Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems

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Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems. / Xie, Lin; Thieme, Nils ; Krenzler, Ruslan; Li, Hanyi.

in: European Journal of Operational Research , Jahrgang 288, Nr. 1, 01.01.2021, S. 80-97.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{799eabaf919445d898b02e6fdca9a3b9,
title = "Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems",
abstract = "In robotic mobile fulfillment systems, human pickers don{\textquoteright}t go to the inventory area to search for and pick the ordered items. Instead, robots carry shelves (called “pods”) containing ordered items from the inventory area to picking stations. At the picking stations, pickers put ordered items into totes; then these items are transported to the packing stations. This type of warehousing system relieves the human pickers and improves the picking process. In this paper, we concentrate on decisions about the assignment of pods to stations and orders to stations to fulfill picking for each incoming customer{\textquoteright}s order. In previous research for an RMFS with multiple picking stations, these decisions are made sequentially with heuristics. Instead, we present a new MIP-model to integrate both decision problems. To improve the system performance even more, we extend our model by splitting orders. This means parts of an order are allowed to be picked at different stations. To the best of the authors{\textquoteright} knowledge, this is the first publication on split orders in an RMFS. And we prove the computational complexity of our models. We analyze different performance metrics, such as pile-on, pod-station visits, robot moving distance and throughput. We compare the results of our models in different instances with the sequential method in our open-source simulation framework RAWSim-O. The integration of the decisions brings better performances, and allowing split orders further improves the performances (for example: increasing throughput by 46%). In order to reduce the computational time for a real-world application, we have proposed a heuristic.",
keywords = "Business informatics, logistics, MIP models, Integrated operational optimization, Robotic mobile fulfillment systems, Split orders",
author = "Lin Xie and Nils Thieme and Ruslan Krenzler and Hanyi Li",
year = "2021",
month = jan,
day = "1",
doi = "10.1016/j.ejor.2020.05.032",
language = "English",
volume = "288",
pages = "80--97",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems

AU - Xie, Lin

AU - Thieme, Nils

AU - Krenzler, Ruslan

AU - Li, Hanyi

PY - 2021/1/1

Y1 - 2021/1/1

N2 - In robotic mobile fulfillment systems, human pickers don’t go to the inventory area to search for and pick the ordered items. Instead, robots carry shelves (called “pods”) containing ordered items from the inventory area to picking stations. At the picking stations, pickers put ordered items into totes; then these items are transported to the packing stations. This type of warehousing system relieves the human pickers and improves the picking process. In this paper, we concentrate on decisions about the assignment of pods to stations and orders to stations to fulfill picking for each incoming customer’s order. In previous research for an RMFS with multiple picking stations, these decisions are made sequentially with heuristics. Instead, we present a new MIP-model to integrate both decision problems. To improve the system performance even more, we extend our model by splitting orders. This means parts of an order are allowed to be picked at different stations. To the best of the authors’ knowledge, this is the first publication on split orders in an RMFS. And we prove the computational complexity of our models. We analyze different performance metrics, such as pile-on, pod-station visits, robot moving distance and throughput. We compare the results of our models in different instances with the sequential method in our open-source simulation framework RAWSim-O. The integration of the decisions brings better performances, and allowing split orders further improves the performances (for example: increasing throughput by 46%). In order to reduce the computational time for a real-world application, we have proposed a heuristic.

AB - In robotic mobile fulfillment systems, human pickers don’t go to the inventory area to search for and pick the ordered items. Instead, robots carry shelves (called “pods”) containing ordered items from the inventory area to picking stations. At the picking stations, pickers put ordered items into totes; then these items are transported to the packing stations. This type of warehousing system relieves the human pickers and improves the picking process. In this paper, we concentrate on decisions about the assignment of pods to stations and orders to stations to fulfill picking for each incoming customer’s order. In previous research for an RMFS with multiple picking stations, these decisions are made sequentially with heuristics. Instead, we present a new MIP-model to integrate both decision problems. To improve the system performance even more, we extend our model by splitting orders. This means parts of an order are allowed to be picked at different stations. To the best of the authors’ knowledge, this is the first publication on split orders in an RMFS. And we prove the computational complexity of our models. We analyze different performance metrics, such as pile-on, pod-station visits, robot moving distance and throughput. We compare the results of our models in different instances with the sequential method in our open-source simulation framework RAWSim-O. The integration of the decisions brings better performances, and allowing split orders further improves the performances (for example: increasing throughput by 46%). In order to reduce the computational time for a real-world application, we have proposed a heuristic.

KW - Business informatics

KW - logistics

KW - MIP models

KW - Integrated operational optimization

KW - Robotic mobile fulfillment systems

KW - Split orders

U2 - 10.1016/j.ejor.2020.05.032

DO - 10.1016/j.ejor.2020.05.032

M3 - Journal articles

VL - 288

SP - 80

EP - 97

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -

DOI