Fuzzy manufacturing scheduling by virus-evolutionary genetic algorithm in self-organizing manufacturing system

This paper deals with a fuzzy manufacturing scheduling problem in the self-organizing manufacturing system (SOMS), in which modules self-organize effectively according to other modules. A module decides its outputs through the interaction with other modules, but the module does not share all information of other modules. In addition, the information received from other modules often includes ambiguous and incomplete information. We therefore apply fuzzy theory to represent incomplete information of other modules. Furthermore, we apply a virus-evolutionary genetic algorithm (VEGA) to a fuzzy flow shop scheduling problem with fuzzy transportation time. The VEGA is a stochastic optimization method simulating coevolution of host population and virus population. The simulation results indicate that the fuzzified information is effective when a module has incomplete information in the SOMS.