A Decision-Tree Stacking Heuristic for Large Scale Reshuffling Problems at a Container Yard

Amir Gharehgozli, Y Yu, Rene de Koster, Jan T Udding

Research output: Contribution to journalArticleAcademicpeer-review

59 Citations (Scopus)

Abstract

Reshuffling containers, one of the daily operations at a container terminal, is time consuming and increases a ship’s berthing time. We propose a decision-tree heuristic to minimise the expected number of reshuffles when arriving containers should be stacked in a block of containers with an arbitrary number of piles. The heuristic algorithm uses the optimal solutions of a stochastic dynamic programming model. Since the total number of states of the dynamic programming model increases exponentially, the model can only solve small-scale problems in a reasonable time. To solve large-scale problems, the heuristic uses the results of the exact model for small-scale problems to generate generalised decision trees. These trees can be used to solve problems with a realistic number of piles. The numerical experiments show the effectiveness of the algorithm. For small-scale problems, the trees can quickly make optimal decisions. For large-scale problems, the decision-tree heuristic significantly outperforms stacking policies commonly used in practice. Using the decision trees, we can compare the performance of a shared-stacking policy, which allows containers of multiple ships to be stacked on top of each other, with a dedicated-stacking policy. Shared-stacking appears to outperform dedicated-stacking.
Original languageEnglish
Pages (from-to)2592-2611
Number of pages20
JournalInternational Journal of Production Research
Volume52
Issue number9
DOIs
Publication statusPublished - 2014

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  • RSM LIS

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