With the increasing use of virtual simulated environments and immersive technologies in STEM education and workforce training, it is becoming increasingly important to study and understand how learners’ interactions and navigation in virtual environments affect their learning and skill development. In this paper, we quantify and assess the effect of learners’ navigation in an immersive simulated environment on learning outcomes, where navigation is characterized by the total time spent in the simulation and time allocations to different areas within the virtual environment. We implement a set of immersive simulation-based learning (ISBL) modules in an undergraduate computer science course with eighteen students and record their screen as they navigate in the simulation environment to perform the tasks needed to complete the ISBL assignments. We use a video analytics tool to process and analyze
the videos and collect statistics related to a set of navigation-related measures for each student. We also use surveys to collect data on students’ demographics, prior knowledge and experience, personality, experiential learning, and self-assessment of learning. We then perform a set of multivariable regression analyses to characterize and explain
the relationship between navigation measures and constructs assessed via survey instruments to determine how/if users’ navigation in the simulated environment can be a predictor of their learning outcomes. The results indicate that the total time spent in the simulation and the distribution of time allocations among different areas within the simulated environment are predictors of experiential learning and students’ self-assessment of learning.

We introduce the visibility graph as an alternative way to estimate the length of a route traveled by order pickers in a warehouse. Heretofore it has been assumed that workers travel along a network of travel paths corresponding to centers of aisles, including along the right angles formed where picking aisles join cross aisles. A visibility graph forms travel paths that correspond to more direct and, we believe, more appropriate “travel by sight.”We compare distance estimations of the visibility graph and the aisle-centers method analytically for a common traditional warehouse design. We conduct a range of computational experiments for both traditional and fishbone warehouse layouts to assess the impact of this change in distance metric. Distance estimations using aisle-centers calculates a length of a picking tour on average 10–20% longer compared to distance estimations based on the visibility graph. The visibility graph metric also has implications for warehouse design: when comparing three traditional layouts, the distance model using a visibility graph resulted in choosing a different best layout in 13.3% of the cases.

In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin-Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150- and 200-city TSPs can increase the overall utilization of computer resources up to 25 percent compared to single thread computing. The resulting speed-up/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC3 in the literature.  We illustrate our approach with an application in the design of order picking warehouses.