Multi-criteria scheduling: an agent-based approach for expert knowledge integration
- 234 Downloads
In this work, we present an agent-based approach to multi-criteria combinatorial optimization. It allows to flexibly combine elementary heuristics that may be optimal for corresponding single-criterion problems.
We optimize an instance of the scheduling problem 1|d j |∑C j ,L max and show that the modular building block architecture of our optimization model and the distribution of acting entities enables the easy integration of problem specific expert knowledge. We present a universal mutation operator for combinatorial problem encodings that allows to construct certain solution strategies, such as advantageous sorting or known optimal sequencing procedures. In this way, it becomes possible to derive more complex heuristics from atomic local heuristics that are known to solve fractions of the complete problem. We show that we can approximate both single-criterion problems such as P m |d j |∑U j as well as more challenging multi-criteria scheduling problems, like P m ||C max,∑C j and P m |d j |C max,∑C j ,∑U j . The latter problems are evaluated with extensive simulations comparing the standard multi-criteria evolutionary algorithm NSGA-2 and the new agent-based model.
KeywordsMulti-criteria scheduling Predator–prey model Parallel machine scheduling Evolutionary multi-criteria optimization
Unable to display preview. Download preview PDF.
- Bartz-Beielstein, T., Lasarczyk, C. W. G., & Preuss, M. (2005). Sequential parameter optimization. In IEEE congress on evolutionary computation (Vol. 1, pp. 773–780). New York: IEEE Press. Google Scholar
- Coello, C. A. C., Lamont, G. B., & Veldhuizen, D. A. V. (2007). Evolutionary algorithms for solving multi-objective problems. Genetic and evolutionary computation (2nd ed.). New York: Springer. Google Scholar
- Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley-interscience series in systems and optimization (1st ed.). New York: Wiley. Google Scholar
- Deb, K., Agrawal, S., Pratab, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer et al. (Eds.), Lecture notes in computer science: Vol. 1917. Proceedings of the conference on parallel problem solving from nature (pp. 849–858). Berlin: Springer. Google Scholar
- Durillo, J., Nebro, A., & Alba, E. (2010). The jMetal framework for multi-objective optimization: design and architecture. In IEEE congress on evolutionary computation (Vol. 5467, pp. 4138–4325). Barcelona, Spain. Berlin: Springer. Google Scholar
- Dutot, P. F., Rzadca, K., Saule, E., & Trystram, D. (2010). Multi-objective scheduling. In Introduction to scheduling, (1st ed.). (pp. 219–251). Boca Raton: CRC Press. Google Scholar
- Grimme, C., & Lepping, J. (2007). Designing multi-objective variation operators using a predator–prey approach. In Lecture notes in computer science: Vol. 4403. Proceedings of the international conference on evolutionary multi-criterion optimization (pp. 21–35). Berlin: Springer. CrossRefGoogle Scholar
- Grimme, C., Lepping, J., & Papaspyrou, A. (2007). Exploring the behavior of building blocks for multi-objective variation operator design using predator–prey dynamics. In D. Thierens et al. (Eds.), Proceedings of the genetic and evolutionary computation conference (pp. 805–812). New York: ACM. Google Scholar
- Jackson, J. R. (1955). Scheduling a production line to minimize maximum tardiness (Management Science Research Project, Research Report 43), University of California, Los Angeles. Google Scholar
- Pinedo, M. (2009). Scheduling: theory, algorithms, and systems (3rd ed.). Berlin: Springer. Google Scholar
- Schwefel, H. P. (1995). Evolution and optimum seeking (1st ed.). New York: Wiley. Google Scholar
- T’kindt, V., & Billaut, J. C. (2006). Multicriteria scheduling. Theory, models and algorithms (2nd ed.). Berlin: Springer. Google Scholar
- Vincent, T. L., & Grantham, W. J. (1981). Optimality in parametric systems (1st ed.). New York: Wiley. Google Scholar
- Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. thesis, ETH Zürich. Google Scholar
- Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm (Technical Report 103). Computer Engineering and Communication Networks Lab (TIK), ETH Zürich. Google Scholar