Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling

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.

Optimal matchday schedule for Turkish professional soccer league using nonlinear binary integer programming

“…Future research may consider measures such as rest mismatch or breaks in addition to seasonal or round specific day distribution. In this regard, uncertain and dynamic natures of these problems can be addressed using mathematical models from the field of dynamic stochastic optimization (see [50] and [51]). Additionally, it is worthwhile to investigate cases with multi-round solutions obtained as other tournaments progress or to incorporate uncertainty into such problems owing to the fact that many games are rescheduled or canceled due to bad weather conditions in popular soccer leagues.…”