Combinatorial optimization

Multidimensional assignment problem

The multidimensional assignment problem (MAP) is a fundamental combinatorial optimization problem which was introduced by . This problem can be seen as a generalization of the linear assignment problem. 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. Alternatively, describing the problem using graph theory: The multidimensional assignment problem consists of finding, in a weighted multipartite graph, a matching of a given size, in which the sum of weights of the edges is minimum. (Wikipedia).

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From playlist Week 2 2015 Shorts

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From playlist How to Solve Multi Step Equations with Variables on Both Sides

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Related pages

Combinatorial optimization | Permutation | Loss function | Weight function | Dimension | Assignment problem | Matching (graph theory) | Cardinality | Scheduling (production processes) | Algorithm | Multipartite graph | Injective function | Feasible region