An Implementation of a Genetic Algorithm Approach in Non-Identical Parallel Machines
Pelin Alcan, Yavuz Ozdemir, Berk Cinar and Huseyin Basligil
After the Industrial Revolution in Europe, manufacturing systems became very important research areas. There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules have been proposed to solve hard combinatorial optimisation problems. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimisation problems in view of the fact that they have high efficiency and are fit for practical application. Even though this is a common problem in the industry, only a small number of studies have dealt with non-identical parallel machines. This paper concerns the fuzzy approach to the scheduling problem of non-identical parallel machines using genetic algorithms. The genetic algorithm proposed here fits the non-identical parallel machine scheduling problem of minimising the maximum completion time. Also, fuzzy systems and fuzzy logic are excellent tools for representing heuristic, common sense rules, which is why we have tried to use fuzzy systems in this study. The real-life nonidentical parallel machine genetic algorithm application using fuzzy numbers has been performed with a JAVA program.
Keywords: Genetic algorithm, parallel machine, scheduling, fuzzy systems, fuzzy logic