9505668 Ramesh The operations scheduling problem concerns the scheduling of generating units in the most economic manner subject to several constraints. The problem is to identify which units should be ON (committed) in each our over a period of time ranging from a day to a week, and to determine the generation level of each committed unit in each hour. The main difficulty in solving the problem is that it is a large mixed-integer nonlinear programming (MINLP) problem, and is compute-intensive. Because of this, the algorithmic techniques currently employed do not model many relevant constraints, and use several approximations which lead to suboptimal solutions. The main objectives of this research are: a) to investigate and develop a new population-based optimization technique for MINLP problems, and b) to use a comprehensive model encompassing most, if not all, relevant constraints for the operations scheduling problem. The new combinatorial optimization technique that we will explore in this research is a decomposition methodology that evaluates a population of solutions, in parallel, for each subproblem of the over-all problem. The approach will be applied to solve a comprehensive model of the operations scheduling problem. We will investigate the complexity and optimality of the proposed technique. ***
