Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations

Claeys, Dieter; Adan, Ijbf Ivo; Boxma, OJ Onno

doi:10.1016/j.ejor.2016.04.050

Cited by 18 publications

(7 citation statements)

References 27 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-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%

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

Tappia

Roy

Melacini

et al. 2019

European Journal of Operational Research

View full text Add to dashboard Cite

Section: System Features Referencesmentioning

confidence: 99%

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

Tappia

Roy

Melacini

et al. 2019

European Journal of Operational Research

View full text Add to dashboard Cite

“…Several other studies use the simulation modeling approach for analyzing integrated AS/RS with remote order picking (Andriansyah et al, 2010, 2011; Manzini et al, 2006; Perry et al, 1984; Raghunath et al, 1986). 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.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Bansal

Roy

2021

Naval Research Logistics

View full text Add to dashboard Cite

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.

show abstract

“…They analyze the performance of end-of-aisle order picking systems assuming items are randomly stored in the rack. Following the research of Bozer and White (1990), several papers study the performance of an AS/ R system with end-of-aisle or remote order picking stations by adopting the three widely studied storage strategies into the system: random storage (Foley and Frazelle, 1991;Claeys et al, 2016;Tappia et al, 2019), full turnover-based storage (Park et al, 2003) and class-based storage (Park et al, 2006), or assuming the travel time of the crane follows a general distribution, so that the results can be applied for all the three storage strategies (Park et al, 1999;Koh et al, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

Forward-reserve storage strategies with order picking: When do they pay off?

Koster

2020

IISE Transactions

View full text Add to dashboard Cite

Customer order response time and system throughput capacity are key performance measures in warehouses. They depend strongly on the storage strategies deployed. One popular strategy is to split inventory into a bulk storage and a pick stock, or Forward-Reserve (FR) storage. Managers often use a rule of thumb: when the ratio m of average picks per replenishment is larger than a certain factor, it is beneficial to split inventory. However, research that systematically quantifies the benefits is lacking. We quantify the benefits analytically by developing response travel time models for FR storage in an Automated Storage/Retrieval system combined with order picking. We compare performance of FR storage with turnover class-based storage, and find when it pays off. Our findings illustrate that, in FR storage systems where forward and reserve stocks are stored in the same rack, FR storage usually pays off, as long as m is sufficiently larger than 1. The response time savings can go up to 50% when m is larger than 10. We validate these results using real data from a wholesale distributor.

show abstract

Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations

Cited by 18 publications

References 27 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

Forward-reserve storage strategies with order picking: When do they pay off?

Contact Info

Product

Resources

About