A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments

Expert Systems with Applications

Volumn 16, Issue 2, 1999, Pages 121-133

  Pendharkar, P C ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Distributed artificial intelligence; Genetic algorithms

Indexed keywords

EID: 0001027563     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0957-4174(98)00066-9     Document Type: Article

References (44)

1. Atlan, L., Bonnet, J., Naillon, M. (1993). Learning distributed reactive strategies by genetic programming for the general job shop problem. Proceedings of the Seventh Annual Florida Artificial Intelligence Research Symposium.
2. Aiba, H., & Terano, T. (1996). A computational model for distributed knowledge systems with learning mechanisms. Expert Systems with Applications, 10 (3/4), 417-427.
3. Aytug, H., Koehler, G. J., & Snowdon, J. L. (1994). Genetic learning of dynamic scheduling within a simulation environment. Computers and Operations Research, 21 (8), 909-925.
4. Aytug, H., Bhattacharyya, S., Koehler, G. J., & Snowdon, J. L. (1994). A review of machine learning in scheduling. IEEE Transactions on Engineering Management, 41 (2).
5. Bhattacharyya, S., Koehler, G.J. (1997). Learning by objectives for adap-tive shop-floor learning. Decision Sciences (to appear).
6. Bond, A. H., & Gesser, L. (eds.) (1998). Readings in distributed artificial intelligence. San Mateo, CA: Morgan Kaufmann.
7. Bull, L., Fogarty, T.C., & Snaith, M. (1995). Evolution in multi-agent systems: evolving communicating classifier systems for gait in a quadrupedal robot. International Conference on Genetic Algorithms.
8. Croce, F. D., Tadei, R., & Volta, G. (1995). A genetic algorithm for the job shop problem. Computers and Operations Research, 22 (1), 15-24.
9. Fox, B. R., & McMahon, M. B. (1991). Genetic Operators for Sequencing Problems. In G. E. Rawlins (Ed.), Foundations of Genetic Algorithms, (pp. 284). Morgan Kaufmann Pub.
10. Goldberg, D. E., & Lingle, R. (1991). Alleles, loci, and the travelling salesman problem. In J. J. Grefenstette (Ed.), Proceedings of First International Conference on Genetics and their Applications, (pp. 154). USA: Carnegie-Mellon University.
11. Graves, S. C. (1981). A review of production scheduling. Operations Research, 29, 646-675.
12. Hadavi, K., Hsu, W. -L., Chen, T., & Lee, C. (1992). An architecture for real-time distributed scheduling. AI Magazine, 46-56.
13. Haynes, T., Wainwright, R., Sen, S., & Schoenefeld, D. (1995). Strongly typed genetic programming in evolving cooperation strategies. International Conference on Genetic Algorithms.
14. Hillard, M. R., Liepins, G. E., Rangarajan, G., & Palmer, M. (1989). Learning decision rules for scheduling problems: a classifier hybrid approach. In A. M. Segre (Ed.), Proc. Sixth International Workshop on Machine Learning, (pp. 188). Ithaca, NY: Cornell University.
15. Hillard, M.R., Liepins, G.E., & Palmer, M. (1988). Machine learning applications to job shop scheduling. Proc. First International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, University of Tennessee Space Institute, June 1-3, ACM Press.
16. Hillard, M.R., Liepins, G.E., Palmer, Morrow, M., & Richardson, J. (1987). A classifier-based system for discovering scheduling heuristics, genetic algorithms and their applications. In: J.J. Grefenstette (ed.), Proc. Second International Conference on Genetic Algorithms (pp.231-235).
17. Husbands, P., Mill, F., & Warrington, S. (1991). Genetic algorithms, production plan optimization and scheduling. In H. Schwefel & R. Manner (Eds.), Parallel Problem Solving from Nature (p. 80). Lecture Notes in Computer Science, 496. Springer Verlag.
18. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence, 4, 237-285.
19. Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29, 51-59.
20. Lee, C. Y., & Choi, J. Y. (1995). A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights. Computers and Operations Research, 22 (8), 857-869.
21. Moulin, B., & Chaib-Draa, B. (1996). An overview of distributed artificial intelligence. In O'Hare & Jennings (eds.), Foundations of distributed artificial intelligence (pp. 3-55).
22. Nordstrom, A.L., & Tufeekci, S. (1995). A Genetic Algorithm for the Talent Scheduling Problem. Computers and Operations Research, 21 (8), 927-940.
23. Oliver, I.M., Smith, D.J., & Holland, J.R.C. (1987). A study of permutation crossover operators on the travelling salesman problem. Genetic Algorithms and their Applications: Proceedings of the Second International Conference, (pp. 224-230).
24. Pendharkar, P.C. (1997). Multi-agent learning for adaptive scheduling, Doctoral Dissertation, Graduate School, Southern Illinois University at Carbondale.
25. Pendharkar, P.C. (1998). Distributed learning of objectives for adaptive scheduling, in review.
26. Pendharkar, P.C. & Bhattacharyya, S. (1997). Multi-Agent Learning in Distributed Artificial Intelligence. Proceedings of the 2nd INFORMS Conference on Information Systems and Technology, San Diego, CA. (pp.156-163).
27. Rao, H. R., Moore, J. C., Nam, K., Raghu, A., & Whinston, A. (1996). A comparison of three information gathering strategies in DAI systems under noisy conditions. Expert Systems With Applications, 11 (4), 489-505.
28. Reeves, C. R. (1995). A genetic algorithm for flowshop sequencing. Computers and Operations Research, 22 (1), 5-13.
29. Rich, E., & Knight, K. (1991). Artificial Intelligence. McGraw Hill Inc.
30. Schaerf, A., Shoham, Y., & Tennenholtz , M. (1995). Adaptive load balancing: a study in multi-agent learning. Journal of Artificial Intelligence Research, 475-500.
31. Shaw, M.J., & Whinston, A.B. (1989). Learning and adaptation in distributed artificial intelligence. In Distributed artificial intelligence (pp.413-429). London: Pitman.
32. Shaw, M.J. (1996). Cooperative problem-solving and learning in multiagent information systems. International Journal of Computational Intelligence and Organizations, 1 (1), 21-34.
33. Sian, S.S. (1991). Extending learning to multiple agents: issues and a model for multi-agent machine learning. Machine Learning - European Workshop Session on Learning. Porto, Portugal (pp. 440-456).
34. Skarek, P., & Varga, L.Z. (1996). Multi-agent cooperation for particle accelerator control. Expert Systems With Applications, 11 (4), 481-487.
35. Smith, S.F. (1980). A Learning System Based on Genetic Adaptive Algorithms, Ph.D. Dissertation, University of Pittsburgh.
36. Sycara, K., Rothm, S., Sadeh, N., & Fox, M. (1990). An investigation into distributed constraint directed factory scheduling. Sixth IEEE Conference on AI Applications, March, Santa Barbara, CA.
37. Syswerda, G. (1991). Schedule optimization using genetic algorithms. In L. Davis (Ed.), Handbook of Genetic Algorithms, (pp. 333). Van Nostrand Reinhold Ch. 13.
38. Weiss, G. (1995). Adaptation and learning in multi-agent systems: Some remarks and a bibliography. Proc. IJCAI' 95 Workshop Montreal, Canada.
39. Weiss, G. (1992). Action selection and learning in multi-agent environments. Proc. Second International Conference on Simulation of Adaptive Behavior.
40. Weiss, G. (1994). Learning to coordinate actions in multi-agent systems. Distributed Artificial Intelligence, 311-316.
41. Wren, A., & Wren, D. O. (1995). A genetic algorithm for public transportation scheduling. Computers and Operations Research, 22, 101-110.
42. Whitley, L. D., Starkweather, T., & Shaner, D. (1991). The travelling salesman and sequence scheduling: quality solutions using genetic edge recombination. In L. Davis (Ed.), Handbook of Genetic Algorithms, (pp. 351). Van Nostrand Reinhold Ch. 13.
43. Whitley, L.D., Starkweather, T., & Fuquay, D. (1989). Scheduling problems and the travelling salesman: The genetic edge recombination operator. Proc. Third International Conference on Genetic Algorithms (pp.133-140). San Mateo, CA: Morgan Kaufmann.
44. Yano, C. A., & Kim, Y. D. (1991). Algorithms for a class of single-machine weighted tardiness and earliness problems. European Journal of Operational Research, 52, 167-178.