TY - JOUR
T1 - Integrating stochastic programs and decision trees in capacitated barge planning with uncertain container arrivals
AU - Gumuskaya, Volkan
AU - van Jaarsveld, Willem
AU - Dijkman, Remco
AU - Grefen, Paul
AU - Veenstra, Albert
N1 - Funding Information:
This work is part of the ISOLA project with project number 438-13-214 , which is partly financed by the Dutch Research Council (NWO), Netherlands .
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85115941148&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2021.103383
DO - 10.1016/j.trc.2021.103383
M3 - Article
VL - 132
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
SN - 0968-090X
M1 - 103383
ER -