scholarly journals Choosing various likelihood functions on uncertainty assessment in groundwater simulation-optimization model

2020 ◽  
Vol 20 (2) ◽  
pp. 737-750 ◽  
Author(s):  
Khadije Norouzi Khatiri ◽  
Mohammad Hossein Niksokhan ◽  
Amin Sarang
Keyword(s):  
Optimization Model ◽  
Likelihood Function ◽  
Simulation Optimization ◽  
Minimum Mean Square Error ◽  
Error Variance ◽  
Uncertainty Assessment ◽  
Likelihood Functions ◽  
Function Selection ◽  
The Relationship ◽  
Residual Errors

Abstract The main goal in this research is study of impacts of various likelihood functions on DREAM(zs) (Differential Evolution Adaptive Metropolis) method results in simulation-optimization model of aquifer. In this study, DREAM(zs) algorithm has been developed to study aquifer simulation-optimization model uncertainties. DREAM(zs) is used to investigate uncertainty of parameters of the simulation-optimization model in Isfahan-Barkhar aquifer, Isfehan province, Iran. This study is carried out on an aquifer simulation model of MODFLOW that is coupled with MOPSO (multi-objective particle swarm optimization) optimization. Three likelihood functions, L1, L2, and L3, are considered as informal and the remaining (L4 and L5) are represented as formal categories. Likelihood function L1 is Nash–Sutcliffe efficiency and L2 is based on minimum mean square error. L3 uses estimation of model error variance and L4 focuses on the relationship between the traditional least squares fitting and the Bayesian inference. In likelihood function L5 the serial dependence of residual errors is calculated using a first-order autoregressive model of the residuals. Results suggested that the parameters sensitivity depend on the likelihood function selection, and sensitivity of all parameters is not similar in different likelihood functions. MOPSO algorithm outputs indicated that likelihood function No. 5 has a higher speed in reaching convergence and this function also showed that objective functions had a better performance compared to the other likelihood functions.

Shore-Based Air-Defence Unit Shooting Mode Optimization Model Research

2014 ◽  
Vol 543-547 ◽  
pp. 1786-1789
Author(s):  
Da Wei Yang ◽  
Jian Chong Chu ◽  
Zi Ming Wang ◽  
Wei Bo Li
Keyword(s):  
Optimization Model ◽  
Air Defense ◽  
Model Research ◽  
Mutual Relations ◽  
Firing Efficiency ◽  
The Relationship

This paper analyzes the relationship between firing way and firing command way. Using the theory of probability and air defense combat strategy, it describes the concept, characteristics, and its mutual relations between concentration, dispersion, and mixing command in shooting mode, analyzes the influence of different ways of command on group firing efficiency.

scholarly journals A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingyou Yan ◽  
Qian Zhang ◽  
Xin Zou
Keyword(s):  
Optimization Model ◽  
Cost Optimization ◽  
Resource Usage ◽  
Project Duration ◽  
Resource Demand ◽  
Resource Leveling ◽  
Proposed Model ◽  
Liquidated Damages ◽  
The Relationship

The study of traditional resource leveling problem aims at minimizing the resource usage fluctuations and obtaining sustainable resource supplement, which is accomplished by adjusting noncritical activities within their start and finish time. However, there exist limitations in terms of the traditional resource leveling problem based on the fixed project duration. This paper assumes that the duration can be changed in a certain range and then analyzes the relationship between the scarce resource usage fluctuations and project cost. This paper proposes an optimization model for the multiresource leveling problem. We take into consideration five kinds of cost: the extra hire cost when the resource demand is greater than the resource available amount, the idle cost of resource when the resource available amount is greater than the resource demand, the indirect cost related to the duration, the liquidated damages when the project duration is extended, and the incentive fee when the project duration is reduced. The optimal objective of this model is to minimize the sum of the aforementioned five kinds of cost. Finally, a case study is examined to highlight the characteristic of the proposed model at the end of this paper.

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