A Comparison of Genetic Algorithms for theStatic Job

A variety of Genetic Algorithms for the static Job Shop Scheduling Problem have been developed using various methods: direct vs. indirect representations, pure vs. hybrid GA's and serial vs. parallel GA's. We implement a hybrid GA, called OBGT, for solving the static Job Shop Scheduling Problem. A chromosome representation containing the schedule itself is used and order-based crossover operators are combined with techniques that produce active and non-delay schedules. Additionally, Local Search is applied to improve each individual created. OBGT results are compared in terms of the quality of solutions against the state-of-the-art Nowicki and Smutnicki Tabu Search algorithm as well as other GAs, including THX, HGA and GA3. The test problems include diierent problem classes from the OR-library benchmark problems and more structured job-correlated and machine-correlated problems. We nd that each technique, including OBGT, is well suited for particular classes of benchmark problems, but no algorithm is best across all problem classes.