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Order batching and order picking with 3D positioning of the articles: solution through a hybrid evolutionary

Fabio M Miguel1, Mariano Frutos2, Máximo Méndez3

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Summary

This study optimizes distribution center operations by jointly addressing order batching and picking problems in 3D environments. The goal is to minimize operational costs and improve delivery efficiency.

Keywords:
evolutionary algorithmlocal searchoptimizationorder batchingorder picking

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Area of Science:

  • Operations Research
  • Logistics Management
  • Supply Chain Optimization

Background:

  • Distribution center efficiency is crucial for logistic management.
  • The pick-up process is the most expensive operation, accounting for 50-75% of total storage facility costs.
  • Previous research addressed 2D instances, but 3D instances present higher complexity.

Purpose of the Study:

  • To jointly address the order batching problem (OBP) and the order picking problem (OPP).
  • To minimize total operational costs in distribution center pick-up processes.
  • To improve the performance of hybrid evolutionary algorithms (HEAs) for 3D instances.

Main Methods:

  • Developing a plan that integrates order batching and order picking strategies.
  • Utilizing a hybrid evolutionary algorithm (HEA) to solve complex 3D instances.
  • Minimizing costs associated with displacement, pick-up times, and order fulfillment.

Main Results:

  • Successfully applied HEA to complex 3D instances, improving upon previous 2D results.
  • Demonstrated a method to reduce operational costs in distribution centers.
  • Enhanced efficiency in pick-up processes through optimized batching and sequencing.

Conclusions:

  • The joint optimization of OBP and OPP in 3D environments is feasible and effective.
  • The enhanced HEA provides a robust solution for complex distribution center logistics.
  • This research contributes to more efficient and cost-effective warehouse operations.