Fuzzy-logic-based decision support system for scheduling tillage operations

Engineering Applications of Artificial Intelligence

Volumn 10, Issue 5, 1997, Pages 463-472

  Thangavadivelu, Suri ^a,c   Colvin, Tom S ^b  

^a KANSAS STATE UNIVERSITY   (United States)

^b USDA ARS National Laboratory for Agriculture and the Environment   (United States)

^c Sunflower Networking Group   (United States)

Author keywords

Decision support systems; Fuzzy logic; Scheduling; Tillage management

Indexed keywords

AGRICULTURAL ENGINEERING; C (PROGRAMMING LANGUAGE); CULTIVATION; FUZZY SETS; INFERENCE ENGINES; SCHEDULING;

SOFTWARE PACKAGE FLODSS; TILLAGE MANAGEMENT;

DECISION SUPPORT SYSTEMS;

EID: 0031246538     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0952-1976(97)00023-7     Document Type: Article

References (20)

1. Babeir, A. S., Colvin, T. S. and Marley, S. J. (1986) Predicting field tractability with a simulation model. Transactions of the ASAE, 29(6), 1520-1525.
2. Bellman, R. E. and Zadeh, L. A. (1970) Decision making in a fuzzy environment. Management Science, 17, 141-164.
3. Charnaik, E. and McDermott, D. (1986) Introduction to Artificial Intelligence. Addison Wesley, Reading, Massachusetts.
4. Jia, L.-M. and Zhang, X.-D. (1994) Distributed intelligent railway traffic control: A fuzzy decision making approach. Engineering Applications of Artificial Intelligence, 7(3), 311-319.
5. Kandel, A. (1986) Fuzzy Mathematical Techniques with Applications, Addison Wesley, Reading, Massachusetts.
6. Kanwar, R. S. (1981) Hydrologic simulation of nitrate losses with drainage water. Unpublished Ph.D. dissertation, Iowa State University, Ames, Iowa.
7. Kanwar, R. S., Johnson, H. P. and Baker, J. L. (1983) Comparison of simulated and measured nitrate losses in tile effluent. Transactions of the ASAE, 26, 1451-1457.
8. Kaufmann, A. and Gupta, M. M. (1988) Introduction to Fuzzy Arithmetic, Van Nostrand Reinhold, New York.
9. King, R. E., Magoulas, G. D. and Stathaki, A. A. (1994) Multivariable fuzzy controller disign. Control Engineering Practice, 2(3), 431-437.
10. Klir, G. J. (1995) Fuzzy logic unearthing: its meaning and significance. IEEE Potentials, 14(4), 10-15.
11. Munakata, T. and Jani, Y. (1994) Fuzzy systems: an overview. Communications of the ACM, 37(3), 69-76.
12. Nakashime, K. and Takagi, N. (1993) Fundamental properties of multiple valued logic functions monotonic with respect to ambiguity. IEICE Transactions on Information and Systems, E76-D(5), 540-547.
13. Rinks, D. B. (1982) The Performance of Fuzzy Algorithm Models for Aggregate Planning and Differing Cost Structures. Approximate Reasoning in Decision Analysis, North Holland, New York.
14. Shaw, R. H. (1963) Estimation of soil moisture under corn. Iowa Agricultural and Home Economics Experiment Station Research Bulletin, #520.
15. Thangavadivelu, S. and Colvin, T. S. (1991) Decision analysis using linguistic approximation in an agricultural environment. Proceedings of the SCS Simulation Multiconference on Al and Simulation, New Orleans.
16. Thornthwaite, G. W. (1948) An approach towards a rational classification of climate. Geographical Review, 3, 55-94.
17. Tong, R. M. and Bonissone, P. P. (1984) Linguistic solutions to fuzzy decision problems. IMS/Studies in the Management Sciences, 20, 323-334.
18. Yager, R. R. (1978) Fuzzy decision making including unequal objectives. Fuzzy sets and systems, 1, 87-95.
19. Yager, R. R. (1981) A new methodology for ordinal multi-objective decisions based on fuzzy sets. Decision Sciences, 12, 589-600.
20. Zimmermann, H.-J. (1985) Fuzzy Set Theory - and its Applications, Kluwer-Nijhoff, Boston.