Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling

GÖÇGÜN Y.

International Journal of Optimization and Control: Theories and Applications, cilt.11, sa.2, ss.178-185, 2021 (Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.11121/ijocta.01.2021.00987
  • Dergi Adı: International Journal of Optimization and Control: Theories and Applications
  • Derginin Tarandığı İndeksler: Scopus, Academic Search Premier, Communication Abstracts, zbMATH, Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.178-185
  • Anahtar Kelimeler: Dynamic stochastic scheduling, Markov decision processes, Approximate dynamic programming
  • İstanbul Medipol Üniversitesi Adresli: Evet

Özet

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.