Copyright © 2012 Liming Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

1. M. Al-Jabari and H. Sawalha, “Treating stone cutting waste by flocculation-sedimentation,” in Proceedings of the Sustainable Environmental Sanitation and Water Services Conference, 28th WEDC Conference, Calcutta, India, 2002.
2. K. Nasserdine, Z. Mimi, B. Bevan, and B. Elian, “Environmental management of the stone cutting industry,” Journal of Environmental Management, vol. 90, no. 1, pp. 466–470, 2009. View at Publisher · View at Google Scholar · View at Scopus
3. N. Almeida, F. Branco, and J. R. Santos, “Recycling of stone slurry in industrial activities: application to concrete mixtures,” Building and Environment, vol. 42, no. 2, pp. 810–819, 2007. View at Publisher · View at Google Scholar · View at Scopus
4. V. Corinaldesi, G. Moriconi, and T. R. Naik, “Characterization of marble powder for its use in mortar and concrete,” Construction and Building Materials, vol. 24, no. 1, pp. 113–117, 2010. View at Publisher · View at Google Scholar · View at Scopus
5. F. Saboya Jr., G. C. Xavier, and J. Alexandre, “The use of the powder marble by-product to enhance the properties of brick ceramic,” Construction and Building Materials, vol. 21, no. 10, pp. 1950–1960, 2007. View at Publisher · View at Google Scholar · View at Scopus
6. N. Bilgin, H. A. Yepremb, S. Arslan, et al., “Use of waste marble powder in brick industry,” Construction and Building Materials, vol. 29, pp. 449–457, 2012. View at Publisher · View at Google Scholar
7. A. Singh and H. H. Lou, “Hierarchical pareto optimization for the sustainable development of industrial ecosystems,” Industrial and Engineering Chemistry Research, vol. 45, no. 9, pp. 3265–3279, 2006. View at Publisher · View at Google Scholar · View at Scopus
8. S. R. Lim and M. P. Jong, “Cooperative water network system to reduce carbon footprint,” Environmental Science and Technology, vol. 42, no. 16, pp. 6230–6236, 2008. View at Publisher · View at Google Scholar · View at Scopus
9. D. Petrovic, R. Roy, and R. Petrovic, “Supply chain modelling using fuzzy sets,” International Journal of Production Economics, vol. 59, no. 1, pp. 443–453, 1999. View at Publisher · View at Google Scholar · View at Scopus
10. H. M. Lee and J. S. Yao, “Economic production quantity for fuzzy demand quantity and fuzzy production quantity,” European Journal of Operational Research, vol. 109, no. 1, pp. 203–211, 1998. View at Scopus
11. R. E. Bellman and L. A. Zadeh, “Decision-making in a fuzzy environment,” Management Science, vol. 17, pp. B141–B164, 1970.
12. S. R. Hejazi, A. Memariani, G. Jahanshahloo, and M. M. Sepehri, “Linear bilevel programming solution by genetic algorithm,” Computers & Operations Research, vol. 29, no. 13, pp. 1913–1925, 2002. View at Publisher · View at Google Scholar
13. M. Al-Salamah, “Temporary workforce planning with firm contracts: a model and a simulated annealing heuristic,” Mathematical Problems in Engineering, vol. 2011, Article ID 209693, 18 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
14. M. Gendreau, P. Marcotte, and G. Savard, “A hybrid tabu-ascent algorithm for the linear bilevel programming problem,” Journal of Global Optimization, vol. 8, no. 3, pp. 217–233, 1996. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
15. J. Li, J. Xu, and M. Gen, “A class of multiobjective linear programming model with fuzzy random coefficients,” Mathematical and Computer Modelling, vol. 44, no. 11-12, pp. 1097–1113, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
16. A. Suppapitnarm and G. T. Parks, Simulated Annealing: An Alternative Approach to True Multiobjective Optimization, Morgan Kaufmann, 1999.
17. J. S. Yao and F. T. Lin, “Constructing a fuzzy flow-shop sequencing model based on statistical data,” International Journal of Approximate Reasoning, vol. 29, no. 3, pp. 215–234, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
18. J. S. Yao and K. Wu, “Consumer surplus and producer surplus for fuzzy demand and fuzzy supply,” Fuzzy Sets and Systems, vol. 103, no. 3, pp. 421–426, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
19. C. J. Lin and U. P. Wen, “A labeling algorithm for the fuzzy assignment problem,” Fuzzy Sets and Systems, vol. 142, no. 3, pp. 373–391, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
20. J. Xu, L. Yao, and X. Zhao, “A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem,” Fuzzy Optimization and Decision Making, vol. 10, no. 3, pp. 255–285, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
21. J. Xu and Z. Zeng, “A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries,” Applied Mathematical Modelling,, vol. 36, no. 8, pp. 3513–3544, 2012. View at Publisher · View at Google Scholar
22. J. C. Bezdek, “Fuzzy models—what are they, and why?” IEEE Transactions on Fuzzy Systems, vol. 1, no. 1, pp. 1–6, 1993. View at Scopus
23. D. Dubois and H. Prade, Fuzzy Sets and Systems, Academic Press, New York, NY, USA, 1980.
24. U. Thole, H. J. Zimmermann, and P. Zysno, “On the suitability of minimum and product operators for the intersection of fuzzy sets,” Fuzzy Sets and Systems, vol. 2, no. 2, pp. 167–180, 1979. View at Scopus
25. B. Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, USA, 1992.
26. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” International Joint Conference on Artificial Intelligence, vol. 14, pp. 1137–1145, 1995.
27. J. Xu and X. Zhou, Fuzzy-Like Multiple Objective Decision Making, Springer, 2011.
28. D. Dubois and H. Prade, “Operations on fuzzy numbers,” International Journal of Systems Science, vol. 9, no. 6, pp. 613–626, 1978. View at Zentralblatt MATH
29. A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic, Van Nostrand Reinhold, New York, NY, USA, 1985.
30. J. F. Bard, “Some properties of the bilevel programming problem,” Journal of Optimization Theory and Applications, vol. 68, no. 2, pp. 371–378, 1991. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
31. P. Hansen, B. Jaumard, and G. Savard, “New branch-and-bound rules for linear bilevel programming,” Society for Industrial and Applied Mathematics, vol. 13, no. 5, pp. 1194–1217, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
32. O. Ben-Ayed and C. E. Blair, “Computational difficulties of bilevel linear programming,” Operations Research, vol. 38, no. 3, pp. 556–560, 1990. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
33. S. Kirkpatrick Jr., C. D. Gelatt,, and M. P. Vecchi, “Optimization by simulated annealing,” American Association for the Advancement of Science, vol. 220, no. 4598, pp. 671–680, 1983. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
34. S. Kirkpatrick, “Optimization by simulated annealing: quantitative studies,” Journal of Statistical Physics, vol. 34, no. 5-6, pp. 975–986, 1984. View at Publisher · View at Google Scholar
35. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of state calculations by fast computing machines,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953. View at Scopus
36. P. J. M. van Laarhoven, Theoretical and Computational Aspects of Simulated Annealing, vol. 51, Stichting Mathematisch Centrum Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands, 1988.
37. J. M. Steel, “A review of Laarhoven,” Journal of the American Statistical Association, vol. 85, pp. 596–596, 1990.
38. A. Suppapitnarm, K. A. Seffen, G. T. Parks, and P. J. Clarkson, “Simulated annealing algorithm for multiobjective optimization,” Engineering Optimization, vol. 33, no. 1, pp. 59–85, 2000. View at Scopus
39. B. Suman, “Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem,” Computers and Chemical Engineering, vol. 28, no. 9, pp. 1849–1871, 2004. View at Publisher · View at Google Scholar · View at Scopus
40. B. Suman and P. Kumar, “A survey of simulated annealing as a tool for single and multiobjective optimization,” Journal of the Operational Research Society, vol. 57, no. 10, pp. 1143–1160, 2006. View at Publisher · View at Google Scholar · View at Scopus
41. B. Sanghamitra, S. Sriparna, M. Ujjwal, and D. Kalyanmoy, “A simulated annealing-based multiobjective optimization algorithm: AMOSA,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 3, pp. 269–283, 2008. View at Publisher · View at Google Scholar · View at Scopus
42. A. Sadegheih, “Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance,” Applied Mathematical Modelling, vol. 30, no. 2, pp. 147–154, 2006. View at Publisher · View at Google Scholar · View at Scopus
43. T. Xu, H. Wei, and G. Hu, “Study on continuous network design problem using simulated annealing and genetic algorithm,” Expert Systems with Applications, vol. 36, no. 2, pp. 1322–1328, 2009. View at Publisher · View at Google Scholar · View at Scopus