scholarly journals Two-stage fuzzy production planning expected value model and its approximation method

2012 ◽  
Vol 36 (6) ◽  
pp. 2429-2445 ◽  
Author(s):  
Guoqiang Yuan
Keyword(s):  
Production Planning ◽  
Approximation Method ◽  
Expected Value ◽  
Two Stage ◽  
Expected Value Model ◽  
Value Model

The DNA Genetic Algorithm Applied for Solving Optimal Placement of Sensors

2012 ◽  
Vol 160 ◽  
pp. 103-108
Author(s):  
Xin Liu ◽  
Zhong Ren Feng ◽  
Xiong Jian Wang ◽  
Bao Fu Wang
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Expected Value ◽  
Optimal Placement ◽  
Stochastic Integer Programming ◽  
Objective Functions ◽  
Stochastic Variable ◽  
Expected Value Model ◽  
Complex Knowledge ◽  
Value Model

In order to solve optimal placement of bridge sensors based on the modal information, a multi-objective integer programming expected value model is established and the number of modal is considered as a stochastic variable in this paper, and here Fisher and MAC matrix are combined as the objective functions . Since DNA Genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly, these merits and the technique of stochastic simulation are also combined, which for estimating stochastic integer programming expected value models problem. And finally the feasibility of the algorithm is showed by XuGe Bridge as an example.

Multiobjective expected value model for portfolio selection in fuzzy environment

2012 ◽  
Vol 7 (8) ◽  
pp. 1765-1791 ◽  
Author(s):  
Pankaj Gupta ◽  
Garima Mittal ◽  
Mukesh Kumar Mehlawat
Keyword(s):  
Portfolio Selection ◽  
Expected Value ◽  
Fuzzy Environment ◽  
Expected Value Model ◽  
Value Model

scholarly journals Solving a Two-Stage Stochastic Capacitated Location-Allocation Problem with an Improved PSO in Emergency Logistics

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ye Deng ◽  
Wanhong Zhu ◽  
Jian Tang ◽  
Jianfei Qin
Keyword(s):  
Two Stage ◽  
Location Allocation ◽  
Adaptive Parameter ◽  
Emergency Logistics ◽  
Decision Variables ◽  
Expected Value Model ◽  
Restart Strategy ◽  
Value Model ◽  
Location Allocation Problem ◽  
Capacitated Location

A stochastic expected value model and its deterministic conversion are developed to formulate a two-stage stochastic capacitated location-allocation (LA) problem in emergency logistics; that is, the number and capacities of supply centers are both decision variables. To solve these models, an improved particle swarm optimization algorithm with the Gaussian cloud operator, the Restart strategy, and the adaptive parameter strategy is developed. The algorithm is integrated with the interior point method to solve the second-stage model. The numerical example proves the effectiveness and efficiency of the conversion method for the stochastic model and the proposed strategies that improve the algorithm.

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