- 1.Campbell, H.G., R. A. Dudek, and M. L. Smith, "A Heuristic Algorithm of the n Job, m Machine Sequencing Problem," Management Science, Vo. 16, n In June, loTn n~n_nn~7Google Scholar
- 2.Cleveland, G. A. and S. F. Smith, "Using Genetic Algorithms to Schedule Flow Shop Releases," In Proceedings of the Third International Conference on Genetic ,41gorithms and Their Applications, Arlington, Va, i 989, pp. i 60- i 69. Google ScholarDigital Library
- 3.Dannenbring, D. G., "An Evaluation of FIowshop en,encino l-le.rinticn" Mnnnm~mont ?~',iont.o Vnl 23, No 11, 1977, pp. I i 74- l 182.Google Scholar
- 4.Davis, L., "Job Shop Scheduling with Genetic Algorithms," In Proceedings of the First International Conj~rence on Genetic Algorithms and Their rlusourgn, pp. 136-140. Google ScholarDigital Library
- 5.DelrAmico, M. and M. Trubian, "Applying Tabu Search to the Job-Shop Scheduling _P?ob!__em__~"/Innnln of Operations Research,, 41, 1993, pp. 231-252. Google ScholarDigital Library
- 6.Glover, F., "Tabu Search-Part !,' ORSA Journal on Computing, Vol. 1, No. 3, Summer 1989, pp. 190- 206.Google Scholar
- 7.Glover, F., "Tabu Search-Part II, ORSA Journal on Computing, Vol. 2, No. I, Winter 1990, pp. 4-32.Google ScholarCross Ref
- 8.G!over, E., "Tahu Search Fundamenmla and I I_nL~," Working Paper, University of Colorado at Boulder, May 1994.Google Scholar
- 9.Goldberg, D. and R. Lingle, "Alleles, Loci, and the Traveling Salesman Problem," In Proceeding of the First international Conference on Genetic Aigorithms and Their Applications, Pittsburgh, PA, 1985, pp. !54-!59. Google ScholarDigital Library
- 10.Goldberg, D., Genetic Algorithms in Search, Optimization & Machine Learning, Addison Wesley Inc., i 989. Google ScholarDigital Library
- 11.Gupta, J. N. D., 'A Functional Heuristic Algorithm for the Flow-Shop Scheduling Problem," Operations Research, 22(i ), 1971, pp. 39-48.Google Scholar
- 12.Holland, J. H., Adaptation in Natural and Artificial Systems, University of' Michigan Press, Ann Arbor, Michigan, 1975. Google ScholarDigital Library
- 13.Johnson, S. M., "Optimal Two and Three-Stage Production Schedules with Setup Times Included," Naval Research Logistics Quarterly, 1 (1), 1954, pp. 61-68.Google ScholarCross Ref
- 14.Kirkpatrick, S., C. D. Gelatt, and M. P. Vecchi, "Optimization by Simulated Annealing," Science, Vol. 220, 1983, pp. 671-680.Google ScholarCross Ref
- 15.Lee, I., R. Sikora, and M. Shaw, "A Genetic Algorithm-Based Approach to Flexible Flow-Line Scheduling," IEEE Transactions on Systems, Man, and Cybernetics-Part B. Cybernetics, Vol. 27, No. 1, 1997, pp. 36-54. Google ScholarDigital Library
- 16.Malek, M., M. Guruswamy, and M. Pandya,"Seriai and Parallel Simulated Annealing and Tabu Search Algorithms for the Traveling Salesman Problem," Annals of Operations Research, 21, 1989, pp. 59- 84. Google ScholarDigital Library
- 17.Matsuo, H., C. J. Sub, and R. S. Sullivan, "A Controlled Search Simulated Annealing Method for the Single Machine Weighted Tardiness Problem," Annals of Operations Research, 21, 1989, pp. 85-108. Google ScholarDigital Library
- 18.Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller, "Equation of State Calculations by Fast Computing Machines," Journal of Chemical Physics, 21, 1953, pp. 1087-1092.Google ScholarCross Ref
- 19.Ogbu, F. A. and D. K. Smith, "The Application of the Simulated Annealing Algorithm to the Solution of the n/m/Cmax Flowshop Problem," Computers and Operations Research, Vol. 17, No. 3, 1990, pp. 243- 253. Google ScholarDigital Library
- 20.Oliver, I. M., D. J. Smith, and J. R. C. Holland, "A Study of Permutation Crossover Operators on the Traveling Salesman Problem," in Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, 1987, pp. 224- 230. Google ScholarDigital Library
- 21.Osman, I. H. and C. N. Potts, "Simulated Annealing for Permutation Flow-Shop Scheduling," OMEGA, Vol. 17, No. 6, 1989, pp. 551-557.Google ScholarCross Ref
- 22.Palmer, D. S., "Sequencing Jobs through a Multistage Process in the Minimum Total Time - A Quick Method of Obtaining a Near Optimum," Operations Research Quarterly, 16, 1965, pp. 10 I- 107.Google ScholarCross Ref
- 23.Perttunen, J, 1994, On the significance of the initial solution in traveling salesman heuristics. Journal of the Operational Research Society, Vol. 45, October, 1131-1140.Google Scholar
- 24.Sikora, R., ~A Multi-Agent Framework for Coordination and System Integration," Ph.D. Dissertation, University of lllinois at Urbana- Champaign, 1994. Google ScholarDigital Library
- 25.So, Kut C. and Scott, Carlton H., "Optimal Production Sequence for a Product with Matching Components," Operations Research, Vol. 42, July/Aug. 1994, pp. 694-708.Google ScholarDigital Library
- 26.Syswerda, G. and J. Palmucci, "The Application of Genetic Algorithms to Resource Scheduling," In Proceedings of the Fourth International Conference on Genetic Algorithms and Their Applications, San Diego, CA, 1991, pp. 502-508.Google Scholar
- 27.Taillard, E. D., "Some Efficient Heuristic Methods for the Flow Shop Sequencing Problem," European Journal of Operational Research, 47, 1990, pp. 65- 74.Google ScholarCross Ref
- 28.Taillard, E. D., "Parallel Taboo Search Techniques for the Job Shop Scheduling Problem," ORSA Journal on Computing, Vol. 6, No. 2, Spring 1994, pp. 108-117.Google ScholarCross Ref
- 29.Van Laarhoven, P. J. M., and E. H. L. Aans, Simulated Annealing: Theory and Applications, D. Reidel, Dordrecht, 1987. Google ScholarDigital Library
- 30.Whitley, D., T. Starkweather, and D'Ann Fuquay, "Scheduling Problems and Traveling Salesman:," In Proceedings of the Third Internal Conference on Genetic Algorithms and Their Applications, 1989, pp. 133-140. Google ScholarDigital Library
- 31.Yano, Candace Arai and Rachamadugu, Ram, 1991, Sequencing to minimize work overload in assembly lines with product options. Management Science, Vol. 37, May, 572-586. Google ScholarDigital Library
Index Terms
- Artificial intelligence search methods for multi-machine two-stage scheduling
Recommendations
Improving simulated annealing with variable neighborhood search to solve the resource-constrained scheduling problem
The purpose of this paper is to improve the simulated annealing method with a variable neighborhood search to solve the resource-constrained scheduling problem. We also compare numerically this method with other neighborhood search (local search) ...
Comparing Three Heuristic Search Methods for Functional Partitioning in Hardware–Software Codesign
This paper compares three heuristic search algorithms: genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for hardware–software partitioning. The algorithms operate on functional blocks for designs represented as directed ...
Unrelated parallel machine scheduling using local search
Simulated annealing and taboo search are well-established local search methods for obtaining approximate solutions to a variety of combinatorial optimization problems. More recently, genetic algorithms have also been applied. However, there are few ...
Comments