No abstract

While home services is a fast growing industry, little attention has been given to the management of its workforce. In particular, the productivity of home-service technicians depends not only on efficiently routing from customer-to-customer, but also the management of their skillsets. This paper introduces a model of technician routing that explicitly models individualized, experience-based learning. The results demonstrate that explicit modeling and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored. We show that these differences result from the levels of specialization that occur in the workforce.

We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent to complete the task. The framework is learned end-toend in an unsupervised fashion addressing the perception uncertainties and decision making in an integrated manner. The framework is shown to provide more fluent coordination between human and robot partners on an example task of packaging compared to alternatives for which perception and decision-making systems are learned independently, using supervised learning. The foremost benefit of the proposed approach is that it allows for fast adaptation to new human partners and tasks since tedious annotation of motion data is avoided and the learning is performed on-line.

With the ever‐growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged quality of experience (QoE) that satisfies the end user's functional and quality‐of‐service (QoS) requirements is necessary. Software‐defined networking (SDN) and network function virtualization (NFV) are considered key enabling technologies for 5G core networks. In this regard, this paper proposes a reinforcement learning–based QoS/QoE‐aware service function chaining (SFC) scheme in SDN/NFV‐enabled 5G slices. First, it implements a lightweight QoS information collector based on the Link Layer Discovery Protocol, which works in a piggyback fashion on the southbound interface of the SDN controller, to enable QoS‐awareness. Then, a deep Q‐network–based orchestration agent is designed to support SFC in the context of NFV. The agent takes into account the QoE and QoS as key aspects to formulate the reward so that it is expected to maximize QoE while respecting QoS constraints. The experiment results show that the proposed framework exhibits good performance in QoE provisioning and QoS requirements maintenance for SFC in dynamic network environments.