia: SIAM.

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Kuhn, H. W. (1995). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2 , 83–97.

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Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

Saul I. Gass

Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, MD, USA

Michael C. Fu

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(2013). Assignment Problem. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_200965

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Quantitative Techniques: Theory and Problems by P. C. Tulsian, Vishal Pandey

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WHAT IS ASSIGNMENT PROBLEM

Assignment Problem is a special type of linear programming problem where the objective is to minimise the cost or time of completing a number of jobs by a number of persons.

The assignment problem in the general form can be stated as follows:

“Given n facilities, n jobs and the effectiveness of each facility for each job, the problem is to assign each facility to one and only one job in such a way that the measure of effectiveness is optimised (Maximised or Minimised).”

Several problems of management has a structure identical with the assignment problem.

Example I A manager has four persons (i.e. facilities) available for four separate jobs (i.e. jobs) and the cost of assigning (i.e. effectiveness) each job to each ...

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Assignment problem in linear programming : introduction and assignment model.

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Assignment problem is a special type of linear programming problem which deals with the allocation of the various resources to the various activities on one to one basis. It does it in such a way that the cost or time involved in the process is minimum and profit or sale is maximum. Though there problems can be solved by simplex method or by transportation method but assignment model gives a simpler approach for these problems.

In a factory, a supervisor may have six workers available and six jobs to fire. He will have to take decision regarding which job should be given to which worker. Problem forms one to one basis. This is an assignment problem.

1. Assignment Model :

Suppose there are n facilitates and n jobs it is clear that in this case, there will be n assignments. Each facility or say worker can perform each job, one at a time. But there should be certain procedure by which assignment should be made so that the profit is maximized or the cost or time is minimized.

In the table, Co ij is defined as the cost when j th job is assigned to i th worker. It maybe noted here that this is a special case of transportation problem when the number of rows is equal to number of columns.

Mathematical Formulation:

Any basic feasible solution of an Assignment problem consists (2n – 1) variables of which the (n – 1) variables are zero, n is number of jobs or number of facilities. Due to this high degeneracy, if we solve the problem by usual transportation method, it will be a complex and time consuming work. Thus a separate technique is derived for it. Before going to the absolute method it is very important to formulate the problem.

Suppose x jj is a variable which is defined as

1 if the i th job is assigned to j th machine or facility

0 if the i th job is not assigned to j th machine or facility.

Now as the problem forms one to one basis or one job is to be assigned to one facility or machine.

The total assignment cost will be given by

The above definition can be developed into mathematical model as follows:

Determine x ij > 0 (i, j = 1,2, 3…n) in order to

Subjected to constraints

and x ij is either zero or one.

Method to solve Problem (Hungarian Technique):

Consider the objective function of minimization type. Following steps are involved in solving this Assignment problem,

1. Locate the smallest cost element in each row of the given cost table starting with the first row. Now, this smallest element is subtracted form each element of that row. So, we will be getting at least one zero in each row of this new table.

2. Having constructed the table (as by step-1) take the columns of the table. Starting from first column locate the smallest cost element in each column. Now subtract this smallest element from each element of that column. Having performed the step 1 and step 2, we will be getting at least one zero in each column in the reduced cost table.

3. Now, the assignments are made for the reduced table in following manner.

(i) Rows are examined successively, until the row with exactly single (one) zero is found. Assignment is made to this single zero by putting square □ around it and in the corresponding column, all other zeros are crossed out (x) because these will not be used to make any other assignment in this column. Step is conducted for each row.

(ii) Step 3 (i) in now performed on the columns as follow:- columns are examined successively till a column with exactly one zero is found. Now , assignment is made to this single zero by putting the square around it and at the same time, all other zeros in the corresponding rows are crossed out (x) step is conducted for each column.

(iii) Step 3, (i) and 3 (ii) are repeated till all the zeros are either marked or crossed out. Now, if the number of marked zeros or the assignments made are equal to number of rows or columns, optimum solution has been achieved. There will be exactly single assignment in each or columns without any assignment. In this case, we will go to step 4.

4. At this stage, draw the minimum number of lines (horizontal and vertical) necessary to cover all zeros in the matrix obtained in step 3, Following procedure is adopted:

(iii) Now tick mark all the rows that are not already marked and that have assignment in the marked columns.

(iv) All the steps i.e. (4(i), 4(ii), 4(iii) are repeated until no more rows or columns can be marked.

(v) Now draw straight lines which pass through all the un marked rows and marked columns. It can also be noticed that in an n x n matrix, always less than ‘n’ lines will cover all the zeros if there is no solution among them.

5. In step 4, if the number of lines drawn are equal to n or the number of rows, then it is the optimum solution if not, then go to step 6.

6. Select the smallest element among all the uncovered elements. Now, this element is subtracted from all the uncovered elements and added to the element which lies at the intersection of two lines. This is the matrix for fresh assignments.

7. Repeat the procedure from step (3) until the number of assignments becomes equal to the number of rows or number of columns.

Related Articles:

• Two Phase Methods of Problem Solving in Linear Programming: First and Second Phase
• Linear Programming: Applications, Definitions and Problems

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What Is the Credit Assignment Problem?

Last updated: March 18, 2024

• Machine Learning
• Reinforcement Learning

1. Overview

In this tutorial, we’ll discuss a classic problem in reinforcement learning: the credit assignment problem. We’ll present an example that demonstrates the problem.

Finally, we’ll highlight some solutions to solve the credit assignment problem.

2. Basics of Reinforcement Learning

Reinforcement learning (RL) is a subfield of machine learning that focuses on how an agent can learn to make independent decisions in an environment in order to maximize the reward. It’s inspired by the way animals learn via the trial and error method. Furthermore, RL aims to create intelligent agents that can learn to achieve a goal by maximizing the cumulative reward.

In RL, an agent applies some actions to an environment. Based on the action applied, the environment rewards the agent. After getting the reward, the agents move to a different state and repeat this process. Additionally, the reward can be positive as well as negative based on the action taken by an agent:

The goal of the agent in reinforcement learning is to build an optimal policy that maximizes the overall reward over time. This is typically done using an iterative process . The agent interacts with the environment to learn from experience and updates its policy to improve its decision-making capability.

3. Credit Assignment Problem

The credit assignment problem (CAP) is a fundamental challenge in reinforcement learning. It arises when an agent receives a reward for a particular action, but the agent must determine which of its previous actions led to the reward.

In reinforcement learning, an agent applies a set of actions in an environment to maximize the overall reward. The agent updates its policy based on feedback received from the environment. It typically includes a scalar reward indicating the quality of the agent’s actions.

The credit assignment problem refers to the problem of measuring the influence and impact of an action taken by an agent on future rewards. The core aim is to guide the agents to take corrective actions which can maximize the reward.

However, in many cases, the reward signal from the environment doesn’t provide direct information about which specific actions the agent should continue or avoid. This can make it difficult for the agent to build an effective policy.

Additionally, there’re situations where the agent takes a sequence of actions, and the reward signal is only received at the end of the sequence. In these cases, the agent must determine which of its previous actions positively contributed to the final reward.

It can be difficult because the final reward may be the result of a long sequence of actions. Hence, the impact of any particular action on the overall reward is difficult to discern.

Let’s take a practical example to demonstrate the credit assignment problem.

Suppose an agent is playing a game where it must navigate a maze to reach the goal state. We place the agent in the top left corner of the maze. Additionally, we set the goal state in the bottom right corner. The agent can move up, down, left, right, or diagonally. However, it can’t move through the states containing stone:

As the agent explores the maze, it receives a reward of +10 for reaching the goal state. Additionally, if it hits a stone, we penalize the action by providing a -10 reward. The goal of the agent is to learn from the rewards and build an optimal policy that maximizes the gross reward over time.

The credit assignment problem arises when the agent reaches the goal after several steps. The agent receives a reward of +10 as soon as it reaches the goal state. However, it’s not clear which actions are responsible for the reward. For example, suppose the agent took a long and winding path to reach the goal. Therefore, we need to determine which actions should receive credit for the reward.

Additionally, it’s challenging to decide whether to credit the last action that took it to the goal or credit all the actions that led up to the goal. Let’s look at some paths which lead the agent to the goal state:

As we can see here, the agent can reach the goal state with three different paths. Hence, it’s challenging to measure the influence of each action. We can see the best path to reach the goal state is path 1.

Hence, the positive impact of the agent moving from state 1 to state 5 by applying the diagonal action is higher than any other action from state 1. This is what we want to measure so that we can make optimal policies like path 1 in this example.

5. Solutions

The credit assignment problem is a vital challenge in reinforcement learning. Let’s talk about some popular approaches for solving the credit assignment problem. Here we’ll present three popular approaches: temporal difference (TD) learning , Monte Carlo methods , and eligibility traces method .

TD learning is a popular RL algorithm that uses a bootstrapping approach to assign credit to past actions. It updates the value function of the policy based on the difference between the predicted reward and the actual reward received at each time step. By bootstrapping the value function from the predicted rewards of future states, TD learning can assign credit to past actions even when the reward is delayed.

Monte Carlo methods are a class of RL algorithms that use full episodes of experience to assign credit to past actions. These methods estimate the expected value of a state by averaging the rewards obtained in the episodes that pass through that state. By averaging the rewards obtained over several episodes, Monte Carlo methods can assign credit to actions that led up to the reward, even if the reward is delayed.

Eligibility traces are a method for assigning credit to past actions based on their recent history. Eligibility traces keep track of the recent history of state-action pairs and use a decaying weight to assign credit to each pair based on how recently it occurred. By decaying the weight of older state-action pairs, eligibility traces can assign credit to actions that led up to the reward, even if they occurred several steps earlier.

6. Conclusion

In this tutorial, we discussed the credit assignment problem in reinforcement learning with an example. Finally, we presented three popular solutions that can solve the credit assignment problem.

Assignment Problem: Linear Programming

The assignment problem is a special type of transportation problem , where the objective is to minimize the cost or time of completing a number of jobs by a number of persons.

In other words, when the problem involves the allocation of n different facilities to n different tasks, it is often termed as an assignment problem.

The model's primary usefulness is for planning. The assignment problem also encompasses an important sub-class of so-called shortest- (or longest-) route models. The assignment model is useful in solving problems such as, assignment of machines to jobs, assignment of salesmen to sales territories, travelling salesman problem, etc.

It may be noted that with n facilities and n jobs, there are n! possible assignments. One way of finding an optimal assignment is to write all the n! possible arrangements, evaluate their total cost, and select the assignment with minimum cost. But, due to heavy computational burden this method is not suitable. This chapter concentrates on an efficient method for solving assignment problems that was developed by a Hungarian mathematician D.Konig.

"A mathematician is a device for turning coffee into theorems." -Paul Erdos

Formulation of an assignment problem

Suppose a company has n persons of different capacities available for performing each different job in the concern, and there are the same number of jobs of different types. One person can be given one and only one job. The objective of this assignment problem is to assign n persons to n jobs, so as to minimize the total assignment cost. The cost matrix for this problem is given below:

The structure of an assignment problem is identical to that of a transportation problem.

To formulate the assignment problem in mathematical programming terms , we define the activity variables as

for i = 1, 2, ..., n and j = 1, 2, ..., n

In the above table, c ij is the cost of performing jth job by ith worker.

Generalized Form of an Assignment Problem

The optimization model is

Minimize c 11 x 11 + c 12 x 12 + ------- + c nn x nn

subject to x i1 + x i2 +..........+ x in = 1          i = 1, 2,......., n x 1j + x 2j +..........+ x nj = 1          j = 1, 2,......., n

x ij = 0 or 1

In Σ Sigma notation

x ij = 0 or 1 for all i and j

An assignment problem can be solved by transportation methods, but due to high degree of degeneracy the usual computational techniques of a transportation problem become very inefficient. Therefore, a special method is available for solving such type of problems in a more efficient way.

Assumptions in Assignment Problem

• Number of jobs is equal to the number of machines or persons.
• Each man or machine is assigned only one job.
• Each man or machine is independently capable of handling any job to be done.
• Assigning criteria is clearly specified (minimizing cost or maximizing profit).

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Assignment Method: Examples of How Resources Are Allocated

What Is the Assignment Method?

The assignment method is a way of allocating organizational resources in which each resource is assigned to a particular task. The resource could be monetary, personnel , or technological.

Understanding the Assignment Method

The assignment method is used to determine what resources are assigned to which department, machine, or center of operation in the production process. The goal is to assign resources in such a way to enhance production efficiency, control costs, and maximize profits.

The assignment method has various applications in maximizing resources, including:

• Allocating the proper number of employees to a machine or task
• Allocating a machine or a manufacturing plant and the number of jobs that a given machine or factory can produce
• Assigning a number of salespersons to a given territory or territories
• Assigning new computers, laptops, and other expensive high-tech devices to the areas that need them the most while lower priority departments would get the older models

Companies can make budgeting decisions using the assignment method since it can help determine the amount of capital or money needed for each area of the company. Allocating money or resources can be done by analyzing the past performance of an employee, project, or department to determine the most efficient approach.

Regardless of the resource being allocated or the task to be accomplished, the goal is to assign resources to maximize the profit produced by the task or project.

Example of Assignment Method

A bank is allocating its sales force to grow its mortgage lending business. The bank has over 50 branches in New York but only ten in Chicago. Each branch has a staff that is used to bring in new clients.

The bank's management team decides to perform an analysis using the assignment method to determine where their newly-hired salespeople should be allocated. Given the past performance results in the Chicago area, the bank has produced fewer new clients than in New York. The fewer new clients are the result of having a small market presence in Chicago.

As a result, the management decides to allocate the new hires to the New York region, where it has a greater market share to maximize new client growth and, ultimately, revenue.

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Unbalanced Assignment Problem: Definition, Formulation, and Solution Methods

Table of Contents

Are you familiar with the assignment problem in Operations Research (OR)? This problem deals with assigning tasks to workers in a way that minimizes the total cost or time needed to complete the tasks. But what if the number of tasks and workers is not equal? In this case, we face the Unbalanced Assignment Problem (UAP). This blog will help you understand what the UAP is, how to formulate it, and how to solve it.

What is the Unbalanced Assignment Problem?

The Unbalanced Assignment Problem is an extension of the Assignment Problem in OR, where the number of tasks and workers is not equal. In the UAP, some tasks may remain unassigned, while some workers may not be assigned any task. The objective is still to minimize the total cost or time required to complete the assigned tasks, but the UAP has additional constraints that make it more complex than the traditional assignment problem.

Formulation of the Unbalanced Assignment Problem

To formulate the UAP, we start with a matrix that represents the cost or time required to assign each task to each worker. If the matrix is square, we can use the Hungarian algorithm to solve the problem. But when the matrix is not square, we need to add dummy tasks or workers to balance the matrix. These dummy tasks or workers have zero costs and are used to make the matrix square.

Once we have a square matrix, we can apply the Hungarian algorithm to find the optimal assignment. However, we need to be careful in interpreting the results, as the assignment may include dummy tasks or workers that are not actually assigned to anything.

Solutions for the Unbalanced Assignment Problem

Besides the Hungarian algorithm, there are other methods to solve the UAP, such as the transportation algorithm and the auction algorithm. The transportation algorithm is based on transforming the UAP into a transportation problem, which can be solved with the transportation simplex method. The auction algorithm is an iterative method that simulates a bidding process between the tasks and workers to find the optimal assignment.

In summary, the Unbalanced Assignment Problem is a variant of the traditional Assignment Problem in OR that deals with assigning tasks to workers when the number of tasks and workers is not equal. To solve the UAP, we need to balance the matrix by adding dummy tasks or workers and then apply algorithms such as the Hungarian algorithm, the transportation algorithm, or the auction algorithm. Understanding the UAP can help businesses and organizations optimize their resource allocation and improve their operational efficiency.

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Operations Research

1 Operations Research-An Overview

• History of O.R.
• Approach, Techniques and Tools
• Phases and Processes of O.R. Study
• Typical Applications of O.R
• Limitations of Operations Research
• Models in Operations Research
• O.R. in real world

2 Linear Programming: Formulation and Graphical Method

• General formulation of Linear Programming Problem
• Optimisation Models
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• Multiple, Unbounded Solution and Infeasible Problems
• Solving Linear Programming Graphically Using Computer
• Application of Linear Programming in Business and Industry

3 Linear Programming-Simplex Method

• Principle of Simplex Method
• Computational aspect of Simplex Method
• Simplex Method with several Decision Variables
• Two Phase and M-method
• Multiple Solution, Unbounded Solution and Infeasible Problem
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4 Transportation Problem

• Basic Feasible Solution of a Transportation Problem
• Modified Distribution Method
• Stepping Stone Method
• Unbalanced Transportation Problem
• Degenerate Transportation Problem
• Transhipment Problem
• Maximisation in a Transportation Problem

5 Assignment Problem

• Solution of the Assignment Problem
• Unbalanced Assignment Problem
• Problem with some Infeasible Assignments
• Maximisation in an Assignment Problem
• Crew Assignment Problem

6 Application of Excel Solver to Solve LPP

• Building Excel model for solving LP: An Illustrative Example

7 Goal Programming

• Concepts of goal programming
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• Using Excel Solver to Solve Goal Programming Models
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8 Integer Programming

• Some Integer Programming Formulation Techniques
• Binary Representation of General Integer Variables
• Unimodularity
• Cutting Plane Method
• Branch and Bound Method
• Solver Solution

9 Dynamic Programming

• Dynamic Programming Methodology: An Example
• Definitions and Notations
• Dynamic Programming Applications

10 Non-Linear Programming

• Solution of a Non-linear Programming Problem
• Convex and Concave Functions
• Kuhn-Tucker Conditions for Constrained Optimisation
• Quadratic Programming
• Separable Programming
• NLP Models with Solver

11 Introduction to game theory and its Applications

• Important terms in Game Theory
• Saddle points
• Mixed strategies: Games without saddle points
• 2 x n games
• Exploiting an opponent’s mistakes

12 Monte Carlo Simulation

• Reasons for using simulation
• Monte Carlo simulation
• Limitations of simulation
• Steps in the simulation process
• Some practical applications of simulation
• Two typical examples of hand-computed simulation
• Computer simulation

13 Queueing Models

• Characteristics of a queueing model
• Notations and Symbols
• Statistical methods in queueing
• The M/M/I System
• The M/M/C System
• The M/Ek/I System
• Decision problems in queueing

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A new study pokes holes in the agile method—but is it agile’s fault.

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Projects that use the Agile development process fail 65% of the time versus lean engineering at 21% and a new method called Impact Engineering at 10%, according to a new study conducted for the book Impact Engineering . I was intrigued by headlines about the study—and by the clever marketing technique of using a survey to help sell a book. The marketing worked on me, given that I just bought and read the book.

The Method Itself, Or The Implementation?

The Agile development process was introduced about 20 years ago as a reaction to more strict development methodologies. At that time, multiple rigid and highly serial “waterfall” development methods were most popular. However, waterfall project management was not well aligned to the needs of software development and lacked the means to adjust effort based upon feedback throughout the development process.

Agile was revolutionary because it replaced waterfall’s long development cycles that were planned out potentially months in advance. Agile is based upon short development cycles—often two or three weeks—called sprints. Sprints are highly iterative and have multiple feedback loops built into the process, which means that change and risks are easy to intercept and fix. The increased collaboration this fosters led to a more transparent management style that meshed well with software startups, which were the early adopters of Agile methods.

Over the years, Agile became more robust, and multiple variants (such as Scrum and Kanban) were developed to meet the needs of diverse development teams. But each of these variants holds true to the original Agile principles. Agile processes are now the de facto approach for software development in many organizations of all sizes.

When I dug into the results of the study and the book, I wondered whether the issues were with Agile itself or the improper implementation of it. As someone who’s seen Agile succeed and fail, I have learned that failure is often the result of other factors, rather than the method itself. So I remain somewhat skeptical about the study’s conclusion that a different approach is required. Also, it should be noted that the results came from a survey of 600 developers from the U.S and U.K., so it’s not a global representation of Agile development.

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Below are the key findings of the survey, followed by my own thoughts on the study's validity based on three of its key findings.

Results from the Impact Engineering survey about Agile's effect on key success factors for software ... [+] development projects

Issue No. 1: Being Able To Discuss And Address Problems Quickly

I completely agree that practices that ensure psychological safety will have a significant impact on success. But the way this is presented by the Impact Engineering proponents suggests that Agile does not support psychological safety—which is not true. The whole point of Agile is for issues to be raised as soon as possible in standup meetings and addressed by the right leaders in the organization via a well-communicated escalation process. So is this an Agile issue or an organizational cultural issue?

Beyond that, I have also seen waterfall implementations fail because of a lack of psychological safety. My take is that when leaders and developers embrace Agile, that needs to come with an embrace of an entirely new level of transparency and trust. When a product team is working under the pressure of a date that was set too early in the process, or the team is being punished for unexpected setbacks, leadership can lose the trust required to make any project a success, Agile or otherwise. To put it another way, when devs don’t feel safe to speak their minds, they will not share critical information to ensure that a proactive solution can be found sooner than later.

Issue No. 2: Project Requirements Are Accurately Based On The Real-World Problem

I think a poor requirements definition will impact a software development project no matter which development process is adopted. But again, Agile has mechanisms to handle requirements collection and refinements really well. First of all, a major component of well-implemented Agile is the creation of “epics” and “stories” (which are proxies for requirements). While previous methods stressed the role of a product manager owning a unitary requirements document, Agile is a more collaborative affair, where team leads and architects are involved early as the requirements are documented and prioritized.

Beyond the team, another key aspect of Agile is letting customers and business partners into the process by hosting product demos early and often to gather feedback. This is absolutely essential to make sure the requirements are correct. It helps the development team understand not only the requirements but also the various nuances of how the problem should be solved. If the development team is taking the word of only the product managers and not interfacing directly with the outside stakeholders, the chances of failure are much higher. Again, transparency and communication are the keys to success.

That said, I think that I can understand why this perception exists with Agile, because I have seen it myself. In fact, bad requirements gathering was a critical reason that I, too, was anti-Agile for a while. My experience is that when teams aren’t well trained in Agile, they tend to focus too much on the rapid development aspects. Another part of the challenge is that Agile is often sold as being “faster,” which it can be in the aggregate due to reduction of rework. But my sense is that Agile is really about being “more nimble.” This misunderstanding creates the requirements challenge. That’s because the best way to go faster is to operate a vacuum. However, the best way to be more nimble is to listen and iterate.

Issue No. 3: Not Having To Work On More Than One Project At A Time

I was surprised to see this particular result. When I have worked with Agile experts and coaches, they often note that pooled or shared resources are harder to manage in Agile, and that the developers themselves should shoulder more testing and DevOps responsibilities. The idea is for the Agile team to be small and athletic. This is why Agile can fail in larger organizations that operate in functional silos. So, in this case I would have expected Agile to perform worse—but it didn’t.

However, the book notes that there seems to be a point of diminishing returns. Dealing with two projects at a time is good, but having more than four to juggle seems to have a negative impact. This result also suggests that maybe it’s not so much about having multiple projects versus people being assigned multiple full-time workloads, leading to burnout and mistakes.

Again, Agile has mechanisms for capacity management, and a good scrum master will be able to manage team capacity against burn-down statistics to ensure good balancing—which is not always the case with other development methods. So, upon further reflection, this one may hold up.

My Conclusion: I Just Don’t Think It’s Agile’s Fault

I am very agreeable to the argument that Agile is hard to deploy in many environments and may not be the best answer in every case. In this connection, it’s probably worth noting that I have been beaten up by multiple executives when I have suggested that Agile is the wrong way for their group to operate under a specific set of circumstances. In my experience, these are usually the same executives who want to see work done faster, but have no willingness to invest in the change management—and the candid feedback, including feedback on their own decisions—that’s necessary to make Agile a success.

In short, if you want to improve how you make products or apps, you need to consider every aspect of the organization and what you are trying to achieve before you decide which method to use.

So, What Did I Think Of The Book?

Like I said earlier, while the survey is a clever marketing mechanism, Impact Engineering ended up being less of a new approach and more an indictment of Agile gone wrong. That is unfortunate, since another unique thing about the book is that it uses a fictional storyline about a project to communicate its points. This would have been a very engaging way for the author to couch doing Agile correctly for a non-technical audience—versus making an argument against Agile.

Another miss in the book is that after the happy ending to the story, there is a very thin conclusion that has a very basic call to action. It would have been a lot more impactful to have a couple of chapters after the storyline with tips and tools on how to make Agile projects better. Since the book did not have that, I’ll close with my best three tips to make Agile work.

Getting The Most Out Of Agile

First, there are many variants of the Agile method and not all of them will be the best fit for you and your team. It’s even hard to say that one variant is better for this technology or that one. What determines the best fit is actually the nexus of the team’s workstyle and the technology. I know of an instance where a team started with the Scrum methodology and was failing. They very appropriately took a step back and switched to Kanban; with a bit of effort, the team turned things around.

Second, get help—and not just from consultants. Consultants can advise you in the moment, but your team probably needs some permanent help. My suggestion is to hire an experienced scrum master as soon as possible. That person will be able to set up and manage the right processes and act as an arbiter in times of misalignment. Someone with experience will be able to expertly balance the team’s capacity versus the work to be done, and that will help in the long run with team morale and burnout.

Third, there are no shortcuts. You have to crisply define the roles that people are responsible for carrying out. You need to expect that in the beginning things will slow down and maybe go badly before you get more nimble. And, most importantly, remember that Agile is not just for the team; it’s for the organization. Command and control leadership from the C-suite is the biggest threat to an Agile effort. I would highly recommend training at all levels and coaching for any executive who touches the product. For that, I suggest anything in the Pragmatic Engineering ecosystem.

All in all, I found Impact Engineering and the related survey to be thought-provoking, but I was hardly convinced by its attempt to indict Agile as a methodology.

NOTE: Special thanks to Ryan Daws for publishing the article that inspired this piece.

Moor Insights & Strategy provides or has provided paid services to technology companies, like all tech industry research and analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking and video and speaking sponsorships. Moor Insights & Strategy does not have paid business relationships with any company mentioned in this article.

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MIT researchers use large language models to flag problems in complex systems

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Identifying one faulty turbine in a wind farm, which can involve looking at hundreds of signals and millions of data points, is akin to finding a needle in a haystack.

Engineers often streamline this complex problem using deep-learning models that can detect anomalies in measurements taken repeatedly over time by each turbine, known as time-series data.

But with hundreds of wind turbines recording dozens of signals each hour, training a deep-learning model to analyze time-series data is costly and cumbersome. This is compounded by the fact that the model may need to be retrained after deployment, and wind farm operators may lack the necessary machine-learning expertise.

In a new study, MIT researchers found that large language models (LLMs) hold the potential to be more efficient anomaly detectors for time-series data. Importantly, these pretrained models can be deployed right out of the box.

The researchers developed a framework, called SigLLM, which includes a component that converts time-series data into text-based inputs an LLM can process. A user can feed these prepared data to the model and ask it to start identifying anomalies. The LLM can also be used to forecast future time-series data points as part of an anomaly detection pipeline.

While LLMs could not beat state-of-the-art deep learning models at anomaly detection, they did perform as well as some other AI approaches. If researchers can improve the performance of LLMs, this framework could help technicians flag potential problems in equipment like heavy machinery or satellites before they occur, without the need to train an expensive deep-learning model.

“Since this is just the first iteration, we didn’t expect to get there from the first go, but these results show that there’s an opportunity here to leverage LLMs for complex anomaly detection tasks,” says Sarah Alnegheimish, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on SigLLM .

Her co-authors include Linh Nguyen, an EECS graduate student; Laure Berti-Equille, a research director at the French National Research Institute for Sustainable Development; and senior author Kalyan Veeramachaneni, a principal research scientist in the Laboratory for Information and Decision Systems. The research will be presented at the IEEE Conference on Data Science and Advanced Analytics.

An off-the-shelf solution

Large language models are autoregressive, which means they can understand that the newest values in sequential data depend on previous values. For instance, models like GPT-4 can predict the next word in a sentence using the words that precede it.

Since time-series data are sequential, the researchers thought the autoregressive nature of LLMs might make them well-suited for detecting anomalies in this type of data.

However, they wanted to develop a technique that avoids fine-tuning, a process in which engineers retrain a general-purpose LLM on a small amount of task-specific data to make it an expert at one task. Instead, the researchers deploy an LLM off the shelf, with no additional training steps.

But before they could deploy it, they had to convert time-series data into text-based inputs the language model could handle.

They accomplished this through a sequence of transformations that capture the most important parts of the time series while representing data with the fewest number of tokens. Tokens are the basic inputs for an LLM, and more tokens require more computation.

“If you don’t handle these steps very carefully, you might end up chopping off some part of your data that does matter, losing that information,” Alnegheimish says.

Once they had figured out how to transform time-series data, the researchers developed two anomaly detection approaches.

Approaches for anomaly detection

For the first, which they call Prompter, they feed the prepared data into the model and prompt it to locate anomalous values.

“We had to iterate a number of times to figure out the right prompts for one specific time series. It is not easy to understand how these LLMs ingest and process the data,” Alnegheimish adds.

For the second approach, called Detector, they use the LLM as a forecaster to predict the next value from a time series. The researchers compare the predicted value to the actual value. A large discrepancy suggests that the real value is likely an anomaly.

With Detector, the LLM would be part of an anomaly detection pipeline, while Prompter would complete the task on its own. In practice, Detector performed better than Prompter, which generated many false positives.

“I think, with the Prompter approach, we were asking the LLM to jump through too many hoops. We were giving it a harder problem to solve,” says Veeramachaneni.

When they compared both approaches to current techniques, Detector outperformed transformer-based AI models on seven of the 11 datasets they evaluated, even though the LLM required no training or fine-tuning.

In the future, an LLM may also be able to provide plain language explanations with its predictions, so an operator could be better able to understand why an LLM identified a certain data point as anomalous.

However, state-of-the-art deep learning models outperformed LLMs by a wide margin, showing that there is still work to do before an LLM could be used for anomaly detection.

“What will it take to get to the point where it is doing as well as these state-of-the-art models? That is the million-dollar question staring at us right now. An LLM-based anomaly detector needs to be a game-changer for us to justify this sort of effort,” Veeramachaneni says.

Moving forward, the researchers want to see if finetuning can improve performance, though that would require additional time, cost, and expertise for training.

Their LLM approaches also take between 30 minutes and two hours to produce results, so increasing the speed is a key area of future work. The researchers also want to probe LLMs to understand how they perform anomaly detection, in the hopes of finding a way to boost their performance.

“When it comes to complex tasks like anomaly detection in time series, LLMs really are a contender. Maybe other complex tasks can be addressed with LLMs, as well?” says Alnegheimish.

This research was supported by SES S.A., Iberdrola and ScottishPower Renewables, and Hyundai Motor Company.

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TechCrunch reporter Kyle Wiggers writes that MIT researchers have developed a new tool, called SigLLM, that uses large language models to flag problems in complex systems. In the future, SigLLM could be used to “help technicians flag potential problems in equipment like heavy machinery before they occur.” 

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