A Novel GA-CP Method for Fixed-Type Multi-Robot Collaborative Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing, multi-robot collaborative systems are increasingly integrated into various production processes. In the flexible job shop environment of automotive stamping, achieving smooth operation and efficient manufacturing of production lines hinges on solving the critical issues of multi-robot task allocation and scheduling. However, for such fixed-type multi-robot collaboration problems, robots are constrained by specific areas or predetermined trajectories, and processing times can only be adjusted by varying the number of available robots. Therefore, the scheduling problem in multi-robot collaborative flexible job shop problems (MCFJSP) is divided into two sub-problems: FJSP with controllable processing times and multi-robot collaborative task balancing. To address these, we propose three distinct methods: mixed integer linear programming (MILP), constraint programming (CP), and a hybrid genetic algorithm-constraint programming (GA-CP). Finally, a set of 48 benchmark cases and two real-world cases are developed to test these methods. Comparative experiments demonstrate that the MILP model is superior in small-scale cases, while the GA-CP model exhibits the best overall performance in medium to large-scale cases. Furthermore, through comparisons with two advanced algorithms, the effectiveness and superiority of the GA-CP method in addressing real-world cases are confirmed.[8pt]Note to Practitioners—In modern manufacturing environments, particularly in industries like automotive manufacturing, multiple robots working together on complex tasks are increasingly common. This paper addresses the practical challenge of effectively scheduling these robots to maximize efficiency while reducing the number of robots assigned to each task. This study introduces and compares different methods, including MILP, CP, and GA-CP methods, that can help practitioners determine the best way to allocate tasks among robots and schedule them efficiently. For example, in small-scale tasks, the MILP model can quickly provide the best solution. However, as the complexity and scale of the task increase, the GA-CP method becomes more practical, offering high-quality solutions within a reasonable timeframe. The study provides actionable insights that can be applied directly to real-world production scenarios, helping practitioners in industries like automotive stamping to maximize job shop productivity while reducing energy consumption losses in robot processing.