Integrating stochastic programs and decision trees in capacitated barge planning with uncertain container arrivals

Volkan Gumuskaya*, Willem van Jaarsveld, Remco Dijkman, Paul Grefen, Albert Veenstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

In this paper, we propose an approach that combines optimization techniques with machine learning to improve capacitated barge planning with uncertain container arrivals. The main idea is to use the predictions of a decision tree in the scenario generation of a 2-stage stochastic program to plan the barge calls (i.e. visits) of a barge operator. The predictions of container arrivals help to generate more accurate scenarios, which in turn leads to more informed decisions and less costs. The approach is tested with an iterative method of periodic planning and simulation for a one year duration so that the long term performance is evaluated. A computational experiment is conducted using the historical data of an inland terminal and the Port of Rotterdam. The results show that the proposed approach improves total costs up to 2.07% over the traditional stochastic approach, and up to 4.57% over the current method used in industry.

Original languageEnglish
Article number103383
Number of pages19
JournalTransportation Research Part C: Emerging Technologies
Volume132
DOIs
Publication statusPublished - Nov 2021

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