CLASSICAL HYBRID SOLUTION TO MULTI-STOP ROUTING
Relational routing data tables are converted into graphs comprising nodes and vertices. The nodes can include origins and destinations associated with routes, while the vertices represent route parameters. Route segments can then be mapped from the graphs. For an origin-destination input, a set of shortest parameterized paths among the route segments is identified. These shortest parameterized paths includes route segments weighted over a range of route parameters. Within a solution domain, an optimal solution can be generated by filtering the shortest parameterized path based on a set of one or more selected parameters to generate an optimal solution. A classical threshold determines whether the optimal solution is generated using a classical computing process or a quantum computing process.
1 . A computerized method comprising:
converting relational routing data tables into graphs comprising nodes and vertices, wherein nodes include origins and destinations associated with routes, and wherein vertices represent one or more route parameters;
mapping route segments from the graphs;
for an origin-destination input, identifying a shortest parameterized path among the route segments, the shortest parameterized path comprising route segments weighted over a range of input parameters, the input parameters governed by a constraint equation corresponding to an objective function;
determining a solution domain from the range of input parameters; and
generating an optimal solution within the solution domain based on applying a classical threshold, wherein based on the classical threshold, the optimal solution is generated using a process selected from one of a classical computing process and a quantum solution process, wherein:
the classical computing process invokes a classical computing device to determine the optimal solution from the solution domain; and
the quantum computing process invokes a quantum annealer to determine the optimal solution from the solution domain.
2 . The method of claim 1 , wherein the one or more route parameters comprise at least one of distance, time, cost, weight, or volume.
3 . The method of claim 1 , wherein route segments are mapped based on a time of arrival or time of departure constraint.
4 . The method of claim 1 , wherein the input parameters is one of distance, time, cost, and object weight.
5 . The method of claim 1 , further comprising minimizing the constraint equation to identify the shortest parameterized path.
6 . The method of claim 1 , further comprising filtering the optimal routing solution from the range of input parameters such that the optimal routing solution respects solution constraints and the objective function.
7 . The method of claim 1 , further comprising verifying an accuracy of the shortest parameterized path.
8 . The method of claim 1 , further comprising inverse transforming the optimal routing solution to map the optimal routing solution to the origin-destination input to output the optimal routing solution to a computing device.
9 . A system comprising:
a classical computing device;
a quantum annealer;
at least one processor; and
one or more computer storage media storing computer readable instructions thereon that when executed by the at least one processor cause the at least one processor to perform operations comprising:
converting relational routing data tables into graphs comprising nodes and vertices, wherein nodes include origins and destinations associated with routes, and wherein vertices represent one or more route parameters;
mapping route segments from the graphs;
for an origin-destination input, identifying a shortest parameterized path among the route segments, the shortest parameterized path comprising route segments weighted over a range of input parameters, the input parameters governed by a constraint equation corresponding to an objective function;
determining a solution domain from the range of input parameters; and
generating an optimal solution within the solution domain based on applying a classical threshold, wherein based on the classical threshold, the optimal solution is generated using a process selected from one of a classical computing process and a quantum solution process, wherein:
the classical computing process invokes the classical computing device to determine the optimal solution from the solution domain; and
the quantum computing process invokes the quantum annealer to determine the optimal solution from the solution domain.
10 . The system of claim 9 , further comprising further comprising minimizing the constraint equation to identify the shortest parameterized path.
11 . The system of claim 9 , further comprising filtering the optimal routing solution from the range of input parameters such that the optimal routing solution respects solution constraints and the objective function.
12 . The system of claim 9 , further comprising verifying an accuracy of the shortest parameterized path.
13 . The system of claim 9 , further comprising inverse transforming the optimal routing solution to map the optimal routing solution to the origin-destination input to output the optimal routing solution to a computing device.
14 . One or more computer storage media storing computer-readable instructions thereon that when executed by a processor cause the processor to perform operations comprising:
determining a computational requirement of an optimization problem comprising routing packages to destination locations; and
based on the computational requirement compared to a threshold computational capacity of a classical computing device, selecting a computational process from one of a classical computational process and a quantum computing process, wherein a classical computing device or a quantum computing device performs operations comprising:
converting relational routing data tables into graphs comprising nodes and vertices, wherein nodes include origin and destination locations associated with routes, and wherein vertices represent one or more route parameters;
mapping route segments from the graphs;
for an origin-destination input, identifying a set of shortest parameterized paths among the route segments, the shortest parameterized path comprising route segments weighted over a range of input parameters, the input parameters governed by a constraint equation corresponding to an objective function; and
generating the optimal solution by filtering the set of shortest parameterized paths based on a selected route parameter.
15 . The media of claim 14 , wherein the one or more route parameters comprise at least one of distance, time, cost, weight, or volume.
16 . The media of claim 14 , wherein the input parameters is one of distance, time, cost, and object weight.
17 . The media of claim 14 , further comprising minimizing the constraint equation to identify the shortest parameterized path.
18 . The media of claim 14 , further comprising filtering the optimal routing solution from the range of input parameters such that the optimal routing solution respects solution constraints and the objective function.
19 . The media of claim 14 , further comprising verifying an accuracy of the shortest parameterized path.
20 . The media of claim 14 , further comprising inverse transforming the optimal routing solution to map the optimal routing solution to the origin-destination input to output the optimal routing solution to a computing device.