Ant Colony Optimization with Global Pheromone Evaluation for Scheduling a Single Machine
 Daniel Merkle,
 Martin Middendorf
 … show all 2 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
Ant Colony Optimization (ACO) is a metaheuristic that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem and compare it to an existing ACO algorithm for the unweighted problem. The proposed algorithm has some novel properties that are of general interest for ACO optimization. A main novelty is that the ants are guided on their way through the decision space by global pheromone information instead of using only local pheromone information. It is also shown that the ACO optimization behaviour can be improved when priority scheduling heuristics are adapted so that they appropriately reflect absolute quality differences between the alternatives before they are used by the ants. Further improvements can be obtained by identifying situations where the ants can perform optimal decisions.
 M. Dorigo and G. Di Caro, “The ant colony optimization metaheuristic,” in New Ideas in Optimization, edited by D. Corne, M. Dorigo, and F. Glover, McGrawHill, pp. 11–32, 1999.
 P. Forsyth and A. Wren, “An ant system for bus driver scheduling,” University of Leeds—School of Computer Studies, Report 97.25, 1997.
 Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M. (1994) Ant system for jobshop scheduling. JORBEL—Belgian Journal of Operations Research, Statistics and Computer Science 34: pp. 3953
 Dorigo, M., Maniezzo, V., Colorni, A. (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Trans. Systems Man, and Cybernetics—Part B 26: pp. 2941
 Stützle, T. (1998) An ant approach for the flow shop problem. Proc. of the 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT'98). Verlag Mainz, Aachen, pp. 15601564
 A. Bauer, B. Bullnheimer, R.F. Hartl, and C. Strauss, “An ant colony optimization approach for the single machine total tardiness problem,” in Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), Washington D.C., USA, 6–9 July, 1999, pp. 1445–1450.
 R. Michels and M. Middendorf, “An ant system for the shortest common supersequence problem,” in New Ideas in Optimization, edited by D. Corne, M. Dorigo, and F. Glover, McGrawHill, pp. 692–701, 1999.
 Du, D.J., Leung, J.Y.T. (1990) Minimizing the total tardiness on one machine is NPhard. Mathematics of Operations Research 15: pp. 483496
 E.L. Lawler, “A ‘pseudopolynomial’ algorithm for sequencing jobs to minimize total tardiness,” Annals of Discrete Mathematics, pp. i:331–342, 1977.
 Crauwels, H.A.J., Potts, C.N., VanWassenhove, L.N. (1998) Local search heuristics for the single machine total weighted tardiness scheduling problem. INFORMS Journal on Computing 10: pp. 341359
 Congram, R.K., Potts, C.N., van de Velde, S.L. (2002) An iterated dynasearch algorithm for the singlemachine total weighted tardiness scheduling problem. INFORMS Journal on Computing 14: pp. 5257
 Dorigo, M., Gambardella, L.M. (1997) Ant colony system: A cooperative learning approach to the travelling salesman problem. IEEE Trans. on Evolutionary Comp. 1: pp. 5366
 http://mscmga.ms.ic.ac.uk/jeb/orlib/wtinfo.html.
 M. den Besten, T. Stützle, and M. Dorigo, “Ant colony optimization for the total weighted tardiness problem,” in Parallel Problem Solving from Nature: 6th International Conference, edited by M. Schoenauer et al., SpringerVerlag, LNCS 1917, pp. 611–620, 2000.
 Title
 Ant Colony Optimization with Global Pheromone Evaluation for Scheduling a Single Machine
 Journal

Applied Intelligence
Volume 18, Issue 1 , pp 105111
 Cover Date
 20030101
 DOI
 10.1023/A:1020999407672
 Print ISSN
 0924669X
 Online ISSN
 15737497
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 ant algorithms
 scheduling
 tardiness
 Industry Sectors
 Authors

 Daniel Merkle ^{(1)}
 Martin Middendorf ^{(2)}
 Author Affiliations

 1. Institute AIFB, University of Karlsruhe, D76128, Karlsruhe, Germany
 2. Department of Computer Science, University of Leipzig, Augustusplatz 1011, Leipzig, Germany, 04109