Refining scheduling policies with genetic algorithms

Abstract

Genetic Algorithms (GAs) are popular approaches in solving various complex real-world problems. However, it is required that a careful attention is to be paid to the contextual knowledge as well as the implementation of genetic material and operators. On the other hand, the job-shop scheduling (JSS) problem remains as challenging NP-hard combinatorial problem, which attracts researchers since it is invented. The dynamic version of job-shop is even more challenging due to its dynamically changing characteristics. Similar to other metaheuristic approaches, GA has not been so successful in solving this sort of problems due to instant decision making process needed in solving this type of problems. Heuristic procedures such as those so called Priority Rule or Dispatching Rules are more useful for this purpose, but, depending on the properties and purpose of use of each, the same performance is not expected from these instant decision making operators. In this paper, a policy refinement approach is proposed to optimise a sequence of Dispatching Rules (DRs) for a timewindow of scheduling process in which a GA algorithm evolves the sequences towards an optimum configuration. The preliminary results provided in this paper seem very encouraging.

Microphone configuration for beamformer design using the Taguchi method

“…A case study is conducted based on a common office [31] in order to evaluate the effectiveness of the Taguchi method. The numerical results obtained by the Taguchi method are compared with those obtained by the two commonly used heuristic methods including genetic algorithm (GA) [32][33][34] and particle swarm optimization algorithm (PSO) [35] for determining microphone configurations. These two heuristic methods are also commonly used on solving multi-objective problems and also they have been used on designing microphone configurations [36][37][38][39][40][41].…”