dwave-optimization#

Reference documentation for dwave-optimization:

About dwave-optimization#

dwave-optimization enables the formulation of nonlinear models for industrial optimization problems. The package includes:

  • A class for nonlinear models used by the Leap service’s quantum-classical hybrid nonlinear-program solver.

  • Model generators for common optimization problems.

Example Usage#

The flow-shop scheduling problem is a variant of the renowned job-shop scheduling optimization problem. Given n jobs to schedule on m machines, with specified processing times for each job per machine, minimize the makespan (the total length of the schedule for processing all the jobs). For every job, the i-th operation is executed on the i-th machine. No machine can perform more than one operation simultaneously.

This small example builds a model for optimizing the schedule for processing two jobs on three machines.

from dwave.optimization.generators import flow_shop_scheduling

processing_times = [[10, 5, 7], [20, 10, 15]]
model = flow_shop_scheduling(processing_times=processing_times)

For explanations of the terminology, see the Concepts section.

Usage Information#