Solving multi-objective chance constrained programming problem involving three parameters log normal distribution

2021 ◽  
Vol 18 (2) ◽  
pp. 236
Author(s):  
Srikumar Acharya ◽  
Berhanu Belay ◽  
Rajashree Mishra
Keyword(s):  
Normal Distribution ◽  
Programming Problem ◽  
Chance Constrained Programming ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming ◽  
Log Normal ◽  
Log Normal Distribution

scholarly journals Chance Constrained Programming Models for Risk-Based Economic and Policy Analysis of Soil Conservation

1994 ◽  
Vol 23 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Minkang Zhu ◽  
Daniel B. Taylor ◽  
Subhash C. Sarin ◽  
Randall A. Kramer
Keyword(s):  
Soil Conservation ◽  
Soil Loss ◽  
Programming Models ◽  
Chance Constrained Programming ◽  
Policy Makers ◽  
Trade Offs ◽  
Constrained Programming ◽  
Log Normal ◽  
Random Nature ◽  
Log Normal Distribution

The random nature of soil loss under alternative land-use practices should be an important consideration of soil conservation planning and analysis under risk. Chance constrained programming models can provide information on the trade-offs among pre-determined tolerance levels of soil loss, probability levels of satisfying the tolerance levels, and economic profits or losses resulting from soil conservation to soil conservation policy makers. When using chance constrained programming models, the distribution of factors being constrained must be evaluated. If random variables follow a log-normal distribution, the normality assumption, which is generally used in the chance constrained programming models, can bias the results.

scholarly journals Multi-Objective Chance Constrained Programming of Spare Parts Based on Uncertainty Theory

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 50049-50054 ◽  
Author(s):  
Yi Yang ◽  
Kunlun Wei ◽  
Rui Kang ◽  
Sixin Wang
Keyword(s):  
Uncertainty Theory ◽  
Spare Parts ◽  
Chance Constrained Programming ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming

scholarly journals A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

Water ◽  
2017 ◽  
Vol 9 (5) ◽  
pp. 322
Author(s):  
Xujun Liu ◽  
Mengjiao Zhang ◽  
Han Su ◽  
Feifei Dong ◽  
Yao Ji ◽  
...  
Keyword(s):  
Programming Approach ◽  
Chance Constrained Programming ◽  
Load Reduction ◽  
Watershed Scale ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming

scholarly journals Multi-objective chance-constrained blending optimization of zinc smelter under stochastic uncertainty

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yu Chen ◽  
Yonggang Li ◽  
Bei Sun ◽  
Chunhua Yang ◽  
Hongqiu Zhu
Keyword(s):  
Chance Constrained Programming ◽  
Analytic Hierarchy ◽  
Pareto Solution ◽  
Hybrid Intelligent Algorithm ◽  
Multi Objective ◽  
Roasting Process ◽  
Objective Chance ◽  
Constrained Programming ◽  
Hierarchy Process ◽  
Production Probability

<p style='text-indent:20px;'>Considering the uncertainty of zinc concentrates and the shortage of high-quality ore inventory, a multi-objective chance-constrained programming (MOCCP) is established for blending optimization. Firstly, the distribution characteristics of zinc concentrates are obtained by statistical methods and the normal distribution is truncated according to the actual industrial situation. Secondly, by minimizing the pessimistic value and maximizing the optimistic value of object function, a MOCCP is decomposed into a MiniMin and MaxiMax chance-constrained programming, which is easy to handle. Thirdly, a hybrid intelligent algorithm is presented to obtain the Pareto front. Then, the furnace condition of roasting process is established based on analytic hierarchy process, and a satisfactory solution is selected from Pareto solution according to expert rules. Finally, taking the production data as an example, the effectiveness and feasibility of this method are verified. Compared to traditional blending optimization, recommended model both can ensure that each component meets the needs of production probability, and adjust the confident level of each component. Compared with the distribution without truncation, the optimization results of this method are more in line with the actual situation.</p>

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