Development a Novel Integrated Distributed Multi-objective Simulation-optimization Model for Coastal Aquifers Management Using NSGA-II and GMS Models

2021 ◽  
Author(s):  
Mahmoud Mohammad Rezapour Tabari ◽  
Mahbobeh Abyar
Keyword(s):  
Coastal Aquifers ◽  
Optimization Model ◽  
Simulation Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Aquifers Management

Development of a simulation–optimization model with a multi‐objective framework for automatic design of a furrow irrigation system

2020 ◽  
Vol 69 (4) ◽  
pp. 603-617
Author(s):  
Elham Saberi ◽  
Abbas Khashei Siuki ◽  
Mohsen Pourreza‐Bilondi ◽  
Ali Shahidi
Keyword(s):  
Optimization Model ◽  
Irrigation System ◽  
Simulation Optimization ◽  
Furrow Irrigation ◽  
Automatic Design ◽  
Multi Objective

scholarly journals Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 811 ◽  
Author(s):  
Yongmao Xiao ◽  
Qingshan Gong ◽  
Xiaowu Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Model ◽  
Low Cost ◽  
Minimum Cost ◽  
Processing Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Set Up

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.

Multi-Objective Optimum Design for Wave-Plate Demister Based on NSGA-ΙΙ Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 1163-1167
Author(s):  
Zong Liang Qiao ◽  
Lei Zhang ◽  
Feng Qi Si ◽  
Zhi Gao Xu
Keyword(s):  
Optimization Model ◽  
Power Plants ◽  
Structural Parameters ◽  
Weight Coefficient ◽  
Structure Design ◽  
Flue Gas Desulfurization ◽  
Support Vector ◽  
Nsga Ii ◽  
Wave Plate ◽  
Multi Objective

For optimizing the structure design of the wave-plate demister vanes in wet flue gas desulfurization system (WFGD) of power plants, the characteristics models of removal efficiency and pressure drop were established by using least squares support vector machine (LSSVM) based on numerical simulation results. The highest relative error between the predicted output and measured value is 2%, it proves the modeling is good for the prediction. Based on the characteristics models, a multi-objective optimization model was established. It used the structural parameters as the optimal variables and the demister characteristics as the objective function. This optimization model was solved by non dominated sorting genetic algorithm (NSGA-II). The simulation data show that the Multi-objective optimum method can get more effective results compared to the weight coefficient method.

Hybrid ACO–ANN-Based Multi-objective Simulation–Optimization Model for Pollutant Load Control at Basin Scale

2014 ◽  
Vol 20 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Mohammad J. Emami Skardi ◽  
Abbas Afshar ◽  
Motahareh Saadatpour ◽  
Samuel Sandoval Solis
Keyword(s):  
Optimization Model ◽  
Simulation Optimization ◽  
Load Control ◽  
Pollutant Load ◽  
Multi Objective ◽  
Basin Scale

Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm

2016 ◽  
Vol 15 (02) ◽  
pp. 423-451 ◽  
Author(s):  
Lean Yu ◽  
Zebin Yang ◽  
Ling Tang
Keyword(s):  
Information Technology ◽  
Optimization Model ◽  
Profit Maximization ◽  
Optimization Approach ◽  
Risk Minimization ◽  
Nsga Ii ◽  
Training Time ◽  
Multi Objective ◽  
Planning Optimization ◽  
Oil Markets

Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk.

Manufacturer Order Fulfillment Based on Multi-Objective Optimization NSGA II Model

2013 ◽  
Vol 340 ◽  
pp. 136-140
Author(s):  
Liang You Shu ◽  
Ling Xiao Yang
Keyword(s):  
Optimization Model ◽  
Optimization Problems ◽  
Population Based ◽  
Pareto Optimal ◽  
Order Fulfillment ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Local Search Procedure

The aim of this paper is to study the production and delivery decision problem in the Manufacturer Order Fulfillment. Owing to the order fulfillment optimization condition of the manufacturer, the multi-objective optimization model of manufacturers' production and delivery has been founded. The solution of the multi-objective optimization model is also very difficult. Fast and Elitist Non-dominated Sorting Genetic Algorithm (NSGA II) have been applied successfully to various test and real-world optimization problems. These population based the algorithm provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front. But its convergence to the true Pareto-optimal front is not guaranteed. Hence SBX is used as a local search procedure. The proposed procedure is successfully applied to a special case. The results validate that the algorithm is effective to the multi-objective optimization model.

scholarly journals Multi-objective optimization for sustainable groundwater management by developing of coupled quantity-quality simulation-optimization model

2017 ◽  
Vol 19 (6) ◽  
pp. 973-992 ◽  
Author(s):  
Asghar Kamali ◽  
Mohammad Hossein Niksokhan
Keyword(s):  
Water Quality ◽  
Optimization Model ◽  
Simulation Optimization ◽  
Simulation Models ◽  
Multi Objective Optimization ◽  
Management Scenarios ◽  
Sustainability Index ◽  
Multi Objective ◽  
Previous Operation ◽  
The Stability

Abstract This study addresses the issue of optimal management of aquifers using a mathematical simulation- optimization model which relies on the stability of water quality and quantity, considering salinity. In this research first we developed a hydrological model (SWAT) to estimate recharge rates and its spatiotemporal distribution. Then, groundwater simulation of the basin was simulated and calibrated using MODFLOW 2000 and water quality was simulated and calibrated using MT3DMS. Afterwards, a multi-objective optimization model (MOPSO) and embed simulation models as tools to assess the objective function was carried out in order to produce a simulation-optimization model. Finally, a sustainability index to assess Pareto front's answers and three management scenarios (continuing previous operation, 30% increasing and reduction in previous operation) was developed. The results show that the majority of Pareto optimal answers have more sustainability index than a 30% reduction of operation with the best answer of 0.059. Relatively, the sustainability index of 30% reduction of operation is 0.05.

Using NSGA-II to solve multi-objective competitive location problem with cooperative coverage for distance-based attractiveness

2021 ◽  
pp. 1-12
Author(s):  
Sheng-Chuan Wang ◽  
Ta-Cheng Chen
Keyword(s):  
Genetic Algorithms ◽  
Optimization Model ◽  
Location Problem ◽  
Solution Procedure ◽  
Competitive Location ◽  
Multi Objective Optimization ◽  
Demand Point ◽  
Nsga Ii ◽  
Solution Sets ◽  
Multi Objective

Multi-objective competitive location problem with cooperative coverage for distance-based attractiveness is introduced in this paper. The potential facilities compete to be selected to serve all demand points which are determined by maximizing total collective attractiveness of all demand points from assigned facilities and minimizing the fixed and distance costs between all demand points and selected facilities. Facility attractiveness is represented as a coverage of the facility with full, partial and none coverage corresponding to maximum full and partial coverage radii. Cooperative coverage, which the demand point is covered by at least one facility, is also considered. The problem is formulated as a multi-objective optimization model and solution procedure based on elitist non-dominated sorting genetic algorithms (NSGA-II) is developed. Experimental example demonstrates the best non-dominated solution sets obtained by developed solution procedure. Contributions of this paper include introducing competitive location problem with facility attractiveness as a distance-based coverage of the facility, re-categorizing facility coverage classification and developing solution procedure base upon NSGA-II.

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