KI 2005: KI 2005: Advances in Artificial Intelligence pp 222-234 | Cite as
Metaheuristics for Late Work Minimization in Two-Machine Flow Shop with Common Due Date
Conference paper
Abstract
In this paper, metaheuristic approaches for the weighted late work minimization in the two-machine flow shop problem with a common due date (F2 | d j =d | Y w ) are presented. The late work performance measure estimates the quality of a solution with regard to the duration of the late parts of jobs not taking into account the quantity of the delay for the fully late activities. Since, the problem mentioned is known to be NP-hard, three trajectory based methods, namely simulated annealing, tabu search and variable neighborhood search were designed and compared to an exact approach and a list scheduling algorithm.
Keywords
Tabu Search Variable Neighborhood Search List Schedule Metaheuristic Approach Tabu Search Approach
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
- 1.Barr, R.S., Golden, B.L., Kelly, J.P., Resende, M.G.C., Stewart Jr., W.R.: Designing and Reporting on Computational Experiments with Heuristic Methods. Journal of Heuristics 1, 9–32 (1995)MATHCrossRefGoogle Scholar
- 2.Blazewicz, J.: Scheduling Preemptible Tasks on Parallel Processors with Information Loss. Recherche Technique et Science Informatiques 3/6, 415–420 (1984)MathSciNetGoogle Scholar
- 3.Blazewicz, J., Ecker, K., Pesch, E., Schmidt, G., Weglarz, J.: Scheduling Computer and Manufacturing Processes, 2nd edn. Springer, Berlin (2001)MATHGoogle Scholar
- 4.Blazewicz, J., Finke, G.: Minimizing Mean Weighted Execution Time Loss on Identical and Uniform Processors. Information Processing Letters 24, 259–263 (1987)MATHCrossRefMathSciNetGoogle Scholar
- 5.Blazewicz, J., Pesch, E., Sterna, M., Werner, F.: Revenue Management in a Job-shop: a Dynamic Programming Approach. Preprint Nr. 40/03. Otto-von-Guericke-University, Magdeburg (2003)Google Scholar
- 6.Blazewicz, J., Pesch, E., Sterna, M., Werner, F.: Open Shop Scheduling Problems with Late Work Criteria. Discrete Applied Mathematics 134, 1–24 (2004)MATHCrossRefMathSciNetGoogle Scholar
- 7.Blazewicz, J., Pesch, E., Sterna, M., Werner, F.: The Two-Machine Flow-Shop Problem with Weighted Late Work Criterion and Common Due Date. European Journal of Operational Research 165/2, 408–415 (2005)MathSciNetGoogle Scholar
- 8.Błażewicz, J., Pesch, E., Sterna, M., Werner, F.: Flow Shop Scheduling with Late Work Criterion – Choosing the Best Solution Strategy. In: Manandhar, S., Austin, J., Desai, U., Oyanagi, Y., Talukder, A.K. (eds.) AACC 2004. LNCS, vol. 3285, pp. 68–75. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 9.Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys 35/3, 268–308 (2003)CrossRefGoogle Scholar
- 10.Brucker, P.: Scheduling Algorithms, 2nd edn. Springer, Berlin (1998)MATHGoogle Scholar
- 11.Crama, Y., Kolen, A., Pesch, E.: Local Search in Combinatorial Optimization. In: Braspenning, P.J., Weijters, A.J.M.M.T., Thuijsman, F. (eds.) Neural Network School 1999. LNCS, vol. 931, pp. 157–174. Springer, Heidelberg (1995)CrossRefGoogle Scholar
- 12.Dorndorf, U., Pesch, E.: Variable Depth Search and Embedded Schedule Neighbourhoods for Job Shop Scheduling. In: Proceedings of the 4th International Workshop on Project Management and Scheduling, pp. 232–235 (1994)Google Scholar
- 13.Garey, M.R., Johnson, D.S.: Computers and Intractability. W.H. Freeman and Co., San Francisco (1979)MATHGoogle Scholar
- 14.Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)MATHGoogle Scholar
- 15.Hansen, P., Mladenović, N.: Variable Neighbour Search. Principles and Applications. European Journal of Operational Research 130, 449–467 (2001)MATHCrossRefGoogle Scholar
- 16.Haupt, R.: A Survey of Priority Rule–Based Scheduling. OR Spektrum 11, 3–16 (1989)MATHCrossRefMathSciNetGoogle Scholar
- 17.Hooker, J.N.: Testing Heuristics: We Have It All Wrong. Journal of Heuristics 1, 33–42 (1995)MATHCrossRefGoogle Scholar
- 18.Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220/4598, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
- 19.Johnson, S.M.: Optimal Two- and Three-Stage Production Schedules. Naval Research Logistics Quarterly 1, 61–68 (1954)CrossRefGoogle Scholar
- 20.Leung, J.Y.T.: Minimizing Total Weighted Error for Imprecise Computation Tasks and Related Problems. In: Leung, J.Y.T. (ed.) Handbook of Scheduling: Algorithms, Models, and Performance Analysis, ch. 34, pp. 1–16. CRC Press, Boca Raton (2004)Google Scholar
- 21.Pesch, E., Glover, F.: TSP Ejection Chains. Discrete Applied Mathematics 76, 165–181 (1997)MATHCrossRefMathSciNetGoogle Scholar
- 22.Pinedo, M., Chao, X.: Operation Scheduling with Applications in Manufacturing and Services. Irwin/McGraw-Hill, Boston (1999)Google Scholar
- 23.Potts, C.N., Van Wassenhove, L.N.: Single Machine Scheduling to Minimize Total Late Work. Operations Research 40/3, 586–595 (1991)Google Scholar
- 24.Potts, C.N., Van Wassenhove, L.N.: Approximation Algorithms for Scheduling a Single Machine to Minimize Total Late Work. Operations Research Letters 11, 261–266 (1991)CrossRefGoogle Scholar
- 25.Sterna, M.: Problems and Algorithms in Non-Classical Shop Scheduling. Scientific Publishers of the Polish Academy of Sciences, Poznan (2000)Google Scholar
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