Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
Model-based predictive control for generalized production planning problems
Computers in Industry - Special issue: ASI '95
A Capacitated Production-Inventory Model with Periodic Demand
Operations Research
Guaranteed cost control for multi-inventory systems with uncertain demand
Automatica (Journal of IFAC)
A reinforcement learning model for supply chain ordering management: An application to the beer game
Decision Support Systems
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
A study of using RST to create the supplier selection model and decision-making rules
Expert Systems with Applications: An International Journal
A dynamical systems model for understanding behavioral interventions for weight loss
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Adaptive inventory control in production systems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Hi-index | 22.15 |
A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain.