High-performance order processing in picking workstations

Füßler, David; Boysen, Nils

doi:10.1007/s13676-017-0113-8

Cited by 23 publications

(11 citation statements)

References 20 publications

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“…-Shared between the storage aisles -More than 1 input buffer lane per station Andriansyah et al, 2011;Claeys, Adan, & Boxma, 2016;Füßler and Boysen (2017) Remote OP system with SBS/R technology  Movement system to stations: looped-conveyor system  Picking stations:…”

Section: System Features Referencesmentioning

confidence: 99%

“…End-of-aisle OP system  Storage rack shape factor (Bozer & White, 1990)  Number of aisle-captive cranes (Bozer & White, 1990;Bozer & White, 1996)  Size of input and output buffers at the end of AS/R system (Bozer & White, 1990;Park, Frazelle, & White, 1999)  Crane velocity (Bozer & White, 1990)  Storage policy (Bozer & White, 1996)  Management of retrievals (Bozer & White, 1996;Schwerdfeger & Boysen, 2017) Remote OP system with AS/R technology  Number of aisle-captive cranes (Andriansyah et al, 2011;Perry, Hoover, & Freeman, 1984;Takakuwa, 1996)  Storage aisle length (Takakuwa, 1996)  Number of picking stations (Perry, Hoover, & Freeman, 1984)  Size of input and output buffers at the end of AS/RS (Lee, Souza, & Ong, 1996)  Size of input buffer at picking stations (Alicke & Arnold, 1997)  Number of AGVs or RGVs (Lee, Souza, & Ong, 1996;Takakuwa, 1989)  Overall system layout (Takakuwa, 1996)  Storage policy (Alicke & Arnold, 1997)  RGV travel pattern (Lee, Souza, & Ong, 1996)  Retrieval sequencing (Alicke & Arnold, 1997;Takakuwa, 1996;Füßler and Boysen, 2017)  Maximum number of unit loads that a crane is allowed to retrieve (Andriansyah et al, 2011)  "Pull" logic for retrievals (Alicke & Arnold, 1997;Andriansyah et al, 2011)  Recirculation (Perry, Hoover, & Freeman, 1984) Remote OP system with SBS/R technology (this paper)…”

Section: Literature Research Stream Design Variables Managerial Policiesmentioning

confidence: 99%

“…Focusing on picking activity, Schwerdfeger and Boysen (2017) addressed the unit load switching problem in order to avoid picker idle time. Füßler and Boysen (2017) investigated the effect of synchronizing retrieved SKU arrivals at the pick stations with the active customer orders, on cumulative order throughput time.…”

Section: 1mentioning

confidence: 99%

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Integrated storage-order picking systems: Technology, performance models, and design insights

Tappia

Roy

Melacini

et al. 2019

European Journal of Operational Research

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Section: System Features Referencesmentioning

confidence: 99%

Section: Literature Research Stream Design Variables Managerial Policiesmentioning

confidence: 99%

See 1 more Smart Citation

Integrated storage-order picking systems: Technology, performance models, and design insights

Tappia

Roy

Melacini

et al. 2019

European Journal of Operational Research

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“…Claeys et al (2016) propose stochastic bounds on the order flow time in an automated parts‐to‐picker system with miniloads and remote order picking work‐stations. While this study is restricted to order picking workstation processing only one order at a time, Füßler and Boysen (2019) extend this by considering the parallel processing of a set of customer orders. Note that, all of the above‐mentioned studies consider AS/RS as the upstream storage system technology.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, low throughput times can be achieved even during peak periods. “By eliminating the unproductive work content such as picker travel time, the advanced picking workstations (also denoted as picking bays) promise a picking performance of up to 1000 order lines per hour (Füßler and Boysen (2019)).”…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of multiline orders in integrated storage‐order picking system

Bansal

Roy

2021

Naval Research Logistics

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Due to demanding service levels in e‐commerce order fulfillment, modeling and analysis of integrated storage and order picking processes in warehouses deserve special attention. The upstream storage system can have a significant impact on the performance of the downstream order picking process. With a particular focus on multiline e‐commerce orders, we develop an analytical modeling framework for integrated analysis of upstream (shuttle‐based storage and retrieval system) and downstream (pick system) networks. To capture the consolidation delays in fulfilling multiline orders, the downstream pick system is modeled with a closed queuing network that includes synchronization nodes. The configuration of the synchronization station is adapted to model the variety of order profiles handled at the pick station. For the downstream closed queuing network, we propose a decomposition‐based solution methodology that results in good solution accuracy. The resulting semi‐open queuing network (SOQN) of the integrated system is analyzed using the matrix‐geometric method (MGM). To improve the accuracy of analytical estimates of the measures, we propose a hybrid simulation/analytical framework, where the performance measures of complex subnetworks are obtained from simulation. We also develop a detailed simulation model of the physical system for validating the analytical and hybrid estimates of the performance measures. The results from experiments indicate that the hybrid simulation/analytical approach reduces the error in the throughput time estimates to 3% from 18% obtained from the analytical model. Then, we investigate the effect of the upstream network configuration (such as the number of storage aisles) and the downstream network configuration (such as the mixed vs. dedicated picking, CONWIP control for orders, order batching) on the order throughput times. Our analysis provides a threshold on the maximum numbers of allowable orders (CONWIP control) and number of aisles beyond which the improvement in average throughput time of the integrated system is marginal. Numerical experiments with high‐order arrivals also highlight that mixed picking in the downstream network can result in significant throughput time reduction in comparison to dedicated picking.

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See the light: Optimization of put‐to‐light order picking systems

Boysen

Füßler

Stephan

2020

Naval Research Logistics

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Put‐to‐light order picking systems invert the basic logic of conventional picker‐to‐parts systems. Instead of successively visiting the storage positions of the stock keeping units (SKUs) when collecting picking orders, an order picker accompanies successive bins each containing multiple items of a specific SKU along a lane of subsequent orders. Whenever the picker passes an order requiring the current SKU, which is indicated by a light signal, she puts the requested number of items into the bin associated with the order. Such an order picking system is well‐suited if the assortment is not overly large and all orders demand similar SKUs, so that it is mainly applied in distribution centers of brick‐and‐mortar retail chains. This paper evaluates four different setups of put‐to‐light systems, which, during operations, require the solution of different storage assignment and SKU sequencing problems. We formulate these problems, prove computational complexity, and suggest suited solution algorithms. By applying these algorithms in a comprehensive computational study, we benchmark the impact of the four different setups on picking performance. In this way, warehouse managers receive decision support on how to set up their put‐to‐light systems.

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High-performance order processing in picking workstations

Cited by 23 publications

References 20 publications

Integrated storage-order picking systems: Technology, performance models, and design insights

Integrated storage-order picking systems: Technology, performance models, and design insights

Stochastic modeling of multiline orders in integrated storage‐order picking system

See the light: Optimization of put‐to‐light order picking systems

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