The design of a guide-path map is a well-known problem in various industries, such as automated guided vehicle (AGV) systems, road networks, and train track design. Nowadays, designing a path map is a manual, time-consuming, and expensive process. The solutions often turn out to be far from optimal. Several approaches are known to generate path maps automatically. The main challenges are computation efficiency due to large solution spaces and the lack of feasibility of solutions for application to real-world layouts. Little research focuses on the importance of computational efficiency to keep up with the pace of dynamic user requirements and constraints.

The goal is to design a framework that balances computational efficiency with feasibility in automated path map generation. A feasible path map can continuously distribute requested flow targets and is practically useable without many manual changes. It is expected that less optimal but still feasible solutions can be generated more efficiently by using a smaller solution space.

The framework consists of four stages: a practical problem, a mathematical model, a mathematical solution, and a practical solution. All solutions are theoretically validated on two aspects: capacity and performance.

The framework is tested using three layouts: low complexity and small-scale, complex and large scale, and one in between. The exploratory model outperforms in the complex large-scale layout while the goal-oriented models outperform in the other layouts.

Path maps are generated in a reasonable and controllable time. The settings allow a trade-off between the efficiency and feasibility of solutions. Due to its efficient computation, the model has the potential to generate path maps that are directly usable in real applications, if several realistic implementations are included to improve the feasibility of solutions.