Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-05T19:29:12.861Z Has data issue: false hasContentIssue false

A CLASS OF MIX DESIGN PROBLEMS: FORMULATION, SOLUTION METHODS AND APPLICATIONS

Published online by Cambridge University Press:  29 July 2009

Zhong Wan
Affiliation:
School of Mathematical Science and Computing Technology, Central South University, Hunan Changsha, PR China (email: [email protected])
K. L. Teo*
Affiliation:
Department of Mathematics and Statistics, Curtin University of Technology, Perth, Australia (email: [email protected])
LingShuang Kong
Affiliation:
School of Information Sciences and Engineering, Central South University, Hunan Changsha, PR China
Chunhua Yang
Affiliation:
School of Information Sciences and Engineering, Central South University, Hunan Changsha, PR China
*
For correspondence; e-mail: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper, we consider a linear program with only equality constraints but containing interval and random coefficients. We first address the linear program with interval coefficients, and establish some structural properties of this linear program. On this basis, a solution method is proposed. We then move on to consider the linear program with random coefficients. Using the chance constraint approach and a new approach, the satisfaction degree approach, we obtain the two respective deterministic equivalent formulations. Then the results and the numerical solution methods obtained for these two linear models are applied to the original linear problem which contains both interval and random coefficients. By way of illustration, we consider a practical problem, where the optimal mixing proportions need to be determined for the mix slurry in the production process of aluminium with sintering. This gives rise to a linear program with interval and random coefficients. Its deterministic equivalent formulations are presented. Preliminary numerical examples show that the proposed models and the solution methods are promising.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 2009

References

[1]Astier, J. E., “Main development in iron ore preparation”, in: The Second International Conference on the Iron Ore Industry (Luleå University, Kiruna, Sweden, 2002).Google Scholar
[2]Ben-Tal, A., Goryashko, A., Guslitzer, E. and Nemirovski, A., “Adjustable robust solutions of uncertain linear programs”, Math. Program. 99(2, Ser. A) (2004) 351376.CrossRefGoogle Scholar
[3]Berkelaar, A., Dert, C., Oldenkamp, B. and Zhang, S., “A primal-dual decomposition-based interior point approach to two-stage stochastic linear programming”, Oper. Res. 50 (2002) 904915.CrossRefGoogle Scholar
[4]Chang, P. K., “An approach to optimizing mix design for properties of high-performance concrete”, Cement Concrete Res. 34(4) (2004) 623629.CrossRefGoogle Scholar
[5]Chen, H. W., “Improvement on burden calculation for raw mix slurry in production of alumina with sintering process”, World Nonferrous Metals 12 (2001) 3641.Google Scholar
[6]Dentcheva, D., Lai, B. and Ruszczynski, A., “Dual methods for probabilistic optimization problems”, Math. Methods Oper. Res. 60 (2004) 331346.CrossRefGoogle Scholar
[7]Dentcheva, D., Prékopa, A. and Ruszczyński, A., “Concavity and efficient points of discrete distributions in probabilistic programming”, Math. Program. 89(1, Ser. A) (2000) 5577.CrossRefGoogle Scholar
[8]DePaolo, C. A. and Rader, J., “A heuristic algorithm for a chance constrained stochastic program”, European J. Oper. Res. 176 (2007) 2745.CrossRefGoogle Scholar
[9]Ermoliev, Y. and Wets, R. J.-B., Numerical techniques for stochastic optimization (Springer, Berlin, 1988).CrossRefGoogle Scholar
[10]Felekoğlu, B., Türkel, S. and Baradan, B., “Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete”, Building and Environment 42(4) (2007) 17951802.CrossRefGoogle Scholar
[11]Hsieh, L. H. and Whiteman, J. A., “Effect of raw material composition on the mineral phases in lime-fluxed iron ore sinter”, ISIJ International 33(4) (1993) 462473.CrossRefGoogle Scholar
[12]Ji, T., Lin, T. and Lin, X., “A concrete mix proportion design algorithm based on artificial neural networks”, Cement Concrete Res. 36(7) (2006) 13991408.CrossRefGoogle Scholar
[13]Kall, P., Stochastic linear programming (Springer, Berlin, 1976).CrossRefGoogle Scholar
[14]Lagoa, C. M., Li, X. and Sznaier, M., “Probabilistically constrained linear programs and risk-adjusted controller design”, SIAM J. Optim. 15 (2005) 938951.CrossRefGoogle Scholar
[15]Lim, C. H., Yoon, Y. S. and Kim, J. H., “Genetic algorithm in mix proportioning of high-performance concrete”, Cement Concrete Res. 34(3) (2004) 409420.CrossRefGoogle Scholar
[16]Lin, X. X., Janak, S. L. and Floudas, C. A., “New robust optimization approach for scheduling under uncertainty I: Bounded uncertainty”, Comput. Chemical Eng. 28 (2004) 10691085.CrossRefGoogle Scholar
[17]Luhandjula, M. K., “Fuzzy stochastic linear programming: Survey and future research directions”, European J. Oper. Res. 174(3) (2006) 13531367.CrossRefGoogle Scholar
[18]Mayer, J., Stochastic linear programming algorithms (Gordon and Breach, Amsterdam, 1998).Google Scholar
[19]Ruszczynski, A. and Shapiro, A., “Stochastic programming”, in: Handbook in operations research and management science (Elsevier, Amsterdam, 2003).Google Scholar
[20]Su, N., Hsu, K. and Chai, H., “A simple mix-design method for selfcompacting concrete”, Cement Concrete Res. 31 (2001) 17991807.CrossRefGoogle Scholar
[21]Vieira, M. T., Catarino, L., Oliveira, M., Sousa, J., Torralba, J. M., Cambronero, L. E. G., González-Mesones, F. L. and Victoria, A., “Optimization of the sintering process of raw material wastes”, J. Materials Processing Technology 93 (1999) 97101.CrossRefGoogle Scholar
[22]Wang, D. Q., “Application of linear programming in production of mixing materials to sinter”, China Metallurgy 15(8) (2005) 1922.Google Scholar
[23]Yang, C. H., Duan, X. G., Wan, Y. L. and Gui, W. H., “Blending expert system for raw mix slurry in production of alumina with sintering process”, J. Cent. South Univ. (Science and Technology) 36(4) (2005) 648652.Google Scholar
[24]Yang, X. K., Yang, C. H., Wang, Y. L. and Gui, W. H., “The application of improved genetic algorithm in raw mix slurry optimal arrangement of alumina process”, Computing Technology and Automation 25(1) (2006) 4446.Google Scholar
[25]Zain, M. F. M., Islam, Md. N. and Basri, Ir. H., “An expert system for mix design of high performance concrete”, Adv. Engineering Software 36(5) (2005) 325337.CrossRefGoogle Scholar