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The assignment problem is a fundamental combinatorial optimization problem. In its most general form, the problem is as follows: The problem instance has a number of agents and a number of tasks. Any agent can be assigned to perform any task, incurring some cost that may vary depending on the agent-task assignment.
According to professor of strategy Arnaud Chevallier, elaborating an approach used at McKinsey & Company, [3] there are two types of issue trees: diagnostic ones and solution ones. [4] Diagnostic trees break down a "why" key question, identifying all the possible root causes for the problem.
Solution of a travelling salesman problem: the black line shows the shortest possible loop that connects every red dot. In the theory of computational complexity, the travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the ...
A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.
A solution to Kirkman's schoolgirl problem with vertices denoting girls and colours denoting days of the week [1] Kirkman's schoolgirl problem is a problem in combinatorics proposed by Thomas Penyngton Kirkman in 1850 as Query VI in The Lady's and Gentleman's Diary (pg.48). The problem states:
The minimum pattern count problem: to find a minimum-pattern-count solution amongst the minimum-waste solutions. This is a very hard problem, even when the waste is known. [10] [11] [12] There is a conjecture that any equality-constrained one-dimensional instance with n sizes has at least one minimum waste solution with no more than n + 1 ...
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Determining the optimal solution to VRP is NP-hard, [2] so the size of problems that can be optimally solved using mathematical programming or combinatorial optimization can be limited. Therefore, commercial solvers tend to use heuristics due to the size and frequency of real world VRPs they need to solve.