Integrating Stochastic Programs and Decision Trees in Capacitated Barge Planning with Uncertain Container Arrivals
Volkan Gümüşkaya, FedEx
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.
Volkan Gümüşkaya started his bachelor's degree in Industrial Engineering department at Middle East Technical University (METU) in 2004. He graduated in 2009 and acquired his master's degree in the same department in 2013. In his master’s study, he worked on disassembly line balancing in hybrid lines with stochastic task times under the supervision of Tevhide Altekin and Pelin Bayindir.
He worked in industry for four years as data scientist, production planning engineer and assembly line supervisor. In 2017, he started his Ph.D. study at Eindhoven University of Technology under the supervision of Willem van Jaarsveld, Remco Dijkman, Paul Grefen and Albert Veenstra at the School of Industrial Engineering. During his Ph.D. he worked on multimodal transport in hinterland.
Currently, he works at FedEx.
Friday, December 3, 2021, 4.00 pm - Zoom Meeting