Authors:
Johan Oxenstierna
1
;
2
;
Jacek Malec
1
and
Volker Krueger
1
Affiliations:
1
Dept. of Computer Science, Lund University, Lund, Sweden
;
2
Kairos Logic AB, Lund, Sweden
Keyword(s):
Storage Location Assignment Problem, Order Batching Problem, Quadratic Assignment Problem, Warehousing, Computational Efficiency.
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 ef
ficiency. Further work is needed to tune hyperparameters in QAP-SBI and to incorporate capability to handle more SLAP scenarios.
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