A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem

  • Authors:

  • Manuel Vázquez;L. Darrell Whitley

  • Affiliations:

  • -;-

  • Venue:
  • PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
  • Year:
  • 2000

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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 nondelay 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.