Multidimensional assignment problem: Difference between revisions

Revision as of 18:01, 19 February 2022

Revision as of 18:02, 19 February 2022

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{{Short description|Generalization of linear assignment problem from two to multiple dimensions}}

The '''multidimensional assignment problem''' is a fundamental [[combinatorial optimization]] problem which was introduced by Pierskalla.<ref name="Pier68">{{cite journal |last=Pierskalla |first=William P. |title=Letter to the Editor—The Multidimensional Assignment Problem | journal=Operations Research 16(2) |publisher=INFORMS |date=1968 |volume=16 |issue=2 |pagepages=422-431422–431 |doi=10.1287/opre.16.2.422 |url=https://pubsonline.informs.org/doi/abs/10.1287/opre.16.2.422}}</ref>. This problem can be seen as a generalization of the linear [[assignment problem]].<ref name="Pasi21">{{Cite arXiv|last=Kammerdiner|first=Alla|last2=Semenov|first2=Alexander|last3=Pasiliao|first3=Eduardo |date=2021|title=Multidimensional Assignment Problem for multipartite entity resolution|eprint=2112.03346}}</ref> In words, the problem can be described as follows:

: An instance of the problem has a number of ''agents'' (i.e., ''cardinality'' parameter) and a number of ''job characteristics'' (i.e., ''dimensionality'' parameter) such as task, machine, time interval, etc. For example, an agent can be assigned to perform task X, on machine Y, during time interval Z. Any agent can be assigned to perform a job with any combination of unique job characteristics at some ''cost''. These costs may vary based on the assignment of agent to a combination of job characteristics - specific task, machine, time interval, etc. The problem is to minimize the ''total cost'' of assigning the agents so that the assignment of agents to each job characteristic is an [[injective function]], or [[one-to-one function]] from agents to a given job characteristic.

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=== Problem parameters ===

The multidimensional assignment problem (MAP) has two key parameters that determine ''the size of a problem instance'':

# The '''[[dimension|dimensionality]]ality parameter''' <math>D</math>

# The '''[[cardinality]] parameter''' <math>N = |A|</math>, where <math>|A|</math> denotes the number of elements in <math>A</math>.

=== Size of cost array ===

Any problem instance of the MAP with parameters <math>D, N</math> has its specific '''cost array''' <math>C</math>, which consists of <math>N^{D}</math> instance-specific costs/weights parameters <math>C(a,a_1,\ldots,a_{D-1})</math>. <math>N^{D}</math> is the ''size'' of cost array.

=== Number of feasible solutions ===

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The problem found application in many domains:

*[[Scheduling (production processes)]] <ref name="Pier68" />

*[[Data fusion|Multi-sensor data fusion]]<ref>{{Cite journal|last=Poore|first=Aubrey B.|date=1994|title=Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking|journal=Computational Optimization and Applications|volume=3|issue=1|pages=27-5727–57|doi=10.1007/BF01299390}}</ref>

*[[Record linkage|Record linkage or multipartite entity resolution]] <ref name="Pasi21" />

*[[Particle physics|Elementary particle physics]]<ref>{{Cite journal|last=Pusztaszeri|first=Jean-François|last2=Rensing|first2=Paul E.|last3=Liebling|first3=Thomas M.|date=1996|title=Tracking elementary particles near their primary vertex: a combinatorial approach|journal=Journal of Global Optimization|volume=9|issue=1|pages=41-6441–64|doi=10.1007/BF00121750}}</ref>

*[[Medical alarm|Fall detection in elderly with small wearable devices]]<ref>{{Cite journal|last=Kammerdiner|first=Alla R.|last2=Guererro|first2=Andre N.|date=2019|title=Data-driven combinatorial optimization for sensor-based assessment of near falls|url=http://link.springer.com/10.1007/s10479-017-2585-1|journal=Annals of Operations Research|language=en|volume=276|issue=1-2|pages=137–153|doi=10.1007/s10479-017-2585-1|issn=0254-5330}}</ref>