Generalized assignment problem: Difference between revisions

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{{short description|Combinatorial optimization problem}}

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 [[Agent-based model|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.

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==

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-tasktasks assignmentdo arenot equalvary between different agents, 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==

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\text{maximize } & \sum_{i=1}^m\sum_{j=1}^n p_{ij} x_{ij}. \\

\text{subject to } & \sum_{j=1}^n w_{ij} x_{ij} \le t_i & & i=1, \ldots, m; \\

& \sum_{i=1}^m x_{ij} =\le 1 & & j=1, \ldots, n; \\

& x_{ij} \in \{0,1\} & & i=1, \ldots, m, \quad j=1, \ldots, n;

\end{align}

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== Complexity ==

The generalized assignment problem is [[NP-hard]],.<ref>{{citation

| last1 = Özbakir | first1 = Lale

| last2 = Baykasoğlu | first2 = Adil

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| series = Applied Mathematics and Computation

| title = Bees algorithm for generalized assignment problem

| doi = 10.1016/j.amc.2009.11.018

| url = http://www.sciencedirect.com/science/article/pii/S0096300309010078

| volume = 215

| issue = 11

| year = 2010}}.</ref> and it is even [[APX-hard]] to approximate it. Recently it was shown that an extension of it is <math>e/(e-1) - \varepsilon</math> hard to approximate for every <math>\varepsilon</math>.{{Citation needed|date=November 2012}}

| year = 2010}}.</ref> However, there are linear-programming relaxations which give a <math>(1 - 1/e)</math>-approximation.<ref>{{cite conference |last1=Fleischer |first1=Lisa |last2=Goemans |first2=Michel X. |last3=Mirrokni |first3=Vahab S. |last4=Sviridenko |first4=Maxim |date=2006 |title=Tight approximation algorithms for maximum general assignment problems |conference=Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm - SODA '06 |pages=611–620}}</ref>

==Greedy approximation algorithm==

For the problem variant in which not every item must be assigned to a bin, there is a family of algorithms for solving the GAP by using a combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for the GAP.<ref>{{cite journal |doi=10.1016/j.ipl.2006.06.003|title=An efficient approximation for the Generalized Assignment Problem|journal=Information Processing Letters|volume=100|issue=4|pages=162–166|year=2006|last1=Cohen|first1=Reuven|last2=Katzir|first2=Liran|last3=Raz|first3=Danny|citeseerx=10.1.1.159.1947}}</ref>

Using any <math> \alpha</math>-approximation algorithm ALG for the [[knapsack problem]], it is possible to construct a (<math> \alpha+1</math>)-approximation for the generalized assignment problem in a greedy manner using a residual profit concept.

Using any <math> \alpha</math>-approximation algorithm ALG for the [[knapsack problem]], it is possible to construct a (<math> \alpha + 1</math>)-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 <math>j</math> a tentative selection of items to bin <math>b_j</math> is selected.

The selection for bin <math>b_j</math> might change as items might be reselected in a later iteration for other bins.

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==See also==

* [[Assignment problem]]

== References ==

{{Reflistreflist}}

* Reuven Cohen, Liran Katzir, and Danny Raz, [http://www.cs.technion.ac.il/~lirank/pubs/2006-IPL-Generalized-Assignment-Problem.pdf "An Efficient Approximation for the Generalized Assignment Problem"], Information Processing Letters, Vol. 100, Issue 4, pp.&nbsp;162–166, November 2006.

* Lisa Fleischer, Michel X. Goemans, Vahab S. Mirrokni, and Maxim Sviridenko, [http://www-math.mit.edu/~goemans/PAPERS/ga-soda06.pdf "Tight Approximation Algorithms for Maximum General Assignment Problems"], SODA 2006, pp.&nbsp;611–620.

* Hans Kellerer, Ulrich Pferschy, David Pisinger, ''Knapsack Problems '', 2005. Springer Verlag {{ISBN|3-540-40286-1}}

==Further reading==

== External links ==

* {{cite book |isbn=978-3-540-24777-7|title=Knapsack Problems|last1=Kellerer|first1=Hans|last2=Pferschy|first2=Ulrich|last3=Pisinger|first3=David|date=2013-03-19|publisher=Springer }}

[[Category:NP-complete problems]]