An Effective Near-Optimal State-Space Search Method: An Application to a Scheduling Problem

Abstract

This paper presents an effective near-optimal search method for state-space problems. The method, LTAast (Learning Threshold Aast), accepts a threshold parameter, p, as an input and finds a solution within that range of the optimum. The larger the parameter, the faster the method finds a solution. LTAast is based on a combination of recursion and dynamic memory and, like Aast, keeps information about all states in memory. In contrast to Aast however, which represents each node as a complete state, LTAast represents each node using an operator. This representation of the nodes makes LTAast dramatically efficient with respect to memory usage. Another advantage of LTAast is that it eliminates any need for computational effort to maintain a priority queue, and this elimination significantly increases speed. To test the effectiveness and efficiency of the method we have applied it to NP-hard problems in scheduling. The test results indicate that the method is effective in trading speed with the quality of solutions and that it is efficient in producing solutions for p = 0.