Genetic algorithms for the scheduling problem with arbitrary precedence relations and skilled operators

Abstract

Scheduling problems involving physical machines and human resources are frequent in real production environments. In this paper, we tackle a problem in which a set of tasks must be performed on a set of machines under the assistance of human operators, subject to some constraints such as precedence relations on the tasks, limited capacity of machines and operators, and skills of the operators to assist the processing of tasks. We analyze the problem and propose a generic schedule builder that may be adapted to build schedules in different dominant and non-dominant search spaces. The schedule builder was exploited as a de- coder in a genetic algorithm. All the proposals were evaluated on a benchmark set with instances of different characteristics. The experimental study revealed useful insights of practical interest and showed substantial improvements of the genetic algorithm over existing methods in the literature.