Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling

Authors

DOI:

https://doi.org/10.11121/ijocta.01.2021.00987

Keywords:

dynamic stochastic advanced scheduling, approximate dynamic programming

Abstract

This paper focuses on the performance comparison of several approximate dynamic programming (ADP) techniques. In particular, we evaluate three ADP techniques through a class of dynamic stochastic scheduling problems: Lagrangian-based ADP, linear programming-based ADP, and direct search-based ADP. We uniquely implement the direct search-based ADP through basis functions that differ from those used in the relevant literature. The class of scheduling problems has the property that jobs arriving dynamically and stochastically must be scheduled to days in advance. Numerical results reveal that the direct search-based ADP outperforms others in the majority of problem sets generated.

Author Biography

Yasin Göçgün, Istinye University

Dr. Gocgun received his B.S. degree and M.S. degree from the Industrial Engineering Department at Bilkent University in 2003 and 2005, respectively. After completing his doctoral studies in the Industrial and Systems Engineering Department at the University of Washington in 2010, Dr. Gocgun worked as a postdoctoral fellow in the Sauder School of Business at the University of British Columbia between 2010 and 2012. After his first post-doc, Dr. Gocgun carried out another post-doc study in the Mechanical and Industrial Engineering Department at the University of Toronto between 2012 and 2014. Prior to joining the Industrial Engineering Department at İstinye University, Dr. Gocgun worked as an assistant professor in the Industrial Engineering Department at Altınbaş University between 2014 and 2020. His research interests primarily focus on dynamic stochastic optimization, operations research in healthcare, Markov decision processes, approximate dynamic programming, and scheduling. Dr. Gocgun’s research studies were published in journals such as Computers and Operations Research, European Journal of Operational Research and Healthcare Management Science.

References

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Published

2021-05-09

How to Cite

Göçgün, Y. (2021). Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling. An International Journal of Optimization and Control: Theories &Amp; Applications (IJOCTA), 11(2), 178–185. https://doi.org/10.11121/ijocta.01.2021.00987

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Section

Research Articles