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New benchmarks and optimization model for the Storage Location Assignment Problem

Oxenstierna, Johan LU orcid ; Malec, Jacek LU orcid and Krueger, Volker LU orcid (2022) 3rd International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2022
Abstract
The Storage Location Assignment Problem (SLAP) is of primary significance to warehouse operations since
the cost of order-picking is strongly related to where and how far vehicles have to travel. Unfortunately, a
generalized model of the SLAP, including various warehouse layouts, order-picking methodologies and
constraints, poses a highly intractable problem. Proposed optimization methods for the SLAP tend to be
designed for specific scenarios and there exists no standard benchmark dataset format. We propose new SLAP
benchmark instances on a TSPLIB format and show how they can be efficiently optimized using an Order
Batching Problem (OBP) optimizer, Single Batch Iterated (SBI), with a Quadratic Assignment... (More)
The Storage Location Assignment Problem (SLAP) is of primary significance to warehouse operations since
the cost of order-picking is strongly related to where and how far vehicles have to travel. Unfortunately, a
generalized model of the SLAP, including various warehouse layouts, order-picking methodologies and
constraints, poses a highly intractable problem. Proposed optimization methods for the SLAP tend to be
designed for specific scenarios and there exists no standard benchmark dataset format. We propose new SLAP
benchmark instances on a TSPLIB format and show how they can be efficiently optimized using an Order
Batching Problem (OBP) optimizer, Single Batch Iterated (SBI), with a Quadratic Assignment Problem
(QAP) surrogate model (QAP-SBI). In experiments we find that the QAP surrogate model demonstrates a
sufficiently strong predictive power while being 50-122 times faster than SBI. We conclude that a QAP
surrogate model can be successfully utilized to increase computational efficiency. Further work is needed to
tune hyperparameters in QAP-SBI and to incorporate capability to handle more SLAP scenarios. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics
editor
Panetto, Hervé ; Weichhart, Georg ; Smirnov, Alexander and Madani, Kurosh
publisher
SciTePress
conference name
3rd International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2022
conference location
Valetta, Malta
conference dates
2022-10-24 - 2022-10-26
external identifiers
  • scopus:85143088103
ISBN
978-989-758-612-5
DOI
10.5220/0011378400003329
project
RobotLab LTH
language
English
LU publication?
yes
id
2f9ce756-dfec-4f57-a235-68aa17500aee
date added to LUP
2022-09-21 02:29:59
date last changed
2023-11-21 11:32:20
@inproceedings{2f9ce756-dfec-4f57-a235-68aa17500aee,
  abstract     = {{The Storage Location Assignment Problem (SLAP) is of primary significance to warehouse operations since<br/>the cost of order-picking is strongly related to where and how far vehicles have to travel. Unfortunately, a<br/>generalized model of the SLAP, including various warehouse layouts, order-picking methodologies and<br/>constraints, poses a highly intractable problem. Proposed optimization methods for the SLAP tend to be<br/>designed for specific scenarios and there exists no standard benchmark dataset format. We propose new SLAP<br/>benchmark instances on a TSPLIB format and show how they can be efficiently optimized using an Order<br/>Batching Problem (OBP) optimizer, Single Batch Iterated (SBI), with a Quadratic Assignment Problem<br/>(QAP) surrogate model (QAP-SBI). In experiments we find that the QAP surrogate model demonstrates a<br/>sufficiently strong predictive power while being 50-122 times faster than SBI. We conclude that a QAP<br/>surrogate model can be successfully utilized to increase computational efficiency. Further work is needed to<br/>tune hyperparameters in QAP-SBI and to incorporate capability to handle more SLAP scenarios.}},
  author       = {{Oxenstierna, Johan and Malec, Jacek and Krueger, Volker}},
  booktitle    = {{Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics}},
  editor       = {{Panetto, Hervé and Weichhart, Georg and Smirnov, Alexander and Madani, Kurosh}},
  isbn         = {{978-989-758-612-5}},
  language     = {{eng}},
  publisher    = {{SciTePress}},
  title        = {{New benchmarks and optimization model for the Storage Location Assignment Problem}},
  url          = {{http://dx.doi.org/10.5220/0011378400003329}},
  doi          = {{10.5220/0011378400003329}},
  year         = {{2022}},
}