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The 28 pages that link to Shortest path problem, each with the reason it gives.

• Graph theoryRelated: It models route planning when edges represent distance, time, or cost.

• GeodesicRelated: On suitable spaces, geodesics provide local solutions to shortest-path problems.

• Graph (discrete mathematics)Related: Routing is a direct use of paths and edge weights.

• Arc lengthRelated: Arc length supplies the path cost when comparing routes through continuous space.

• Edge (graph theory)Related: Edge lengths or weights determine the cost of each candidate route.

• DistanceRelated: Many distance calculations reduce to finding a shortest route through a space.

• Dynamic programmingRelated: Shortest-path algorithms use recursive optimality across paths and graph states.

• Breadth-first searchNarrower topic: Breadth-first search solves it when every edge has equal cost.

• Dijkstra's algorithmNarrower topic: Dijkstra's algorithm is one method for the single-source version of this broader problem.

• Path (graph theory)Related: A path becomes an optimization object when its length or weight is minimized.

• Maximum flow problemCompared with: It optimizes the cost of one route rather than the total amount routed through a network.

• Road networkRelated: Road routing uses it to find paths by distance, time, or another cost.

• Weighted graphRelated: It treats edge weights as costs and combines them along candidate paths.

• Transportation networkRelated: Route-finding methods select paths through a network using distance, time, or cost.

• Transportation problemCompared with: It routes a single flow along connected arcs rather than allocating quantities across source-destination pairs.

• Edsger W. DijkstraNarrower topic: Dijkstra’s algorithm solves this problem for nonnegative edge weights.

• Flow networkCompared with: It optimizes route length rather than total transmitted quantity.

• Vehicle routing problemCompared with: A shortest-path problem connects a given pair, while routing coordinates visits to many locations.

• A* search algorithmRelated: A* solves this problem when its heuristic and search conditions support optimality.

• Hamiltonian pathCompared with: It optimizes distance between endpoints rather than requiring a visit to every vertex.

• Bellman–Ford algorithmNarrower topic: Bellman–Ford solves the single-source version of this broader optimization problem.

• Floyd–Warshall algorithmNarrower topic: Floyd–Warshall solves this problem simultaneously for every ordered pair of vertices.

• Kruskal's algorithmCompared with: A minimum spanning tree minimizes total network weight, not individual path distances.

• Minimum-cost flow problemBroader topic: It is a single-unit routing case when capacities and balances impose no further complications.

• Richard BellmanRelated: Dynamic programming solves it by relating each route to smaller path problems.

• Hamiltonian path problemCompared with: Unlike Hamiltonian path, it does not require visiting every vertex.

• Hopf–Rinow theoremRelated: The theorem guarantees a minimizing geodesic between any two points under completeness.

• Chinese checkersRelated: Efficient routes through the board reduce the distance pieces must travel to the opposite arm.