Joint green dynamic order batching and picker routing problem using PSO with global worst experience

Chun Cheng Lin, Yi Chun Peng, Jia Rong Kang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


With the increasing importance of various countries’ initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically; and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm's previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing.

Original languageEnglish
Article number111336
JournalApplied Soft Computing
StatePublished - Mar 2024


  • Carbon footprint
  • Dynamic arriving order picking
  • Green warehousing
  • Particle swarm optimization
  • Vehicle routing problem


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