New Benchmarks and Optimization Model for the Storage Location Assignment Problem

: 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.

• Publication year

  2022

• Venue

  International Conference on Innovative Intelligent Industrial Production and Logistics

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.5220/0011378400003329
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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