1,096
Views
15
CrossRef citations to date
0
Altmetric
Articles

Robotic mobile fulfilment systems considering customer classes

ORCID Icon, ORCID Icon & ORCID Icon
Pages 5032-5049 | Received 05 Sep 2019, Accepted 31 May 2020, Published online: 22 Jun 2020
 

Abstract

This paper studies a Robotic Mobile Fulfilment System (RMFS), featured by a number of robots lifting and transporting movables storage shelves from storage grids to order pickers. In such systems, online retailers often classify their customers by two major classes ‘expedited shipping’ and ‘standard shipping’. We build high-dimension Markov models to describe this system with customer classes, calculate the throughput of this system given the number of robots and provide design rules to determine the optimal number of robots and their capacities considering the trade-off between capacities of picker stations and robots. We verify the analytic results of Markov models with simulations. We further consider multiple-picker RMFS and study its optimal design.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported in part by the National Natural Science Foundation of China (No. 71620107002), the National Social Science Foundation of China (16ZDA013), and Erasmus+ [grant number 2019-1-FR01-KA203-063063]. Yeming GONG issupported by Business Intelligence Center of EMLYON.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 973.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.