Optimisation and Scheduling

Provides an understanding of the underlying principles of optimisation in relation to the various techniques used for realising optimisers and schedulers. Topics covered include the formulation of optimisation problems; equalities, constraints and objective functions: least squares and regression: linear programming; concepts of LP, simplex method, sensitivity and duality: unidirectional optimisation; continuity, constrained and unconstrained functions, Newton’s method, quadratic approximation and interpretation: multivariable optimisation; direct and indirect methods, finite difference approximations: non-linear programming (NLP); quadratic programming, penalty functions, sequential recursive programming (SQP), optimisation of dynamic processes: real-time optimisation; structure of optimisers, steady state models, technical requirements, interface with control system, calibration, checks for convergence: sequential processes; formulation of objectives, project evaluation and review technique (PERT), critical path methods (CPM), dynamic programming, mixed integer linear programming (MILP) & non-linear programming (MINLP), branch and bound, genetic algorithms, biological analogies, concepts of evolutionary computing, chromosome representation, genetic operators, multi-objective genetic algorithms (MOGA).

Case studies and PC based exercises are used to familiarise delegates with the application of these methods to practical optimisation and scheduling problems. [details]