GSL Solver

GSL Solver is an AI route optimization engine that applies deterministic Vehicle Routing Problem (VRP) algorithms to delivery fleet planning for logistics managers, supply chain engineers, and enterprise operations teams. Unlike traditional metaheuristic solvers that produce different results on each run, GSL Solver's zero-variance architecture guarantees identical, auditable outputs every single execution — making KPI planning and compliance reporting straightforward.

The engine handles complex constraint types including CVRP, VRPTW, and MDVRPTW, scaling from 30 to 10,000+ nodes without performance degradation. Its K-Focus structural framework mathematically minimizes the number of vehicles required before route optimization even begins, directly reducing fixed fleet costs. In a published 800-node VRPTW benchmark (Homberger c1_8_1), GSL required 80 trucks and 32 seconds where a standard ALNS metaheuristic needed 106 trucks and over 3.5 minutes — translating to an estimated $1.3M annual OPEX saving on that single dataset. Real-world validation against a Bosnian industrial network showed a 31.4% cost reduction and 169 km saved per delivery cycle.

Access is fully cloud-based via a web interface or REST API, eliminating the need for on-premise servers or specialized IT infrastructure. Pricing follows a credit model where 1 Node Credit equals 1 data line processed.

Best for: logistics operations teams, last-mile delivery companies, and fleet managers running medium-to-enterprise scale networks who need reproducible, audit-ready routing decisions rather than probabilistic approximations.

Last-mile delivery route planning, enterprise fleet size reduction, VRPTW constraint solving, multi-depot logistics optimization, logistics KPI planning and auditing, supply chain cost reduction analysis, API-based routing integration for TMS platforms