Recent trends towards larger and more complex systems necessitate the use of heterogeneous and flexible automated guided vehicles (AGVs) to fulfill the transport demand within a factory. To operate the fleet of AGVs efficiently, it is also important to consider their limited battery capacity. In this context, we tackle the problem of scheduling transport requests on multi-load and multi-ability AGVs with battery management. Each AGV can carry more than one load at a time and have specific capabilities such as lift loads, tow loads, or handle loads with a mounted robot arm. Each request consists of a pickup and a delivery task associated with an origin, a destination, a soft time window, and a priority. Each transport request may also require different AGV capabilities, and the AGV batteries can be recharged partially under consideration of a critical battery threshold. The decisions involve assigning transport and charging requests to AGVs, sequencing these requests, and determining the arrival times and charging duration. A mixed-integer linear programming model is formulated. A hybrid adaptive large neighborhood search with an integrated local search method is proposed to find a feasible schedule with the aim to minimize the tardiness costs of requests and travel costs of AGVs. We illustrate the efficacy of the hybrid algorithm with an industry case study using real-world data. The computational results reveal a 20%–50% cost reduction in current practice by using our hybrid algorithm, and around 50% cost reduction with respect to a single-load AGV scheduling approach proposed in the literature.

最近朝着更大和更复杂的系统发展的趋势需要使用异构和灵活的自动导引车 (AGV) 来满足工厂内的运输需求。为了有效地运营 AGV 车队，考虑其有限的电池容量也很重要。在这种情况下，我们解决了在具有电池管理功能的多负载和多能力 AGV 上调度运输请求的问题。每个 AGV 一次可以承载多个负载，并具有特定功能，例如提升负载、牵引负载或通过安装的机器人手臂处理负载。每个请求由与起点、目的地、软时间窗口和优先级相关联的取件和递送任务组成。每个运输请求也可能需要不同的 AGV 能力，考虑到电池临界阈值，AGV电池可以部分充电。决策涉及将运输和充电请求分配给 AGV，对这些请求进行排序，并确定到达时间和充电持续时间。制定了混合整数线性规划模型。提出了一种具有集成局部搜索方法的混合自适应大邻域搜索，以找到可行的时间表，旨在最小化 AGV 的请求延迟成本和旅行成本。我们通过使用真实世界数据的行业案例研究来说明混合算法的功效。计算结果表明，通过使用我们的混合算法，当前实践中的成本降低了 20%–50%，相对于文献中提出的单负载 AGV 调度方法，成本降低了约 50%。