Discrete optimization problems are usually NP hard. The structural characteristics of these problems significantly dictate the solution landscape. In this paper, we explore a structure-based approach to solving these kinds of problems. We use a reinforcement learning system to adaptively learn the structural characteristics of the problem, hereby decomposing the problem into several subproblems. Based on these structural characteristics, we develop a Genetic Algorithm by using structural operations to recombine these subproblems together to solve the problem. The reinforcement learning system directs the GA. We test our algorithm on the Tactical Fixed Interval Scheduling Problem(TFISP) which is the problem of determining the minimum number of parallel non-identical machine such that a feasible schedule exists for a given set of jobs. This work continues our work in exploiting structure for optimization.
Exact and approximation algorithms for the operational fixed interval scheduling problem
