Data-driven scheduling optimization under uncertainty using Renyi entropy and skewness criterion

In order to deal with the resource cost uncertainties, this paper introduces a Renyi mean-entropy-skewness (RMES) information criterion for the scheduling optimization problems in flexible manufacturing systems (FMSs). Motivated by potential limitations in the existing measures, this third-order information criterion is carefully integrated to be more general and more robust in representing the schedule dispersion under uncertainties. The RMES information criterion is estimated using data-driven techniques so that it does not rely on the assumptions of exact probability distributions which are usually unknown in practice. Modeled with Petri net (PN) and system state reachable graph (RG), the RG-based dynamic programming (DP) algorithm and an approximate dynamic programming (ADP) algorithm are presented to solve the proposed model. The effectiveness of the introduced information criterion is verified by both technical proofs and extensive simulation studies of systems with a wide range of scales and data types. A real stamping industrial case study is also conducted as a justification of the model's practical applicability.