A decision support model for scheduling exhibition projects in art museums

Expert Systems with Applications

Volumn 37, Issue 2, 2010, Pages 919-925

  Lee, Hsin Yun ^a   Lin, Yu Cheng ^b  

^a NATIONAL ILAN UNIVERSITY   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Art museum; Decision support; Exhibition; Genetic algorithms; Scheduling

Indexed keywords

DECISION MAKING; DECISION SUPPORT SYSTEMS; GENETIC ALGORITHMS; MUSEUMS; SCHEDULING; SCHEDULING ALGORITHMS;

ART MUSEUMS; COMPLEX COMPUTATION; DECISION MAKING PROCESS; DECISION SUPPORT MODELS; DECISION SUPPORTS; OPTIMAL DECISIONS; PLANNING TIME; PRIORITY VALUES;

EXHIBITIONS;

EID: 71749083759     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.03.003     Document Type: Article

References (18)

1. Abdennadher S., and Marte M. University course timetabling using constraint handling rules. Applied Artificial Intelligence 14 4 (2000) 311-325
2. Abdullah S., Ahmadi S., Burke E.K., Dror M., and McCollum B. A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem. Journal of the Operational Research Society 58 11 (2007) 1494-1502
3. Ames, P. J. (1994). Measuring museums' merits (pp. 22-30). Routledge, London, UK.
4. Carrasco, M. P., & Pato, M. V. (2001). A multi-objective genetic algorithm for the class/teacher timetabling problem. Lecture notes in computer science (Vol. 2079, pp. 3-17). Berlin, German: Springer.
5. Collingwood E., Corne D., and Ross P. Useful diversity via multiploidy. Proceedings of International Conference on Evolutionary Computing (1996) 810-813
6. Daskalaki S., Birbas T., and Housos E. An integer programming formulation for a case study in university timetabling. European Journal of Operational Research 153 1 (2004) 117-135
7. Dean, D. (1994). Museum exhibition: theory and practice (pp. 82-84). London, UK: Routledge.
8. Dzeng R.J., and Lee H.Y. Activity and value orientated decision support for the development planning of a theme park. Expert Systems with Applications 33 4 (2007) 923-935
9. Eley, M. (2006). Some experiments with Ant colony algorithms for the exam timetabling problem. In Proceedings of 5th international workshop on ant colony optimization and swarm intelligence (ANTS 2006) (pp. 492-499).
10. Farley, A. M. (2007). Choice and development. In Proceedings of the 2007 GECCO (genetic and evolutionary computation conference) conference (pp. 2468-2474).
11. Fonteix C., Bicking F., Perrin E., and Marc I. Haploid and diploid algorithms, a new approach for global optimization: compared performances. International Journal of Systems Science 26 10 (1995) 1919-1933
12. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addition-Wesley.
13. Holland J. Adaptation in natural and artificial system (1975), University of Michigan Press, Ann Arbor, MI
14. Petrovic S., Yang Y., and Dror M. Case-based selection of initialisation heuristics for metaheuristic examination timetabling. Expert Systems with Applications 33 3 (2007) 772-785
15. Smith K.A., Abramson D., and Duke D. Hopfield neural networks for timetabling: Formulations, methods, and comparative results. Computers & Industrial Engineering 44 2 (2003) 283-305
16. Ueda H., Ouchi D., Takahashi K., and Miyahara T. Comparisons of genetic algorithms for timetabling problems. Systems and Computers in Japan 35 7 (2004) 691-701
17. Wu, M. D. (2002). Evolution of complex structures: Employing structural adaptation and polyploidy in solving multi-state problems. PhD Thesis, Department of Computer and Information Science, National Chiao Tung University, Taiwan.
18. Wu M.D., and Sun C.T. Fuzzy modeling employing fuzzy polyploidy genetic algorithms. Journal of Information Science and Engineering 18 (2002) 163-186