chance-constrained programming (CCP)
A Unified Approach for Multiobjective Fuzzy Chance Constrained Programming with Joint Normal Distribution
... stochastic programming (SP) [2] and also by Chance constrained programming (CCP) technique ...fuzzy programming (FP) method [4, ...
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Low Probability Identification Performance In Radar Network System By Using Fuzzy Chance-Constrained Programming
... fuzzy chance-constrained programming (FCCP) based security information optimization scheme is presented to achieve enhanced LPID performance in radar network systems, which focuses on minimizing the ...
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Project selection problem under uncertainty: An application of utility theory and chance constrained programming to a real case Pages 373-385 Download PDF
... on chance constrained programming and utility theory for a certain range of problems and under some practical ...Utility Programming, as the proposed modeling approach, will be compared with ...
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Slow Adaptive OFDMA Systems Through Chance Constrained Programming
... Meanwhile, the data rate requirements of individual users are accommodated on the fast timescale with high probability, thereby meeting the requirements except occasional outage. Such an objective has a natural ...
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Chance constraint problem having parameters as pareto random variables
... information. Chance constrained programming (CCP) has been used for modeling and analysis of risks in a number of application ...nonlinear chance constraint programming problem using ...
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International Journal of Data Envelopment Analysis and *Operations Research*
... proposed chance-constrained DEA model that allow stochastic in all data variables to determine the most appropriate third-party reverse logistics to assist the decision ...the chance ...
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Vol 3, No 6 (2012)
... D ata envelopment analysis (DEA) is non-parametric method for evaluating the efficiency of the decision making units (DMUs) using mathematical programming. The models which exist are just for evaluating the ...
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Impacts of Exchange Rate Volatility and FDI on Technical Efficiency—A Case Study of Vietnamese Agricultural Sector
... the chance-constrained pro- gramming model would be used to estimate efficiency of the agricultural production ...the chance-constrained programming model to measure technical ...
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A Chance Constrained Integer Programming Model for Open Pit Long-Term Production Planning
... decisions are known, these may be considered as a penalization for constraint violation. In this method the stochastic constraints have to be surely held, i.e., they are to be satisfied with probability of one [21]. In ...
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Chance constrained compromise mixed allocation in multivariate stratified sampling
... ‘Chance Constrained Programming Technique’, that allows the cost constraint to be violated by a specified small ...a Chance Constrained Nonlinear Programming Problem in which the ...
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Gamma distribution approach in chance constrained stochastic programming model
... goal programming and chance-constraint linear goal ...the programming based on probability for the control of nitrate pollution in their studies and compared this with the approaches of various ...
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Project Scheduling Problem with Uncertain Variables
... [1] [2] initially presented function relationship between project cost and activity duration times, and established a mathematical model of deterministic project scheduling problem with objective of minimizing the total ...
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Sequential Programming for PDE Constrained Optimizations
... In this chapter we present specific examples to demonstrate the applicability of the SP method for the constrained optimization. First, we show how to apply SP for the finite dimensional optimal control problem. ...
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Data driven approaches to managing uncertain load control in sustainable power systems (project outputs)
... robust chance constraints corresponding to log-concave uncertainty distribu- tions with known first-order moments and ellipsoidal ...robust chance constraints as second- order cone ...
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Integer Linear Programming in NLP Constrained Conditional Models
... on Constrained Conditional Models along with his student Scott Yih, presented in CoNLLʼ04, and since then has worked on learning and inference issue within this framework as well as on applying it for several NLP ...
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Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
... CVaR constrained MDP, one is able to define a Bellman operator on M ¯ (whose exact definition can be found in Theorem 10), whose fixed point solution is equal to the solution of the original CVaR Lagrangian ...
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Programming Memory-Constrained Networked Embedded Systems - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... Wireless sensor networks are composed of large numbers of tiny sensor devices with wireless communication capabilities. The sensor devices autonomously form networks through which sensor data is transported. The sensor ...
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Predictors of Treatment Adherence in Adolescents with Inflammatory Bowel Disease: The Role of Age, Body Satisfaction and Prospective Memory in Medication and Diet Behavior
... 1inear programming problem is further constrained for some or all of its decision variables to be integer values, the resulting formulation is called integer linear programming, (commonly referred to ...
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Quadratically Constrained Quadratic Programming Problems and Extensions.
... geometric programming is studied. Based on the geometric programming results, an -optimal solution can be obtained by solving the geometric dual problem and a linear programming problem in ...
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A modified exact smooth penalty function for nonlinear constrained optimization
... In the paper [], a new penalty function is proposed for the constrained optimization problem. By augmenting the dimension of the program with an additional variable ε that controls the weight of the penalty ...
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