Analytical models for analysis of automated warehouse material handling systems

Heragu, Sunderesh S.; Cai, Xiao; Krishnamurthy, Ananth; Malmborg, Charles J.

doi:10.1080/00207543.2010.518994

Cited by 106 publications

(66 citation statements)

References 26 publications

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“…This queueing model can handle both single-line and multiline orders, and the model is based on discrete time to better capture arrival rates from empirical data. Heragu, Cai, Krishnamurthy, and Malmborg (2011) model variants of both AVS/RS and AS/RS as Open Queueing Networks (OQN) and analyze the OQNs using a tool called the manufacturing system performance analyzer. Their conclusion is that this approach works better than simulation for rapidly evaluating different designs.…”

Section: Introductionmentioning

confidence: 99%

Estimating performance in a Robotic Mobile Fulfillment System

Tessensohn

Roy

Koster

2017

European Journal of Operational Research

204

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a b s t r a c tThis paper models Robotic Mobile Fulfillment Systems and analyzes their performance. A Robotic Mobile Fulfillment System is an automated, parts-to-picker storage system where robots bring pods with products to a workstation. It is especially suited for e-commerce distribution centers with large assortments of small products, and with strong demand fluctuations. Its most important feature is the ability to automatically sort inventory and to adapt the warehouse layout in a short period of time. Queueing network models are developed for both single-line and multi-line orders, to analytically estimate maximum order throughput, average order cycle time, and robot utilization. These models can be used to quickly evaluate different warehouse layouts, or robot zoning strategies. Two main contributions are that the models include accurate driving behavior of robots and multi-line orders. The results show that: (1) the analytical models accurately estimate robot utilization, workstation utilization, and order cycle time, (2) maximum order throughput is quite insensitive to the length-to-width ratio of the storage area and (3) maximum order throughput is affected by the location of the workstations around the storage area.

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Section: Introductionmentioning

confidence: 99%

Estimating performance in a Robotic Mobile Fulfillment System

Tessensohn

Roy

Koster

2017

European Journal of Operational Research

204

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“…Next, we first outline the queuing network based research and close this section with simulation based work. [14] create queueing networks similar to earlier queueing networks used for autonomous vehicle storage and retrieval systems (AVS/RS) and automated storage and retrieval systems (AS/RS) (see [8] and [16]). Their queueing networks capture both pick and replenishment operations but cannot model robot movement realistically.…”

Section: Related Workmentioning

confidence: 99%

Decision rules for robotic mobile fulfillment systems

Merschformann

Tessensohn

Koster

et al. 2019

Operations Research Perspectives

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The Robotic Mobile Fulfillment Systems (RMFS) is a new type of robotized, parts-to-picker material handling system, designed especially for e-commerce warehouses. Robots bring movable shelves, called pods, to workstations where inventory is put on or removed from the pods. This paper simulates both the pick and replenishment process and studies the order assignment, pod selection and pod storage assignment problems by evaluating multiple decision rules per problem. The discrete event simulation uses realistic robot movements and keeps track of every unit of inventory on every pod. We analyze seven performance measures, e.g. throughput capacity and order due time, and find that the unit throughput is strongly correlated with the other performance measures. We vary the number of robots, the number of pick stations, the number of SKUs (stock keeping units), the order size and whether returns need processing or not. The decision rules for pick order assignment have a strong impact on the unit throughput rate. This is not the case for replenishment order assignment, pod selection and pod storage. Furthermore, for warehouses with a large number of SKUs, more robots are needed for a high unit throughput rate, even if the number of pods and the dimensions of the storage area remain the same. Lastly, processing return orders only affects the unit throughput rate for warehouse with a large number of SKUs and large pick orders.

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“…For example, a general analytical model to determine the performance of AVS/RS does not exist. In addition to that a literature review shows that the influences of storage management policies are still not analyzed yet (compared to [1], [2], [3], [4], [5], [6], [7], [8]). Consequently there is great interest to get a general understanding of the influence of different factors (such as storage policies and the number of vehicles) on the performance of an AVS/RS.…”

Section: Introductionmentioning

confidence: 96%

Performance analysis of autonomous vehicle storage and retrieval systems depending on storage management policies

Kaczmarek

Goldenstein

Hompel

2014

2014 IEEE International Conference on Industrial Engineering and Engineering Management

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This paper deals with the influence of storage management policies on the performance of autonomous vehicle storage and retrieval systems (AVS/RS). Strategies like cross storage, access frequency based strategies, as well as path optimized and first in first out strategies were analyzed by using an AutoMod TM based simulation model. For each strategy the vehicle selection strategy was varied to identify existing interactions between the two sets of strategies. The physical components were not changed throughout the simulation study, except for the amount of used vehicles. The simulation results were analyzed with the aim of obtaining universally valid statements on the system behavior. The conclusion is that immense performance effects can be achieved by intelligent control of AVS/RS without changing the physical configuration. The increase in performance can be generated by several individual effects. Therefore, the design of each autonomous vehicle storage and retrieval system needs a by-case examination.

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Analytical models for analysis of automated warehouse material handling systems

Cited by 106 publications

References 26 publications

Estimating performance in a Robotic Mobile Fulfillment System

Estimating performance in a Robotic Mobile Fulfillment System

Decision rules for robotic mobile fulfillment systems

Performance analysis of autonomous vehicle storage and retrieval systems depending on storage management policies

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