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 JEP  Vol.8 No.3 , March 2017
An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method
Abstract: In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.
Cite this paper: Jin, W. , Hu, Z. and Chan, C. (2017) An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method. Journal of Environmental Protection, 8, 231-249. doi: 10.4236/jep.2017.83018.
References

[1]   Anderson, L. (1968) A Mathematical Model for the Optimization of a Waste Management System. Technical Report 68-1, Sanitary Engineering Research Laboratory, University of California, Berkely.

[2]   Christensen, H. and Haddix, G. (1974) A Model for Sanitary Landfill Management and Decision. Computers and Operation Research, 1, 275-281.
https://doi.org/10.1016/0305-0548(74)90052-5

[3]   Fuertes, L., Hundson, J. and Mark, D. (1974) Solid Waste Management: Equity Trade-Off Models. Journal of Urban Planning and Development, 100, 155-171.

[4]   Jenkins, L. (1982) Parametric Mixed Integer Programming: An Application to Solid Waste Management. Management Science, 28, 1271-1284.
https://doi.org/10.1287/mnsc.28.11.1270

[5]   Jacobs, T. and Everett, J. (1992) Optimal Scheduling of Landfill Operations Incorporating Recycling. Journal of Environmental Engineering, 118, 420-429.
https://doi.org/10.1061/(ASCE)0733-9372(1992)118:3(420)

[6]   Badran, M. and El-Haggar, S. (2006) Optimization of Municipal Solid Waste Management in Port Said-Egypt. Waste Management, 26, 534-545.
https://doi.org/10.1016/j.wasman.2005.05.005

[7]   Sushi, A. and Vart, P. (1989) Waste Management Policy Analysis and Growth Monitoring: An Integrated Approach to Perspective Planning. International Journal of Systems Science, 20, 907-926. https://doi.org/10.1080/00207728908910180

[8]   Chang, N. (1996) A Grey Fuzzy Multiobjective Programming Approach for the Optimal Planning of a Reservoir Watershed, Part A: Theoretical Development. Water Research, 30, 2329-2340. https://doi.org/10.1016/0043-1354(96)00124-8

[9]   Chang, N., Shoemaker, C. and Schuler, R. (1996) Solid Waste Management System Analysis with Air Pollution and Leachate Impact Limitations. Waste Management and Research, 14, 463-481. https://doi.org/10.1177/0734242X9601400505

[10]   Huang, G., Baetz, B. and Patry, G. (1995) Grey Integer Programming: An Application to Waste Management Planning under Uncertainty. European Journal of Operational Research, 83, 594-620. https://doi.org/10.1016/0377-2217(94)00093-R

[11]   Huang, G., Baetz, B. and Patry, G. (1995) Grey Quadratic Programming and Its Application to Municipal Waste Management Planning under Uncertainty. Engineering Optimization, 23, 201-223. https://doi.org/10.1080/03052159508941354

[12]   Li, Y. and Huang, G. (2009) Dynamic Analysis for Solid Waste Management Systems: An Inexact Multistage Integer Programming Approach. Journal of the Air and Waste Management Association, 59, 279-292.
https://doi.org/10.3155/1047-3289.59.3.279

[13]   Huang, G. and Cai, Y. (2010) A Superiority-Inferiority-Based Inexact Fuzzy Stochastic Programming Approach for Solid Waste Management under Uncertainty. Environmental Modeling and Assessment, 15, 381-396.
https://doi.org/10.1007/s10666-009-9214-6

[14]   Ekmekçioglu, M., Kaya, T. and Kahraman, C. (2010) Fuzzy Multicriteria Disposal Method and Site Selection for Municipal Solid Waste. Waste Management, 30, 1729-1737. https://doi.org/10.1016/j.wasman.2010.02.031

[15]   Piresa, A., Martinho, G. and Chang, N. (2001) Solid Waste Management in European Countries: A Review of Systems Analysis Techniques. Journal of Environmental Management, 92, 1033-1050. https://doi.org/10.1016/j.jenvman.2010.11.024

[16]   Beliën, J., De Boeck, L. and Van Ackere, J. (2012) Municipal Solid Waste Collection and Management Problems: A Literature Review. Transportation Science, 48, 78-102. https://doi.org/10.1287/trsc.1120.0448

[17]   Or, I. and Curi, K. (1993) Improving the Efficiency of the Solid Waste Collection System in Izmir, Turkey, through Mathematical Programming. Waste Management & Research, 11, 297-311. https://doi.org/10.1177/0734242X9301100404

[18]   Sun, W., Huang, G., Lv, Y. and Li, G. (2013) Inexact Joint-Probabilistic Chance-Constrained Programming with Left-Hand-Side Randomness: An Application to Solid Waste Management. European Journal of Operational Research, 228, 217-225.
https://doi.org/10.1016/j.ejor.2013.01.011

[19]   Chang, N., Schuler, R. and Shoemaker, C. (1993) Environment and Economic Optimization of an Integrated Solid Waste Management System. Journal of Resource Management and Technology, 21, 87-100.

[20]   Huang, G., Baetz, B. and Patry, G. (1993) A Grey Fuzzy Linear Programming Approach for Municipal Solid Waste Management Planning under Uncertainty. Civil Engineering Systems, 10, 123-146. https://doi.org/10.1080/02630259308970119

[21]   Huang, G., Baetz, B. and Patry, G. (1994) Grey Dynamic Programming for Solid Waste Management Planning Under Uncertainty. Journal of Urban Planning and Development, 120, 132-156.
https://doi.org/10.1061/(ASCE)0733-9488(1994)120:3(132)

[22]   Huang, G., Baetz, B. and Patry, G. (1994) Waste Flow Allocation Planning through a Grey Fuzzy Quadratic Programming Approach. Civil Engineering Systems, 11, 209-243. https://doi.org/10.1080/02630259408970147

[23]   Jin, W., Hu, Z. and Chan, C. (2013) A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty. Mathematical Problems in Engineering, 2013, Article ID: 272491.
http://www.hindawi.com/journals/mpe/2013/272491/
https://doi.org/10.1155/2013/272491


[24]   Chen, M. and Huang, G. (2001) A Derivative Algorithm for Inexact Quadratic Program-Application to Environmental Decision-Making under Uncertainty. European Journal of Operational Research, 128, 570-586.
https://doi.org/10.1016/S0377-2217(99)00374-4

[25]   Callan, S. and Thomas. J. (2001) Economies of Scale and Scope: A Cost Analysis of Municipal Solid Waste Services. Land Economics, 77, 548-560.
https://doi.org/10.2307/3146940

[26]   Melanie. M. (1998) An Introduction of Genetic Algorithms, The MIT Press, Cambridge.

[27]   Houck, C., Joines, J. and Kay, M. (1995) A Genetic Algorithm for Function Optimization: A Matlab Implementation. NCSU-IE TR 95.09.

[28]   Winston, W. (2003) Operation Research: Applications and Algorithms. Duxbury Press, Boston.

[29]   Michalewicz, Z. (1998) Genetic Algorithm + Data Structures = Evolution Programs. 3rd Revised and Extended Edition, Springer, Berlin.

[30]   Gen, M. and Cheng, R. (1997) Genetic Algorithm and Engineering Design. Wiley, New York.

[31]   Joines, J. and Houck, C. (1994) On the Use of Non-Stationary Penalty Functions to Solve Constrained Optimization Problems with Genetic Algorithms. Proceedings of the 1st IEEE Conference on Evolutionary Computation, Orlando, 27-29 June 1994, 579-584.

 
 
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