Open AccessPerformance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling
Author(s) -
Yasin Göçgün
Publication year - 2021
Publication title -
an international journal of optimization and control: theories and applications/e-an international journal of optimization and control: theories and applications
Language(s) - English
Resource type - Journals
eISSN - 2146-5703
pISSN - 2146-0957
DOI - 10.11121/ijocta.01.2021.00987
Subject(s) - dynamic programming , mathematical optimization , computer science , scheduling (production processes) , stochastic programming , class (philosophy) , dynamic priority scheduling , lagrangian , job shop scheduling , mathematics , artificial intelligence , schedule , operating system
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.