# Generalized Assignment Problem Greedy Algorithm Example

The assignment problem is defined as:

**There are n people who need to be assigned to n jobs, one person per job. The cost that would accrue if the ith person is assigned to the jth job is a known quantity C[i,j] for each pair i, j = 1, 2, ..., n. The problem is to find an assignment with the minimum total cost.**

There is a question asking to design a greedy algorithm to solve the problem. It also asks if the greedy algorithm always yields an optimal solution and for the performance class of the algorithm. Here is my attempt at designing an algorithm:

Am I correct in saying that my algorithm is of O(n^2)? Am I also correct in saying that a greedy algorithm does not always yield an optimal solution? I used my algorithm on the following cost matrix and it is clearly not the optimal solution. Did I Do something wrong?

algorithmsgreedy-algorithmsassignment-problem

asked Apr 7 '17 at 5:53

In applied mathematics, the maximum **generalized assignment problem** is a problem in combinatorial optimization. This problem is a generalization of the assignment problem in which both tasks and agents have a size. Moreover, the size of each task might vary from one agent to the other.

This problem in its most general form is as follows:

There are a number of agents and a number of tasks. Any agent can be assigned to perform any task, incurring some cost and profit that may vary depending on the agent-task assignment. Moreover, each agent has a budget and the sum of the costs of tasks assigned to it cannot exceed this budget. It is required to find an assignment in which all agents do not exceed their budget and total profit of the assignment is maximized.

## In special cases[edit]

In the special case in which all the agents' budgets and all tasks' costs are equal to 1, this problem reduces to the assignment problem. When the costs and profits of all agents-task assignment are equal, this problem reduces to the multiple knapsack problem. If there is a single agent, then, this problem reduces to the knapsack problem.

## Explanation of definition[edit]

In the following, we have *n* kinds of items, through and *m* kinds of bins through . Each bin is associated with a budget . For a bin , each item has a profit and a weight . A solution is an assignment from items to bins. A feasible solution is a solution in which for each bin the total weight of assigned items is at most . The solution's profit is the sum of profits for each item-bin assignment. The goal is to find a maximum profit feasible solution.

Mathematically the generalized assignment problem can be formulated as an integer program:

## Complexity[edit]

The generalized assignment problem is NP-hard,^{[1]} and it is even APX-hard to approximate it. Recently it was shown that an extension of it is hard to approximate for every .^{[citation needed]}

## Greedy approximation algorithm[edit]

Using any -approximation algorithm ALG for the knapsack problem, it is possible to construct a ()-approximation for the generalized assignment problem in a greedy manner using a residual profit concept. The algorithm constructs a schedule in iterations, where during iteration a tentative selection of items to bin is selected. The selection for bin might change as items might be reselected in a later iteration for other bins. The residual profit of an item for bin is if is not selected for any other bin or – if is selected for bin .

Formally: We use a vector to indicate the tentative schedule during the algorithm. Specifically, means the item is scheduled on bin and means that item is not scheduled. The residual profit in iteration is denoted by , where if item is not scheduled (i.e. ) and if item is scheduled on bin (i.e. ).

Formally:

- Set
- For do:
- Call ALG to find a solution to bin using the residual profit function . Denote the selected items by .
- Update using , i.e., for all .

## See also[edit]

## References[edit]

- Reuven Cohen, Liran Katzir, and Danny Raz, "An Efficient Approximation for the Generalized Assignment Problem", Information Processing Letters, Vol. 100, Issue 4, pp. 162–166, November 2006.
- Lisa Fleischer, Michel X. Goemans, Vahab S. Mirrokni, and Maxim Sviridenko, "Tight Approximation Algorithms for Maximum General Assignment Problems", SODA 2006, pp. 611–620.
- Hans Kellerer, Ulrich Pferschy, David Pisinger,
*Knapsack Problems*, 2005. Springer Verlag ISBN 3-540-40286-1