Developing GA-based hybrid approaches for a real-world mixed-integer scheduling problem

Many real-world scheduling problems are suited to a mixed-integer formulation. The solution of these problems involves the determination of integer and continuous variables at each time interval of the scheduling period. The solution procedure requires simultaneous consideration of these two types of variables. In recent years researchers have focused much attention on developing new hybrid approaches using modern heuristic and traditional exact methods. This paper proposes the development of a variety of hybrid approaches that combines heuristics and mathematical programming within a genetic algorithm (GA) framework for a real-world mixed integer scheduling problem, namely the generation scheduling (GS) problem in electrical power systems. The problem is to define on/off decisions and generation levels for each generator in a power system for each scheduling interval. This paper investigates how the optimum or near optimum solution for the GS problem may be quickly identified. The results obtained are promising and show that the hybrid approach offers an effective alternative for solving the GS problems within a realistic timeframe