This research addresses the problem of dividing a group of people into a collection of teams that need to be “balanced” across a variety of different attributes. This type of problem arises, for example, in an academic setting where it is necessary to partition students into a number of balanced study teams. Recent proposed solutions use linear and nonlinear integer programming. This talk describes a linear approximation to the nonlinear objective function that significantly reduces the number of integer variables and constraints. Computational experiments are performed on random instances of the problem which lead to a way to determine the quality of the optimal solution obtained by the heuristics. The approach developed here almost always obtains better balanced teams than those from prior methods.
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