Advanced Scheduling Models and Methods

ENG ME 766

Emphasizes basic methodological tools and recent advances for the solution of scheduling problems in both deterministic and stochastic settings. Models considered include classical scheduling models, DEDS, neural nets, queueing models, flow control models, and linear programming models. Methods of control and analysis include optimal control, dynamic programming, fuzzy control, adaptive control, hierarchical control, genetic algorithms, simulated annealing, Lagrangian relaxation, and heavy traffic approximations. Examples and case studies focus on applications from manufacturing systems, computer and communication networks, and transportation systems. Meets with ENGSE766. Students may not receive credit for both.

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.