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.
Highlights A method that combines machine learning with operations research is presented. Decision trees are used to make probabilistic predictions of container delays. Predictions are fed into the scenario generation process of a two-stage stochastic program, which is solved by sample average approximation (SAA) to make barge planning. Simulations show that up to 2.07% improvement is observed over traditional stochastic program.
Integrating stochastic programs and decision trees in capacitated barge planning with uncertain container arrivals
2021-09-07
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Dynamic barge planning with stochastic container arrivals
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