How AI-Powered Swing‑Hang Scheduling Cuts Logistics Costs and Empty Miles

Timeliness and cost are core concerns in logistics, and the scheduling of transport tasks plays a crucial role in addressing them.

Intelligent Swing‑Hang Scheduling Algorithm

The G7 intelligent swing‑hang scheduling algorithm, based on an improved multi‑objective genetic algorithm, simulates natural selection to find the optimal cost schedule for transport tasks under limited vehicle capacity.

Workflow

Part I: Full‑process visibility

Leveraging G7’s IoT platform, the system visualizes the entire transport chain—including drivers, tractors, trailers, box trucks, stations, and delivery points—capturing real‑time data such as location, time, and vehicle status, which are fed back to the G7 management cloud.

Part II: Scheduling for efficiency and cost reduction

The algorithm follows four main steps:

1) Preset identical daily delivery tasks, generate multiple vehicle‑combination plans, and evaluate each plan’s cost.

2) Select plans probabilistically based on cost, giving lower‑cost plans higher selection probability while preserving diversity.

3) Apply crossover (combining parts of different plans) and mutation (randomly swapping tasks or vehicles) to create new candidate solutions, expanding the search space.

4) Iterate the process until the most cost‑effective delivery plan emerges.

When the optimal plan is generated, the G7 intelligent management system automatically matches tasks to vehicles, reducing empty runs and fuel consumption, improving vehicle and driver turnover, and significantly boosting overall logistics efficiency.

The swing‑hang scheduling algorithm serves as a technical foundation for broader delivery optimization, powered by AI+IA, and G7 will continue to iterate the solution to meet deeper logistics demands.