Regular Papers: Artificial IntelligenceNo Access

Design and Performance Estimation of Mixed-Robotic Fulfillment System

Cheng Chi

https://orcid.org/0000-0003-3522-0364

School of Control Science and Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, China

E-mail Address: chicheng2009cc@163.com

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Shasha Wu

School of Control Science and Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, China

E-mail Address: wushasha@mail.sdu.edu.cn

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Luyao Wang

School of Control Science and Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, China

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, and

Yaohua Wu

School of Control Science and Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, China

E-mail Address: yaohua.wu@sdu.edu.cn

Corresponding author.

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https://doi.org/10.1142/S0218001421590576Cited by:2 (Source: Crossref)

This article is part of the issue:

Special Section on the 15th International Conference on Computer Vision Theory and Applications (VISAPP 2020)
Guest Editors: Petia Radeva and Giovanni Maria Farinella

Abstract

E-commerce retailers face the challenge to assemble a large number of time-critical picking orders. Common parts-to-picker autonomous intelligent warehouses such as automated vehicle storage and retrieval system and robotic mobile fulfillment system are often a little ill-suited for these prerequisites. A mixed-robotic fulfillment system is a hybrid robot picking system based on multi-device collaboration. It is a fusion innovation of traditional automated vehicle storage and retrieval system and robotic mobile fulfillment system. This paper comprehensively considers the characteristics of the system and customer demand, through the construction of a queuing network model to evaluate the performance of the system. A series of problems such as order service time, throughput capacity, and vehicle quantity configuration are analyzed experimentally. The validity of the model is verified by a simulation model.

Keywords:

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