Start with the problem.
Routing workloads differ in stop count, fleet size, constraint complexity, and solver approach. A compute strategy that works well for one configuration may be inefficient for another.
Look for useful parallel work.
Candidate scoring, local search, and batches of scenarios may offer opportunities for parallel evaluation. The benefit depends on the algorithm, data movement, workload size, and available hardware.
Measure the end-to-end workflow.
Solver runtime is only one part of the story. Consider input preparation, data transfer, orchestration, result validation, and cost. A faster kernel does not by itself establish a faster planning workflow.
Compare like with like.
Use the same inputs, constraints, objective, and evaluation budget for a meaningful baseline. Report solution quality alongside runtime. A fast infeasible answer is not an operational improvement.
Keep the strategy flexible.
CPU, GPU, and hybrid methods can each be appropriate. RouteForge treats compute as an engineering choice guided by the workload and evaluation criteria.