Assignment Problem: Meaning, Methods and Variations | Operations Research
After reading this article you will learn about:- 1. Meaning of Assignment Problem 2. Definition of Assignment Problem 3. Mathematical Formulation 4. Hungarian Method 5. Variations.
Meaning of Assignment Problem:
An assignment problem is a particular case of transportation problem where the objective is to assign a number of resources to an equal number of activities so as to minimise total cost or maximize total profit of allocation.
The problem of assignment arises because available resources such as men, machines etc. have varying degrees of efficiency for performing different activities, therefore, cost, profit or loss of performing the different activities is different.
Thus, the problem is “How should the assignments be made so as to optimize the given objective”. Some of the problem where the assignment technique may be useful are assignment of workers to machines, salesman to different sales areas.
Definition of Assignment Problem:
ADVERTISEMENTS:
Suppose there are n jobs to be performed and n persons are available for doing these jobs. Assume that each person can do each job at a term, though with varying degree of efficiency, let c ij be the cost if the i-th person is assigned to the j-th job. The problem is to find an assignment (which job should be assigned to which person one on-one basis) So that the total cost of performing all jobs is minimum, problem of this kind are known as assignment problem.
The assignment problem can be stated in the form of n x n cost matrix C real members as given in the following table:
Check it out now on O’Reilly
Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day.
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
Related Topics
Privacy Policy , Terms and Conditions , DMCA Policy and Compliant
Copyright © 2018-2023 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.
Your Article Library
Assignment problem in linear programming : introduction and assignment model.
ADVERTISEMENTS:
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
No comments yet.
Leave a reply click here to cancel reply..
You must be logged in to post a comment.
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
x = | 1 if job j is performed by worker i | |
0 otherwise |
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).
Share this article with your friends
Operations Research Simplified Back Next
Goal programming Linear programming Simplex Method Transportation Problem
- Access through your organization
- Purchase PDF
Article preview
Introduction, section snippets, references (72), cited by (409).
European Journal of Operational Research
Discrete optimization assignment problems: a golden anniversary survey, the classic assignment problem, models with multiple tasks per agent, multi-dimensional assignment problems, bottleneck assignment problems under categorization, computers & operations research, multiple bottleneck assignment problem, a heuristic procedure for the crew rostering problem, vehicle routing considerations in distribution system design, quadratic assignment problems, lexicographic bottleneck problems, operations research letters, development and evaluation of an assignment heuristic for allocating cross-trained workers, a multi-level bottleneck assignment approach to the bus drivers’ rostering problem, a survey of algorithms for the generalized assignment problem, the β-assignment problems, the k -cardinality assignment problem, discrete applied mathematics, minimum deviation and balanced optimization: a unified approach, slotmanager: a microcomputer-based decision support system for university timetabling, decision support systems, on an assignment problem with side constraints, computers & industrial engineering, a variation of the assignment problem, the three-dimensional assignment problem with capacity constraints, minimum deviation problems, tabu search for the multilevel generalized assignment problem, formulating and solving production planning problems, balanced optimization problems, the bottleneck generalized assignment problem, bottleneck generalized assignment problems, engineering costs and production economics, stable solutions vs. multiplicative utility solutions for the assignment problem, using the generalized assignment problem in scheduling the rosat space telescope, computer scheduling of medical school clerkships, computers & education, an integer programming model for the allocation of databases in a distributed computer system, an algorithm for fractional assignment problems, capacity planning by the dynamic multi-resource generalized assignment problem (dmrgap), lexicographic bottleneck combinatorial problems, an optimal solution to a dock door assignment problem, linear and semi-assignment problems: a core oriented approach, solving some lexicographic multi-objective combinatorial problems, a note on the assignment problem with seniority and job priority constraints, an introduction to timetabling, heuristic and exact algorithms for the simultaneous assignment problem, a variant of time minimizing assignment problem, employees recruitment: a prescriptive analytics approach via machine learning and mathematical programming, robust nurse-to-patient assignment in home care services to minimize overtimes under continuity of care, optimization for dynamic ride-sharing: a review, simultaneous task allocation and planning for temporal logic goals in heterogeneous multi-robot systems, a distributed version of the hungarian method for multirobot assignment, a partition-based match making algorithm for dynamic ridesharing.
270+ Operations Research Solved MCQs
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Bachelor of Computer Applications (BCA) , Bachelor of Management Studies (BMS) .
1. | |
A. | objective function |
B. | decision variable |
C. | constraints |
D. | opportunity cost |
Answer» A. objective function |
2. | |
A. | infeasible region |
B. | unbounded region |
C. | infinite region |
D. | feasible region |
Answer» D. feasible region |
3. | |
A. | outgoing row |
B. | key row |
C. | basic row |
D. | interchanging row |
Answer» C. basic row |
4. | |
A. | dummy |
B. | epsilon |
C. | penalty |
D. | regret |
Answer» B. epsilon |
5. | |
A. | ncwr |
B. | lcm |
C. | vam |
D. | hungarian |
Answer» D. hungarian |
6. | |
A. | head path |
B. | sub path |
C. | critical path |
D. | sub critical path |
Answer» C. critical path |
7. | |
A. | 7 |
B. | 10 |
C. | 18 |
D. | 8 |
Answer» B. 10 |
8. | |
A. | interfering float = total float – free float |
B. | total float =free float + independent float |
C. | total float ≥ free float ≥ independent float |
D. | free float = total float – head event slack |
Answer» B. total float =free float + independent float |
9. | |
A. | expected |
B. | pessimitic |
C. | optimistic |
D. | most likely |
Answer» C. optimistic |
10. | |
A. | processing order |
B. | idle time |
C. | processing time |
D. | elapsed time |
Answer» D. elapsed time |
11. | |
A. | physical |
B. | symbolic |
C. | deterministic |
D. | probabilistic |
Answer» C. deterministic |
12. | |
A. | physical |
B. | symbolic |
C. | deterministic |
D. | probabilistic |
Answer» D. probabilistic |
13. | |
A. | cpm and pert |
B. | assignment & transportation |
C. | game theory |
D. | decision theory & inventory models |
Answer» A. cpm and pert |
14. | |
A. | objective function |
B. | decision variables |
C. | constraints |
D. | opportunity cost |
Answer» B. decision variables |
15. | |
A. | objective function |
B. | decision variables |
C. | constraints |
D. | opportunity cost |
Answer» A. objective function |
16. | |
A. | objective function |
B. | variables |
C. | constraints |
D. | profit |
Answer» C. constraints |
17. | |
A. | infeasible |
B. | unbounded |
C. | improper |
D. | unknown |
Answer» A. infeasible |
18. | |
A. | less than or equal to |
B. | greater than or equal to |
C. | mixed |
D. | equal to |
Answer» D. equal to |
19. | |
A. | infeasible |
B. | infinite |
C. | unique |
D. | degenerate |
Answer» B. infinite |
20. | |
A. | key column |
B. | incoming column |
C. | important column |
D. | variable column |
Answer» A. key column |
21. | |
A. | vital element |
B. | important element |
C. | basic element |
D. | key element |
Answer» D. key element |
22. | |
A. | surplus |
B. | artificial |
C. | slack |
D. | additional |
Answer» C. slack |
23. | |
A. | null resource |
B. | scarce resource |
C. | abundant resource |
D. | zero resource |
Answer» B. scarce resource |
24. | |
A. | either zero or positive |
B. | either zero or negative |
C. | only positive |
D. | only negative |
Answer» A. either zero or positive |
25. | |
A. | vogel’s approximat ion method |
B. | nwcr |
C. | lcm |
D. | modi |
Answer» C. lcm |
26. | |
A. | infeasible solution |
B. | feasible solution |
C. | optimum solution |
D. | degenerate solution |
Answer» B. feasible solution |
27. | |
A. | infeasible solution |
B. | feasible solution |
C. | non degenerate solution |
D. | degenerate solution |
Answer» C. non degenerate solution |
28. | |
A. | vam |
B. | nwcr |
C. | modi |
D. | lcm |
Answer» A. vam |
29. | |
A. | balanced |
B. | unbalanced |
C. | infeasible |
D. | unbounded |
Answer» B. unbalanced |
30. | |
A. | vam |
B. | nwcr |
C. | modi |
D. | hungarian |
Answer» D. hungarian |
31. | |
A. | cost |
B. | regret |
C. | profit |
D. | dummy |
Answer» B. regret |
32. | |
A. | critical |
B. | sub-critical |
C. | best |
D. | worst |
Answer» A. critical |
33. | |
A. | tentative |
B. | definite |
C. | latest |
D. | earliest |
Answer» C. latest |
34. | |
A. | machines order |
B. | job order |
C. | processing order |
D. | working order |
Answer» C. processing order |
35. | |
A. | processing |
B. | waiting |
C. | free |
D. | idle |
Answer» D. idle |
36. | |
A. | objective function |
B. | decision variables |
C. | constraints |
D. | opportunity cost |
Answer» C. constraints |
37. | |
A. | less than |
B. | greater than |
C. | not greater than |
D. | not less than |
Answer» A. less than |
38. | |
A. | infeasible |
B. | infinite |
C. | unbounded |
D. | feasible |
Answer» D. feasible |
39. | |
A. | multiple constraints |
B. | infinite constraints |
C. | infeasible constraints |
D. | mixed constraints |
Answer» D. mixed constraints |
40. | |
A. | outgoing row |
B. | key row |
C. | interchanging row |
D. | basic row |
Answer» B. key row |
41. | |
A. | null resource |
B. | scarce resource |
C. | abundant resource |
D. | zero resource |
Answer» C. abundant resource |
42. | |
A. | unit price |
B. | extra price |
C. | retail price |
D. | shadow price |
Answer» D. shadow price |
43. | |
A. | either zero or positive |
B. | either zero or negative |
C. | only positive |
D. | only negative |
Answer» B. either zero or negative |
44. | |
A. | vogel’s approximat ion method |
B. | nwcr |
C. | lcm |
D. | modi |
Answer» A. vogel’s approximat ion method |
45. | |
A. | dummy |
B. | penalty |
C. | regret |
D. | epsilon |
Answer» D. epsilon |
46. | |
A. | there is no degeneracy |
B. | degeneracy exists |
C. | solution is optimum |
D. | problem is balanced |
Answer» A. there is no degeneracy |
47. | |
A. | dummy |
B. | non-critical |
C. | important |
D. | critical |
Answer» D. critical |
48. | |
A. | one |
B. | zero |
C. | highest |
D. | equal to duration |
Answer» B. zero |
49. | |
A. | optimistic |
B. | pessimistic |
C. | expected |
D. | most likely |
Answer» A. optimistic |
50. | |
A. | processing time |
B. | waiting time |
C. | elapsed time |
D. | idle time |
Answer» C. elapsed time |
Done Studing? Take A Test.
Great job completing your study session! Now it's time to put your knowledge to the test. Challenge yourself, see how much you've learned, and identify areas for improvement. Don’t worry, this is all part of the journey to mastery. Ready for the next step? Take a quiz to solidify what you've just studied.
COMMENTS
Learn the definition, difference, algorithm and examples of assignment problem, a special case of transportation problem in operations research. The notes cover the Hungarian method, the cost matrix, the optimal solution and the steps to solve the problem.
Learn about the assignment problem, a combinatorial optimization problem of finding the optimal assignment of agents to tasks with minimal cost. Explore the definitions, examples, algorithms, and applications of this problem in different contexts.
The Assignment Problem: An Example A company has 4 machines available for assignment to 4 tasks. Any machine can be assigned to any task, and each task requires processing by one machine. The time required to set up each machine for the processing of each task is given in the table below. TIME (Hours) Task 1 Task 2 Task 3 Task 4 Machine 1 13 4 7 6
An assignment problem is a case of transportation problem where the objective is to assign a number of resources to an equal number of activities so as to optimize the given objective. Learn the mathematical formulation, Hungarian method and variations of assignment problem with examples.
Learn how to formulate and solve assignment problems using the Hungarian method, a simpler and faster technique than the transportation method. See examples, special cases and applications of assignment problems, such as the travelling salesman problem.
Learn how to formulate the assignment problem as a 0,1-integer linear program and solve it using the Hungarian algorithm. The assignment problem is a combinatorial optimization problem that involves finding a minimum cost matching in a bipartite graph.
Learn the basics of assignment problem, a special case of transportation problem in operations research. See the matrix form, mathematical formulation, difference with transportation problem and Hungarian method with an example.
Learn how to solve assignment problems using the Hungarian Method with step-by-step illustrations and examples. The web page explains the algorithm, the notation and the solution procedure with a funny toys company case.
Learn how to solve assignment problems using Hungarian method with examples and diagrams. This chapter covers single step and multiple step assignments, balanced and unbalanced problems, and payoff matrices.
ASSIGNMENT PROBLEM Introduction: 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:
Learn how to solve assignment problems by maximizing the overall performance using the Hungarian method. See an example of converting a maximization problem into a minimization problem by subtracting the highest element from each value in the matrix.
Learn about the assignment problem, a special case of the transportation problem, and its solution method based on the Hungarian algorithm. The chapter also explains the general model of the assignment problem and its applications.
Learn how to solve assignment problems using the Hungarian method, a technique to find the optimal assignment schedule and cost. See examples of balanced and unbalanced problems, and how to balance an unbalanced problem with a dummy column.
Learn what is an assignment problem, a special type of linear programming problem that allocates resources to activities on a one-to-one basis. Find out how to solve it using the Hungarian technique, a step-by-step procedure with examples and diagrams.
Learn how to formulate and solve an assignment problem using linear programming. An 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.
A comprehensive survey of various variations of the assignment problem that have appeared in the literature over the past 50 years. The paper explains the basic concepts, notation, and examples of each model, such as the classic, bottleneck, generalized, and multi-dimensional assignment problems.
The assignment problem is a standard topic discussed in operations research textbooks [8] and [10]. It is an important subject, put forward immediately after the transportation problem, is the assignment problem. This is particularly important in the theory of decision making. The assignment problem is one of the earliest
The assignment matrix only contains eighter 0 or 1 in the respective row or column. The balanced assignment problems are those types of assignment problems where the number of facilities is equal to the number of jobs. The solution to the balance assignment problem is binary due to the uni-modularity property.
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Bachelor of Computer Applications (BCA) , Bachelor of Management Studies (BMS) . ... When a maximization assignment problem is converted in minimization problem, the resulting matrix is called matrix. ... The type of ...