In order to increase the order picking efficiency, e-commerce retailers have started to implement order picking systems where mobile robots carry inventory pods to pick stations. In pick stations, pickers pick the products from inventory pods and put them in customer bins. In such a robotic mobile fulfillment center, pickers are constantly busy with picking customer orders and avoid non-value adding activities such as walking to reach storage locations. To fulfill customer orders, each robot needs to complete a sequence of missions and each mission includes a set of retrieval requests. We study the operational problem of scheduling a mobile robot fulfilling a set of customer orders from a pick station. The mobile robot needs to bring each pod from a retrieval location to the pick station and return the pod to a storage location. The objective is to minimize the total travel time of the robot which can be considered as a proxy for other objectives such a shorter lead time, higher throughput and less capital investment. We formulate the basic problem as an asymmetric traveling salesman problem. We then extend the model by adding general precedence constraints to give different priorities to customer orders based on their urgency (e.g. same-day, one-day, two-day, and standard orders). We also study a variation of the problem where the pod can be stored in multiple alternative locations. In this case, we model the problem as a generalized asymmetric traveling salesman problem. An adaptive large neighborhood search heuristic is developed to efficiently solve real size instances. The method outperforms the heuristics commonly used in practice.

为了提高订单拣选效率，电子商务零售商已开始实施订单拣选系统，其中移动机器人将库存吊舱运送到拣选站。在拣货站，拣货员从库存吊舱中挑选产品，然后将其放入客户垃圾箱。在这样的机器人移动履行中心中，拣选人员一直忙于拣选客户订单，并避免非增值活动，例如步行到达仓库地点。为了满足客户订单，每个机器人都需要完成一系列任务，并且每个任务都包含一组检索请求。我们研究了调度从拣选站执行一组客户订单的移动机器人的操作问题。移动机器人需要将每个吊舱从取回位置带到拣选站，然后将吊舱返回到存储位置。目的是最大程度地减少机器人的总行程时间，可以将其视为替代其他目标的对象，例如缩短交货时间，提高吞吐量和减少资本投入。我们将基本问题表述为非对称旅行商问题。然后，我们通过添加一般优先级约束来扩展模型，以根据客户订单的紧急程度（例如当天，一天，两天和标准订单）为客户订单赋予不同的优先级。我们还研究了豆荚可以存储在多个替代位置的问题的变体。在这种情况下，我们将问题建模为广义的非对称旅行商问题。开发了一种自适应的大邻域搜索试探法，以有效地解决实际大小的实例。该方法优于实践中常用的启发式方法。