Analytical models for an automated storage and retrieval system with multiple in-the-aisle pick positions

Ramtin, Faraz; Pazour, Jennifer A.

doi:10.1080/0740817x.2014.882037

Cited by 27 publications

(14 citation statements)

References 41 publications

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“…In a later study, Su et al (2009) study the effect of machine routing on the distance travelled by the machine. Ramtin and Pazour (2014) compare different storage policies in an AS/RS and investigate their effect on machine travel time. Khojasteh and Jae-Dong (2016) develop a heuristic to minimise machine travel time in an AS/RS.…”

Section: Layoutmentioning

confidence: 99%

“…;Ramtin and Pazour 2014;Khojasteh and Jae-Dong 2016), picker and machine idle time(Wu and Mulgund 1998) Parts-to-picker systems with vertical lift modules (VLMs) ThroughputThroughput(Bauters et al 2011;Battini et al 2015;Lenoble, Frein, and Hammami 2016;Sgarbossa, Calzavara, and Persona 2019) Lead timeOrder picking time (Lenoble, Frein, and Hammami 2018) Human factorsErgonomics(Dukic et al 2018) Operational efficiency Space utilisation(Dukic et al 2018) Parts-to-picker systems with conveyors Throughput Throughput(Andriansyah et al 2014) Lead timeOrder processing time(Armstrong, Cook, and Saipe 1979), order flow time(Andriansyah, Etman, and Rooda 2009), order fulfilment time (Wu et al 2017) Operational efficiency Picking efficiency (Wu and Wu 2014; Liu et al 2015) Parts-to-picker systems with carousels Throughput Throughput (Park, Park, and Foley 2003; Park and Rhee 2005) Lead time Job sojourn time (Park and Rhee 2005), order picking time (Yanyan, Shandong, and Changpeng 2014; Lenoble, Frein, and Hammami 2017), retrieval time (Chang, Wen, and Lin 1993) Operational efficiency Machine travel time (Litvak and Adan 2001), picker utilisation (B. Park, Park, and Foley 2003) Costs Picking cost (Lee and Kuo 2008) Robotic parts-to-picker OPSs Throughput Throughput (Bauters et al 2016; Lamballais, Roy, and De Koster 2017; Lamballais Tessensohn, Roy, and De Koster 2020; Roy et al 2019) Lead time Average order cycle time (Ekren and Heragu 2010; Lamballais, Roy, and De Koster 2017), throughput time (Yuan and Gong 2017; Roy et al 2019), picking time (Xue, Dong, and Qi 2018; Zou, Xu, and De Koster 2018) Human factors Ergonomics (Lee, Chang, and Choe 2017; Hanson, Medbo, and Johansson 2018), operator training (Hanson, Medbo, and Johansson 2018) Quality Picking accuracy (Hanson, Medbo, and Johansson 2018) Flexibility Flexibility (Hanson, Medbo, and Johansson 2018) Operational efficiency Robot utilisation (Lamballais, Roy, and De Koster 2017), uptime (Hanson, Medbo, and Johansson 2018), collision-free paths (Kumar and Kumar 2018), waiting times for vehicles (Ekren and Heragu 2010), average utilisation of vehicles and lifts (Ekren and Heragu 2010), effeciency (Zhao et al 2019), picker and robot utilisation (Wang, Chen, and Wang 2019), robot travel time (Wang, Yang, and Li 2019).…”

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confidence: 99%

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Automated order picking systems and the links between design and performance: a systematic literature review

Jaghbeer

Hanson

Johansson

2020

International Journal of Production Research

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With new market developments and e-commerce, there is an increased use of and interest in automation for order picking. This paper presents a systematic review and content analysis of the literature. It has the purpose of understanding the relevant performance aspects for automated, or partly automated, OPSs and identifying the studied links between design and performance, i.e. identifying which combinations of design aspects and performance aspects have been studied in previous research. For this purpose, 74 papers were selected and reviewed. From the review, it is clear that there has been an increased number of papers dealing with the performance of automated, or partly automated, OPSs in recent years. Moreover, there are differences between the different OPS types, but, overall, the performance categories of throughput, lead time, and operational efficiency have received the most attention in the literature. The paper identifies links between design and performance that have been studied, as well as links that appear to be under-researched. For academics, this paper synthesises the current knowledge on the performance of automation in OPSs and identifies opportunities for future research. For practitioners, the paper provides knowledge that can support the decision-making process of automation in OPSs.

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Section: Layoutmentioning

confidence: 99%

mentioning

confidence: 99%

Automated order picking systems and the links between design and performance: a systematic literature review

Jaghbeer

Hanson

Johansson

2020

International Journal of Production Research

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“…Kouloughli and Sari (2015) developed analytical AS/RS cycle time models to optimize a three dimension multi-aisle AS/RS in which the AS/R machine can travel among different aisles. Ramtin and Pazour (2014) were the first to develop an analytical model for the MIAPP-AS/RS operation. They calculated the throughput performance and the length-to-height configuration of an AS/RS aisle to minimize the expected travel time.…”

Section: Chebyshev Metricmentioning

confidence: 99%

Stochastic analysis of an automated storage and retrieval system with multiple in‐the‐aisle pick positions

Liu

Liao

White

2020

Naval Research Logistics

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An automated storage and retrieval system with multiple in-the-aisle pick positions (MIAPP-AS/RS) is often used to support case-picking occurring at floor and mezzanine levels. In this paper, the replenishment process is modeled as an M/G/1/N/N queueing problem in which the aisle-captive AS/R machine is the server and pick positions requiring replenishments are customers. Four scenarios are considered: finite population with dedicated storage, finite population with random storage, infinite population with dedicated storage and infinite population with random storage. The corresponding probability density functions (PDFs) for travel time are derived using a Chebyshev travel metric. For the finite population case, PDFs are derived using a novel method based on contour lines; the contour-line-based method can be used to derive PDFs for similar problems. From queueing and simulation results, the robustness of the M/G/1/N/N queueing model is demonstrated, allowing it to be applied in analyzing an MIAPP-AS/RS case-picking operation when interarrival times of replenishment requests are not exponentially distributed and heterogeneous customers are present. Dedicated-and random-storage policies for reserve storage are compared and conditions favoring each policy are provided. A case study demonstrates how queueing results can be applied in designing an MIAPP-AS/RS.

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“…Previously used picked products have to be returned to be stored again in the bulk storage system (step two). This problem has been studied by some researchers, but only in combination with manual pick processes (Yu and De Koster 2010, Ramtin and Pazour 2014, 2015, Schwerdfeger and Boysen 2017.…”

Section: Directions For Future Researchmentioning

confidence: 99%

Robotized Warehouse Systems: Developments and Research Opportunities

2017

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Robotized handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of robotized handling systems, such as the shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and OR modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Due to unique system features (such as autonomous control, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotized warehouse systems will form the next category of warehouses. All vital warehouse design, planning and control logic such as methods to design layout, storage and order picking system selection, storage slotting, order batching, picker routing, and picker to order assignment will have to be revisited for new robotized warehouses. 1 Introduction Warehouse operations tend to be labor intensive and require large space for facilities. Large buildings are needed to store the item assortment in racks, to move stock, to unload and load 1 trailers and containers, to inspect picked orders, and to allow trucks to maneuver in the yard and to dock them. With the advent of e-commerce, companies store millions of unique items and handle large and variable daily order volumes. On the other hand, the most laborious and expensive process, order picking, is repetitive, often suffers from poor ergonomics, and requires high-quality labor willing to work in shifts, which is often difficult to get. It is therefore not surprising that warehousing systems and processes are key candidates for automation. In addition, the land available for warehouses (which should preferably be close to the demand points) has become scarce, and many warehouses have to operate 24/7. Together, this has given warehouse automation a big boost. Warehouse automation dates back to the 1960s, when the first high-bay (20-40 m high was quite standard) unit-load warehouses were established in Germany with aisle-captive cranes driving on rails, constructed as a silo building (Industrieforum 2004). These so-called AS/R (automated storage and retrieval) systems were able to store bulk stock on unit loads (pallets, or totes: miniload system). They could also work in conjunction with manual pick stations as a parts-to-picker system, where the retrieved unit load was restored after picking units from it. Since then, AS/R systems have become very popular in practice, and research ...

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Analytical models for an automated storage and retrieval system with multiple in-the-aisle pick positions

Cited by 27 publications

References 41 publications

Automated order picking systems and the links between design and performance: a systematic literature review

Automated order picking systems and the links between design and performance: a systematic literature review

Stochastic analysis of an automated storage and retrieval system with multiple in‐the‐aisle pick positions

Robotized Warehouse Systems: Developments and Research Opportunities

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