Non-traditional layouts decrease the average travel distance for unit-load operations compared to traditional warehouse (i.e, straight rows with pick aisles and perpendicular cross aisles that reduce the travel distance between pick locations). Gue and Meller (2009) propose the fishbone layout that decreases average travel distance up to 20%. Ozturkoglu et. al. (2012) relax the assumption of Gue and Meller (2009) so that the pick aisles can take any angle, and achieve up to 22% improvement over traditional layout. Moreover, they prove that these designs are optimal for unit-load operations. However, the optimal warehouse designs for order picking operations are unknown. Here we describe an approach that optimizes layouts with up to 4% improvement over traditional layouts. For small pick list sizes, because of the higher importance of depot location in travel cost, near optimal layouts are creating a vertical cross aisles. As the pick list size increases these cross aisles become horizontal allowing better access between storage locations. We anticipate our approach to be a starting point for more detailed research for warehouse layout optimization. For example, analytical models can be created for the near-optimal layouts we present. Furthermore, layout optimization is a major target for retail industry, and a well-defined encoding and routing approach will be relevant for such optimizations.
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.
This paper presents novel immersive simulation-based learning (I-SBL) modules as an alternative active-learning method for teaching and learning fundamental concepts related to database de-sign. I-SBL involves a 3-dimensional simulated environment that resembles a real-world system.Students can navigate through the simulated environment (in low- and high-immersion modes),observe and understand the underlying dynamics, evaluate the effect of the changes on the system,and learn by doing. The use of such modules is especially important when access to the real system is limited or impossible due to geographical barriers and/or regulations and safety considerations.We assess the impact of the proposed approach by implementing a sample I-SBL module in an undergraduate database class. The study involves two groups of students: control and test groups.Students in the control group complete a traditional problem-based learning (PBL) assignment,while the test group uses the I-SBL version of the same assignment. The assessment data collected include demographics, prior preparation, motivation, usability tests, and pre/post quizzes to mea-sure knowledge gain. Statistical analysis of the results suggests that I-SBL performs at least as wellas PBL. The results also provide important insights into the effective design and implementation of I-SBL.
The problem of improving the environmental performance of a supply chain without entailing excessive cost is becoming a frequent problem as
companies face an increasing pressure from governments and customers for reducing the environmental impact of their activities. As the environmental improvement of an operating supply chain implies not only technology upgrading decisions, but also decisions regarding the structure of the supply chain itself; deciding on what strategy to follow is a complex task. The aim of this work is to provide a bi- objective solution approach for finding such strategy so that both the environmental and financial goals are best met.
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.