Abstract
A variety of Genetic Algorithms (GA’s) 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 JSSP. A chromosome representation containing the schedule itself is used and order-based 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 different problem classes from the OR-library benchmark problems and more structured job-correlated and machine-correlated problems. We find that each technique, including OBGT, is well suited for particular classes of benchmark problems, but no algorithm is best across all problem classes.
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Vázquez, M., Whitley, D. (2000). A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_30
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DOI: https://doi.org/10.1007/3-540-45356-3_30
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