New Benchmarks and Optimization Model for the Storage Location Assignment Problem

Johan Oxenstierna, Johan Oxenstierna, Jacek Malec, Volker Krueger

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 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.

Paper Citation

in Harvard Style

Oxenstierna J., Malec J. and Krueger V. (2022). New Benchmarks and Optimization Model for the Storage Location Assignment Problem. In Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL, ISBN 978-989-758-612-5, pages 26-35. DOI: 10.5220/0011378400003329

in Bibtex Style

@conference{in4pl22,
author={Johan Oxenstierna and Jacek Malec and Volker Krueger},
title={New Benchmarks and Optimization Model for the Storage Location Assignment Problem},
booktitle={Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL,},
year={2022},
pages={26-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011378400003329},
isbn={978-989-758-612-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL,
TI - New Benchmarks and Optimization Model for the Storage Location Assignment Problem
SN - 978-989-758-612-5
AU - Oxenstierna J.
AU - Malec J.
AU - Krueger V.
PY - 2022
SP - 26
EP - 35
DO - 10.5220/0011378400003329