Coordinated optimization of scheduling and path planning for delivery robots in intelligent manufacturing workshops

Delivery robots (DRs) are essential components of modern automated logistics systems, particularly in manufacturing workshops where they are extensively used for material delivery tasks. Their implementation provides significant benefits, including reduced production costs and enhanced transportation efficiency. This paper addresses a critical challenge in this field: the coordinated optimization of scheduling and path planning for multiple DRs. We tackle this problem by comprehensively considering both robot energy consumption costs and task delay penalties, formulating it as a mixed-integer linear program (MILP). To solve this complex optimization problem, we employ an adaptive genetic algorithm (AGA). The effectiveness and efficiency of the proposed model and algorithm are validated through the comprehensive simulation case.