Using Genetic Algorithms for Large Scale Optimizationof Assignment, Planning and Rescheduling Problems

There has always been a need to solve real-life large-scale problems, such as efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimization genre. A class of combinatorial optimization problems is NP-hard and, among many well-known, several of them are assignment, planning and rescheduling problems. Assignment problems can deal with optimal assignment of teams of collaborating agents; planning problems can be effects-based planning that search for promising plans to get desired end states with minimal cost; rescheduling problems can be multi-criteria optimization of rescheduling resources that modify existing original schedule. These large scale optimization problems are complex with intractable and highly complex search spaces. Currently, there are no known algorithms with polynomial time complexity, which can solve these problems. Genetic Algorithms have been successfully applied to solve many complex optimization problems but not to the specific problems mentioned above. The aim of the research, presented in this thesis, is to use Genetic Algorithms for large scale optimization of assignment, planning and rescheduling problems. More specifically, the contributions of the thesis are to: (i) adapt existing and develop new efficient Genetic Algorithms to solve large scale assignment problems, and (ii) adapt existing Genetic Algorithms to solve large scale effects-based planning, and multi-objective rescheduling optimization problems. In case of assignment, we solve a team assignment problem and investigate specific regions in a solution space for assignment problems with huge search spaces. For the team assignment, an existing Genetic Algorithm is adapted and applied for optimal assignment of tasks to teams of collaborating agents. The algorithm is scalable, stable, robust and produces a near optimal solution. The results of the team assignment problem show that the existing Genetic Algorithms are not efficient for optimal assignment of tasks to teams of agents. Hence, to solve larger instances of the problem efficiently, new Genetic Algorithms are developed with emphasis on the construction of crossover operators. Since teams assignment can be multi-criteria, a multi-objective model is constructed and two widely used multi-objective evolutionary algorithms are applied. Further, for the assignment problems with huge search spaces, an existing Genetic Algorithm is adapted to extract possible combinations of input parameters from a specified solution space region. To solve the large scale effects-based planning, a multi-objective optimization problem is formulated for the evaluation of operational plans and a multi-objective Genetic Algorithm is adapted and applied to the problem. The results show that the suggested algorithm is much more efficient than A*. For the rescheduling problem, a multi-objective optimization model for rescheduling of resources is proposed and a multi-objective Genetic Algorithm is adapted and applied to obtain the Pareto-optimal solutions. The research presented in this thesis confirms that Genetic Algorithms can be used for large scale assignment, planning and rescheduling problems since they have shown to be suitable in solving these problems efficiently.