Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

Current barriers and possible solutions to effective project team formation and deployment within a large construction organisation

The characteristics of the construction industry present an extremely challenging context for effective human resource management (HRM). The dynamic project-based nature of the industry results in extreme fluctuations in organisations’ workloads and requires teams to form, develop and disband relatively quickly. Thus, the importance of efficient management of employee resourcing activities cannot be understated. This paper reports on the findings of research which explored employee resourcing practices within large UK construction firms. The results suggest that managers currently attempt to carry out some strategic planning with regards to employee resourcing, but that this does not necessarily translate into effective operational practice which simultaneously takes account of organisational, project and individual employee needs. A new approach for more effective employee resourcing decision-making, based on encouraging the involvement of the employees in the deployment process, is put forward as a management tool which informs effective team formation and deployment. However, this will require the acceptance of both decision-support technology and of employee input into what is currently a tacit, management-oriented decision process

Understanding employee resourcing in construction organizations

In recent years the literature on employee resourcing has consistently advocated the importance of adopting a holistic, strategic approach to employee deployment decision making rather than adopting a reactive needs-based approach. This is particularly problematic in construction where the multi-project environment leads to constantly changing resource requirements and to changing demands over a project's life cycle. This can lead to inappropriate decisions, which fail to meet the longer-term needs of both construction organizations and their employees. A structured and comprehensive understanding of the current project team deployment practices within large construction organizations was developed. Project deployment practices were examined within seven case study contracting firms. The emergent themes that shaped the decision-making processes were grouped into five broad clusters comprising human resource planning, performance/career management, team deployment, employee involvement and training and development. The research confirms that a reactive and ad hoc approach to the function prevails within the firms investigated. This suggests a weak relationship between the deployment process and human resource planning, team deployment, performance management, employee involvement and training and development activities. It is suggested that strategic HR-business partnering could engender more transparent and productive relationships in this crucial area

Learning Dynamic Robot-to-Human Object Handover from Human Feedback

Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.Comment: Appears in the Proceedings of the International Symposium on Robotics Research (ISRR) 201