A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives

The rapid development and implementation of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) in the engineering and manufacturing field have embraced a virtual identity to ensure nearly real-time adjustment. Warehouses are challenged to reassess its order fulfillment operations while simultaneously being provided with the opportunity to develop its own cloud-based CPS with the aid of IoT devices. Robotic Mobile Fulfillment System (RMFS) is a system controlling mobile robots, mobile storage rack, putaway and picking workstations, charging stations, and wireless communication infrastructure in the context of robotic-assisted warehouse. This paper addresses the value creation utilizing cloud-based CPS in RMFS. By providing an analysis of cloud services and IoT enhancement, theoretical concepts from the literatures are consolidated to solve the research que-stions on how RMFS offering better order fulfillment can gain benefits in terms of operational efficiency and system reliability. The paper also proposes a cloud-based CPS architecture, providing a comprehensive understanding on conflict avoidance strategy in the multi-layers multi-deeps warehouse layout. This research presents six conflict classifications in RMFS and provides a case study in the real-life context. Dock grid conflict is a new type of conflict appearing in multi-deeps RMFS. A scenario analysis with real customer orders is applied to present the collision detection and solution.

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Cloud-Based Cyber-Physical Robotic Mobile Fulfillment Systems: A Case Study of Collision Avoidance

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.

W. C. Tai

Papers

Smart robotic mobile fulfillment system with dynamic conflict-free strategies considering cyber-physical integration