How to optimize container withholding decisions for reuse in the hinterland?

Benjamin Legros*, Jan Fransoo, Oualid Jouini

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

This study investigates how a hinterland consignee (importer) makes decisions regarding the storage of empty containers for reuse by a shipper (exporter). The system is modeled as a double-ended queue with non-zero matching times, limited truck resources, and both consignee and shipper having fixed withholding capacities. The consignee's withholding threshold is strategically set to minimize overall transport and detention costs. We derive closed-form expressions for performance measures in the case of a single storage facility at the shipper, utilizing a matrix-based approach. We extend this methodology numerically to the general case. Additionally, we present an accurate fixed-point approximation facilitating the determination of performance measures and optimal threshold. Our findings show the importance of withholding decisions in import and balanced areas for cost reduction. In export areas, a policy of full reuse proves nearly optimal. Analyzing dynamic state-dependent consignee decisions via a Markov decision process, we establish that the optimal policy involves withholding thresholds increasing with stored quantity at the shipper. While optimal, state-dependent thresholds yield limited cost savings compared to a fixed threshold, suggesting minimal impact from consignee-shipper information sharing. Additionally, we examine the influence of variability in matching and production times, observing decreased costs with reduced variability, particularly in export areas, but with minor impacts on withholding decisions.

Original languageEnglish
Pages (from-to)930-941
Number of pages12
JournalEuropean Journal of Operational Research
Volume316
Issue number3
DOIs
Publication statusPublished - 1 Aug 2024

Keywords

  • (O) Transportation
  • Container
  • Double-ended queue
  • Markov decision process
  • Street turn

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