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In this paper, we propose an algorithm for solving multi-objective assignment problem (MOAP) through Hungarian Algorithm, and this approach emphasizes on optimal solution of each objective function by minimizing the resource. To illustrate the algorithm a numerical example (Sec. 4; Table 1) is presented.

General assignment problem includes “N” tasks that must assign to “N” workers where each worker has the competence to do all tasks. However, due to personal ability or other reasons, each worker may spend different amount of resource to finish different tasks. The objective is to assign each task to a proper worker so that the total resource that spends finishing all tasks can be minimized.

Many studies have been developed to solve the assignment problem [

Most of the developed methods for the assignment problem consider only one-objective situation, such as (1) the minimum cost assignment problem, (2) the minimum finishing time assignment problem. The minimum cost

Jobs/Machine | |||||
---|---|---|---|---|---|

9 | 7 | 4 | 6 | ¬ | |

2 | 1 | 8 | 2 | ¬ | |

1 | 1 | 1 | 5 | ¬ | |

12 | 5 | 5 | 8 | ||

9 | 9 | 1 | 8 | ||

7 | 5 | 5 | 9 | ||

9 | 9 | 9 | 11 | ||

8 | 9 | 5 | 6 | ||

1 | 7 | 5 | 7 | ||

2 | 7 | 11 | 8 | ||

1 | 5 | 4 | 9 | ||

1 | 3 | 5 | 3 |

assignment problem focuses on how to assign tasks to workers so that the total operation cost can be minimized. Such problems have been generally discussed and well developed in many operations researches. Geetha and Nair [

Assignment problem is one of the special cases of transportation problems. The goal of the assignment problem is to minimize the cost or time of completing a number of jobs by a number of persons. An important characteristic of the assignment problem is the number of sources is equal to the number of destinations. It is explained in the following way.

1) Only one job is assigned to person.

2) Each person is assigned with exactly one job.

Management has faced with problems whose structures are identical with assignment problems.

For example, a manager has five persons for five separate jobs and the cost of assigning each job to each person is given. His goal is to assign one and only job to each person in such a way that the total cost of assignment is minimized.

Balanced assignment problem: The number of persons is equal to the number of jobs.

Minimize (Maximize):

Subject to

where

Problem definition: Consider a problem which consists a set of “n” machines

The triangle illustrates the relationship between three primary forces in an assignment. Time is available to deliver the assignment, cost represents the amount of money and quality represents fit for the purpose of assignment which should be a successful achievement.

To determine the assignment of cost (C), time (T) and quality (Q) vs. machine (s) of an assignment problem for a set of “n” machines

Step 1: Consider “m” jobs on “n” machines costs given as a matrix (ACM), which is an balanced assignment problem, where

Step 2: Obtain the sum of cost, time, quality in each job of the ACM.

Step 3: If the total effectiveness of ACM is to be maximized, change the sign of each cost element in the effectiveness matrix and go to Step 4, otherwise go directly to Step 5 if ACM has the total value as minimum.

Step 4: If the minimum element in the

Step 5: If the minimum element in the column j is not zero, then subtract this minimum element from each element in the column j (

12 | 9 | 13 | 13 | |

28 | 19 | 11 | 25 | |

18 | 25 | 19 | 24 | |

4 | 15 | 20 | 20 |

Step 6: Examine rows successively, beginning with row 1, for a row with exactly one unmarked zero. If at least one exists, mark this zero with the symbol (,) to denote an assignment. Cross out (X) the other zeros in the same column so that additional assignment will not be made to that column. Repeat the process until each row has no unmarked zeros or at least two unmarked zeros.

Step 7: Examine columns successively, beginning with column 1, for a column with exactly one unmarked zero. If at least one exists, mark this zero with the symbol (,) to denote an assignment. Cross out (X) the other zeros in the same row so additional assignment will not be made to that row. Repeat the process until each column has no unmarked zeros or at least two unmarked zeros.

Step 8: Repeat Steps 7 and 8 successively (if necessary) until one of the three things occurs:

Step 9: Every row has an assignment (,). Go to Step 16.

Step 10: There are at least two unmarked zeros in each row and each column. Go to Step 7.

Step 11: There are no zeros left unmarked and a complete assignment has not been made. Go to Step 10.

Step 12: Check (Ö) all rows for which assignment (,) has not been made.

Step 13: Check (Ö) columns not already checked which have a zero in checked rows.

Step 14: Check (Ö) rows not already checked which have assignments in the checked column.

Step 15: Repeat Steps 11 and 12 until the chain of checking ends.

Step 16: Draw lines through all unchecked rows and through all checked columns. This will necessarily give the minimum number of lines needed to cover each zero at least one time.

Step 17: Examine the elements that do not have at least one line through them. Select the smallest of these and subtract it from every element in each row that contains at least one uncovered element. Add the same element to every element in each column that has a vertical line through it. Return to Step 7.

Step 18: List the assignment cost and combination corresponding to sub problem.

Step 19: Add assignment cost of each sub problem to obtain the total assignment cost of the main problem, which shall be the optimal cost, and also rearrange the combinations.

Step 20: Stop.

Solve the problem (

Now obtain the sum of cost, time, quality in each job of the ACM and we get modified ACM (,) (

Now apply the Hungarian method for Modified ACM (,), then the final optimal assignments of Modified ACM (,) (

The above illustration was taken by the defined algorithm and implemented on several sizes of the problems to test the effectiveness of the algorithm. This approach was implemented on different sizes of multi-objective balanced assignment problems from the above. We noticed that by using standard Hungarian method we could get the optimum value. The time complexity verified and found that they were getting optimum in less time comparative to other methods.

VentepakaYadaiah,V. V.Haragopal, (2016) Multi-Objective Optimization of Time-Cost-Quality Using Hungarian Algorithm. American Journal of Operations Research,06,31-35. doi: 10.4236/ajor.2016.61005