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Worked example of assigning tasks to an unequal number of workers using the Hungarian method. 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.
The problem for graphs is NP-complete if the edge lengths are assumed integers. The problem for points on the plane is NP-complete with the discretized Euclidean metric and rectilinear metric. The problem is known to be NP-hard with the (non-discretized) Euclidean metric. [3]: ND22, ND23
"A worked example is a step-by-step demonstration of how to perform a task or how to solve a problem". [8]: 190 Worked examples are designed to support initial acquisition of cognitive skills through introducing a formulated problem, solution steps and the final solution.
The XY problem obscures the real issues and may even introduce secondary problems that lead to miscommunication, resource mismanagement, and sub-par solutions. The solution for the support personnel is to ask probing questions as to why the information is needed in order to identify the root problem Y and redirect the end user away from an ...
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 workaround is a bypass [1] of a recognized problem or limitation in a system or policy. [2] A workaround is typically a temporary fix [3] [4] that implies that a genuine solution to the problem is needed. But workarounds are frequently as creative as true solutions, involving outside the box thinking [5] [6] in their creation.
Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business and technical fields. The former is an example of simple problem solving (SPS) addressing one issue, whereas the latter is complex problem solving (CPS) with multiple interrelated obstacles. [1]
Greedy algorithms fail to produce the optimal solution for many other problems and may even produce the unique worst possible solution. One example is the travelling salesman problem mentioned above: for each number of cities, there is an assignment of distances between the cities for which the nearest-neighbour heuristic produces the unique ...