This paper addresses an integrated rack assignment and robot routing problem arising in robotic movable fulfillment systems (RMFS). This NP-hard planning task goes beyond current literature by simultaneously optimizing movable rack selection and multi-agent collision-free path finding, rather than decomposing them. A mixed integer programming (MIP) model with a new level-space-time network representation is proposed, jointly considering reusable racks, robot-rack pairings, storage repositioning, and collision avoidance. To improve computational efficiency, a fast rolling horizon heuristic and greedy algorithm are developed. Extensive experiments demonstrate that the integrated method's solutions can improve by 30 % upon conventional decomposed approaches. Intriguing test cases reveal the model, suggesting non-intuitive robot carryover policies that are unfound by separate selection and routing methods. This indicates potential optimization benefits from explicitly coordinating task assignment, scheduling, and routing decisions in complex automated warehousing systems. The rolling horizon heuristic solutions approach optimality with much greater efficiency than directly solving one large MIP, validating its practical value. This research provides useful integrated modeling insights, efficient solution algorithms, and decision support for efficiently controlling next-generation robotic movable fulfillment systems.

中文翻译：

---

采用整数编程的机器人可移动履行系统中的高效路由：滚动视野和启发式方法


本文解决了机器人可移动履行系统（RMFS）中出现的集成机架分配和机器人路由问题。这种 NP 难规划任务通过同时优化可移动机架选择和多智能体无碰撞路径查找而不是分解它们，超越了当前文献。提出了一种具有新的水平时空网络表示的混合整数规划（MIP）模型，共同考虑可重复使用的货架、机器人-货架配对、存储重新定位和避免碰撞。为了提高计算效率，开发了快速滚动水平启发式和贪婪算法。大量实验表明，集成方法的解决方案可以比传统分解方法提高 30%。有趣的测试用例揭示了该模型，提出了单独的选择和路由方法未发现的非直观机器人遗留策略。这表明在复杂的自动化仓储系统中明确协调任务分配、调度和路由决策具有潜在的优化优势。与直接求解一个大型 MIP 相比，滚动地平线启发式解决方案以更高的效率接近最优，验证了其实用价值。这项研究为有效控制下一代机器人可移动履行系统提供了有用的集成建模见解、高效的解决算法和决策支持。