A problem space algorithm for single machine weighted tardiness problems (original) (raw)

Abstract

We propose a problem space genetic algorithm to solve single machine total weighted tardiness scheduling problems. The proposed algorithm utilizes global and time-dependent local dominance rules to improve the neighborhood structure of the search space. They are also a powerful exploitation (intensifying) tool since the global optimum is one of the local optimum solutions. Furthermore, the problem space search method significantly enhances the exploration (diversification) capability of the genetic algorithm. In summary, we can improve both solution quality and robustness over the other local search algorithms reported in the literature.

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References (13)

  1. Akturk, M.S. and Yildirim, M.B. (1998) A new lower bounding scheme for the total weighted tardiness problem. Computers and Opera- tions Research, 25(4), 265-278.
  2. Crauwels, H.A.J., Potts, C.N. and Van Wassenhove, L.N. (1998) Local search heuristics for the single machine total weighted tardiness scheduling. INFORMS Journal on Computing, 10(3), 341-350.
  3. Emmons, H. (1969) One machine sequencing to minimize certain functions of job tardiness. Operations Research, 17, 701-715.
  4. Lawler, E.L. (1977) A 'Pseudopolynomial' algorithm for sequencing jobs to minimize total tardiness. Annals of Discrete Mathematics, 1, 331-342.
  5. Morton, T.E. and Pentico, D.W. (1993) Heuristic Scheduling Systems with Applications to Production Systems and Project Management, Wiley, New York, NY.
  6. Potts, C.N. and Van Wassenhove, L.N. (1985) A branch and bound algorithm for total weighted tardiness problem. Operations Re- search, 33, 363-377.
  7. Potts, C.N. and Van Wassenhove, L.N. (1991) Single machine tardi- ness sequencing heuristics. IIE Transactions, 23, 346-354.
  8. Rinnooy Kan, A.H.G., Lageweg, B.J. and Lenstra, J.K. (1975) Mini- mizing total costs in one-machine scheduling. Operations Re- search, 23, 908-927.
  9. Storer, R.H., Wu, S.D. and Vaccari, R. (1992) New search spaces for sequencing problems with application to job shop scheduling. Management Science, 38(10), 1495-1509.
  10. Storer, R.H., Wu, S.D. and Vaccari, R. (1995) Local search in problem and heuristic space for job shop scheduling. ORSA Journal on Computing, 7(4), 453-467.
  11. Biographies Selcuk Avci is a research staff member in the CASTLE Laboratory in the Department of Operations Research and Financial Engineering at Princeton University. He received his B.S. in Mechanical Engineering from the Middle East Technical University, Turkey, M.S. in Industrial Engineering from the Bilkent University, Turkey, and Ph.D. in In- dustrial Engineering from Lehigh University. His current research in- terests include production planning and scheduling, transportation planning and logistics, inventory theory and modern optimization heuristics.
  12. M. Selim Akturk is an Associate Professor of Industrial Engineering at Bilkent University, Turkey. He holds a Ph.D. in Industrial Engineering from Lehigh University, USA and B.S.I.E. and M.S.I.E. degrees from the Middle East Technical University, Turkey. His current research interests include hierarchical planning of large scale systems, produc- tion scheduling, cellular manufacturing systems, and advanced manu- facturing technologies. He is a senior member of IIE and member of INFORMS.
  13. Robert H. Storer is Professor of Industrial and Systems Engineering, Co-Director of the Manufacturing Logistics Institute, and Co-Director of the Integrated Business and Engineering Honors Program at Lehigh University. He received his B.S in Industrial and Operations Engi- neering from the University of Michigan in 1979, and M.S. and Ph.D. degrees in Industrial and Systems Engineering from the Georgia In- stitute of Technology in 1982 and 1986 respectively. His interests lie in operations research and applied statistics with particular interest in heuristic optimization, scheduling and logistics. Contributed by the Scheduling Department