Editing Travelling salesman problem (section)

===Related problems===
<!-- This belongs to somewhere else-->

* An equivalent formulation in terms of [[graph theory]] is: Given a [[Glossary of graph theory|complete weighted graph]] (where the vertices would represent the cities, the edges would represent the roads, and the weights would be the cost or distance of that road), find a [[Hamiltonian cycle]] with the least weight. This is more general than the [[Hamiltonian path problem]], which only asks if a Hamiltonian path (or cycle) exists in a non-complete unweighted graph.
* The requirement of returning to the starting city does not change the [[Computational complexity theory|computational complexity]] of the problem; see [[Hamiltonian path problem]].
* Another related problem is the [[Bottleneck traveling salesman problem|bottleneck travelling salesman problem]]: Find a Hamiltonian cycle in a [[glossary of graph theory|weighted graph]] with the minimal weight of the weightiest [[edge (graph theory)|edge]]. A real-world example is avoiding narrow streets with big buses.<ref>{{cite web|url=http://online.WSJ.com/public/resources/documents/print/WSJ_-A002-20170812.pdf|title=''How Do You Fix School Bus Routes? Call MIT'' in Wall street Journal}}</ref> The problem is of considerable practical importance, apart from evident transportation and logistics areas. A classic example is in [[Printed circuit board|printed circuit]] manufacturing: scheduling of a route of the [[drill]] machine to drill holes in a PCB. In robotic machining or drilling applications, the "cities" are parts to machine or holes (of different sizes) to drill, and the "cost of travel" includes time for retooling the robot (single-machine job sequencing problem).<ref>{{Citation
| last1 = Behzad| first1 = Arash| last2 = Modarres
| first2 = Mohammad| year = 2002
| title = New Efficient Transformation of the Generalized Traveling Salesman Problem into Traveling Salesman Problem
| journal = Proceedings of the 15th International Conference of Systems Engineering (Las Vegas)}}</ref> 
* The [[Set TSP problem|generalized travelling salesman problem]], also known as the "travelling politician problem", deals with "states" that have (one or more) "cities", and the salesman must visit exactly one city from each state. One application is encountered in ordering a solution to the [[cutting stock problem]] in order to minimize knife changes. Another is concerned with drilling in [[semiconductor]] manufacturing; see e.g., {{US patent|7054798}}. Noon and Bean demonstrated that the generalized travelling salesman problem can be transformed into a standard TSP with the same number of cities, but a modified [[distance matrix]].
* The sequential ordering problem deals with the problem of visiting a set of cities, where precedence relations between the cities exist.
* A common interview question at [[Google]] is how to route data among data processing nodes; routes vary by time to transfer the data, but nodes also differ by their computing power and storage, compounding the problem of where to send data.
* The [[Traveling purchaser problem|travelling purchaser problem]] deals with a purchaser who is charged with purchasing a set of products. He can purchase these products in several cities, but at different prices, and not all cities offer the same products. The objective is to find a route between a subset of the cities that minimizes total cost (travel cost + purchasing cost) and enables the purchase of all required products.