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  1. Humans assume different production roles in a workspace. On one hand, humans design workplans to complete tasks as efficiently as possible in order to improve productivity. On the other hand, a nice workspace is essential to facilitate teamwork. In this way, workspace design and workplan design complement each other. Inspired by such observations, we propose an automatic approach to jointly design a workspace and a workplan. Taking staff properties, a space, and work equipment as input, our approach jointly optimizes a workspace and a workplan, considering performance factors such as time efficiency and congestion avoidance, as well as workload factors such as walk effort, turn effort, and workload balances. To enable exploration of design trade-offs, our approach generates a set of Pareto-optimal design solutions with strengths on different objectives, which can be adopted for different work scenarios. We apply our approach to synthesize workspaces and workplans for different workplaces such as a fast food kitchen and a supermarket. We also extend our approach to incorporate other common work considerations such as dynamic work demands and accommodating staff members with different physical capabilities. Evaluation experiments with simulations validate the efficacy of our approach for synthesizing effective workspaces and workplans. 
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  2. Abstract

    Toward enhancing automation, this paper proposes an efficient approach for multi‐group motion planning, where the set of goals is divided intokgroups and the objective is to compute a collision‐free and dynamically feasible trajectory that enables a virtual vehicle to reach at least one goal from each group. The approach works with ground and aerial vehicles operating in complex environments containing numerous obstacles. In addition to modeling the vehicle dynamics by differential equations, the approach can use physics‐based game engines, which provide an increased level of realism. The approach is based on a hybrid search that uses generalized traveling salesman tours over a probabilistic roadmap to effectively guide the sampling‐based expansion of a motion tree. As the motion tree is expanded with collision‐free and dynamically feasible trajectories, tours are adjusted based on a partition of the motion tree into equivalence classes. This gives the approach the flexibility to discover new tours that avoid collisions and are compatible with the vehicle dynamics. Comparisons to related work show significant improvements both in terms of runtime and solution length. Copyright © 2016 John Wiley & Sons, Ltd.

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