Application and research of the refilling process with Clip Type Manipulator

Jin, Hui; Chen, Yun; Gao, Xiaoshan

doi:10.1109/robio.2015.7418863

Cited by 5 publications

(11 citation statements)

References 29 publications

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“…Lead time aspects are studied by Yigong (2008), who finds that OP sequence in dispensers affects picking time. Jin, Yun, and Gao (2015) find that a dispenser picking time is affected by storage container assignment. Human factors are studied by Franklin et al (2008), who investigated the effect of two dispenser types on safety and staff satisfaction.…”

Section: Picker-lessmentioning

confidence: 95%

“…Order picking sequence affects picking time (Yigong 2008), as does storage assignment policy (Jin, Yun, and Gao 2015).…”

Section: Policymentioning

confidence: 99%

“…Order of storage containers affects dispensing efficiency (Jin, Yun, and Gao 2015). Order sequencing affects picker utilisation (Boywitz, Schwerdfeger, and Boysen 2019).…”

Section: Policymentioning

confidence: 99%

“…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). Costs Costs (Boysen, Briskorn, and Emde 2017), costs (Li et al 2017) Robot-to-parts OPSs Lead time Picking time (Zhu et al 2016), picking cycle time (Boudella, Sahin, and Dallery 2018) Flexibility Easily adjusted to changes in product quantity (Kimura et al 2015) Costs Investment costs and payback period (Bonini, Urru, and Echelmeyer 2016) Parts-to-robot OPSs Throughput Throughput (Derby 2008) Lead time Cycle time (Kim et al 2003a), picking time (Khachatryan and McGinnis 2005) Flexibility Adapt robot to pick new items (Dieter Schraft and Ledermann 2003) Operational efficiency Robot travel time (Kim et al 2003b), robot utilisation (Li and Bozer 2010) Picker-less OPSs with dispensers Throughput Throughput (Liu et al 2011) Lead time Picking time (Yigong 2008) Human factorsSafety(Franklin et al 2008)Staff satisfaction(Franklin et al 2008) Quality Picking error(Franklin et al 2008) Operational efficiencyEfficiency(Franklin et al 2008) CostsOperational costs(Caputo and Pelagagge 2006;Liu et al 2011) Picker-less OPSs with A-frames Throughput Throughput (Pazour and Meller 2011) Lead time Picking time (Jin, Yun, and Gao 2015) Operational efficiency Dispensing efficiency(Jin, Yun, and Gao 2015)Picker utilisation(Boywitz, Schwerdfeger, and Boysen 2019) Costs Total restock cost(Liu et al 2008)Replenishment and picking costs(Meller and Pazour 2008)Infrastructure investment(Meller and Pazour 2008)…”

mentioning

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: Picker-lessmentioning

confidence: 95%

“…Order picking sequence affects picking time (Yigong 2008), as does storage assignment policy (Jin, Yun, and Gao 2015).…”

Section: Policymentioning

confidence: 99%

“…Order of storage containers affects dispensing efficiency (Jin, Yun, and Gao 2015). Order sequencing affects picker utilisation (Boywitz, Schwerdfeger, and Boysen 2019).…”

Section: Policymentioning

confidence: 99%

mentioning

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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“…The automatic refilling subsystem is important in refilling route, which contains two primary parts, the transmission part of the manipulator and the loading medicine manipulator. 14 The transmission part of a manipulator drives the manipulator to a specified location, and the medicine is pushed into storage space by the manipulator. To ensure that the manipulator operates with accuracy and stability, a Cartesian coordinate robot module is used to drive the loading medicine manipulator.…”

Section: Typementioning

confidence: 99%

Ant colony optimization model with characterization-based speed and multi-driver for the refilling system in hospital

Jin

Wang

Cai

et al. 2017

Advances in Mechanical Engineering

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Different optimization algorithms are used in fast medicine dispensing system to improve the efficiency of refilling. To achieve rapid replenishing of the manipulator and improve efficiency in pharmacy automation system. An ant colony optimization algorithm, which is named ant colony optimization model with characterization-based speed and multidriver, is proposed. Simulations of different picking points by order-picking mode, particle swarm optimization, particle swarm optimization with characterization-based speed and multi-driver, genetic algorithm, genetic algorithm with characterization-based speed and multi-driver, ant colony optimization, and ant colony optimization with characterization-based speed and multi-driver were carried out, and practical tests of different picking points by order-picking mode and ant colony optimization with characterization-based speed and multi-driver were done in a hospital. The data were collected and comparisons were made. Comparisons among order-picking mode, particle swarm optimization, genetic algorithm, particle swarm optimization with characterization-based speed and multi-driver, genetic algorithm with characterization-based speed and multi-driver, ant colony optimization, and ant colony optimization with characterization-based speed and multi-driver are made. Practical test results in hospital show that the ranges of optimization ratios of the average time values tested by ant colony optimization with characterization-based speed and multi-driver and the order-picking mode are 12.4%-40.3% when the picking points are between 5 and 20. Ant colony optimization with characterization-based speed and multi-driver is suitable for the refilling process in fast medicine dispensing system. It is capable of finding the refilling route which spends the shortest refilling time, and this meets the requirements in hospital.

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Optimization for medical logistics robot based on model of traveling salesman problems and vehicle routing problems

Jin

et al. 2021

International Journal of Advanced Robotic Systems

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Fast medicine dispensing system (FMDS) as a kind of medical logistic robot can dispense many drugs for one prescription at the same time. To guarantee the sustainability of drug dispensation, it is required that FMDS replenish drugs rapidly. The traditional order picking model (OPM) is difficult to meet the demand of prompt replenishment. To solve the problems of prolonged refilling route and inefficiency of drugs replenishment, a mixed refilling model based on multiple steps traveling salesman problem model (MTSPM) and vehicle routing problem model (VRPM) is proposed, and it is deployed in two circumstances of FMDS, including temporary replenishment mode (TRM) and concentrate replenishment mode (CRM). It not only meted the demand under different circumstances of drug replenishment but also shortened the refilling route significantly. First, the new pick sets were generated. Then, the orders of pick sets were optimized and the new paths were achieved. When the number of pickings is varied no more than 20, experiment results declared that the refilling route is the shortest by utilizing MTSPM when working under the TRM condition. Comparing MTSPM with OPM, the rate of refilling route length decreased up to 32.18%. Under the CRM condition, the refilling route is the shortest by utilizing VRPM. Comparing VRPM with OPM, the rate of refilling route length decreased up to 58.32%. Comparing VRPM with MTSPM, the rate of refilling route length has dropped more than 43.26%.

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Application and research of the refilling process with Clip Type Manipulator

Cited by 5 publications

References 29 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

Ant colony optimization model with characterization-based speed and multi-driver for the refilling system in hospital

Optimization for medical logistics robot based on model of traveling salesman problems and vehicle routing problems

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