An Evolutionary Approach to Cyclic Real World Scheduling

Real world problems are often an inspiration for engineers to model and solve new scheduling problems. In this paper a cyclic scheduling of jobs with uncertain data is considered. A minimization of the cycle time represents an important economical factor considered by the companies. Proposed model was tested and data concerning processing times was obtained. Due to the nature of stations and human operator factor, we deal with uncertain data modeled using fuzzy numbers. For the modeled production station a fuzzy genetic algorithm was proposed and tested against deterministic algorithms. Proposed algorithm outperformed all deterministic algorithms.