A simulation modeler's view of a carousel
WSC '95 Proceedings of the 27th conference on Winter simulation
Generalization of an AS/RS model in SIMAN/CIMENA
WSC '93 Proceedings of the 25th conference on Winter simulation
ORDER PICKING IN CAROUSEL SYSTEMS UNDER THE NEAREST ITEM HEURISTIC
Probability in the Engineering and Informational Sciences
PICKING CLUMPY ORDERS ON A CAROUSEL
Probability in the Engineering and Informational Sciences
An Alternating Service Problem
Probability in the Engineering and Informational Sciences
Optimal picking of large orders in carousel systems
Operations Research Letters
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Carousel conveyors (also known simply as 芒聙聹carousels芒聙聺) are an important and well-known type of material handling system used in warehousing and manufacturing to store/retrieve small-to-medium-sized parts. A traditional multi-tier (or multi-level) carousel, which is powered by a single drive, rotates as a whole unit, and brings the items to the input/output point one at a time, since each level cannot be rotated independently. A special type of carousel (also known as a 芒聙聹rotary rack芒聙聺) uses a dedicated drive for each level of the carousel. This allows each level of the carousel to rotate independently of and concurrently with the other levels. It also allows a rotary rack to support multiple pickers (or robots) while a traditional carousel can support typically only one picker. Although rotary racks have been available for quite some time, questions about their performance and efficient use remain largely unanswered, especially in comparison with traditional carousels, which have been the subject of numerous studies. Assuming one or more picking robots, in this paper we use a simulation model to develop and analyze four retrieval strategies for a rotary rack. We compare the performance of the four strategies as a function of the input data and the number of robots in the system. We also address the question of the appropriate number of robots. As one would expect, the simulation results indicate that the throughput of the system increases with the number of robots. However, more robots also lead to more idle time per robot. We show the tradeoff between the expected time required to process an order and the expected idle time per robot.