Optimum Pareto Method for Simultaneous Placement of Manual and Remote Controlled Switch Based on MILP Model

2021 ◽  
Author(s):  
Allan Costa Gomes ◽  
Raimundo Furtado Sampaio ◽  
Giovanni Cordeiro Barroso ◽  
Ruth Pastora Saraiva Leao
Keyword(s):  
Milp Model ◽  
Pareto Method

Economic Dispatch of Power Systems with LMP-dependent Demands: A Non-iterative MILP Model

Energy ◽  
2021 ◽  
pp. 121015
Author(s):  
Ziqi Shen ◽  
Wei Wei ◽  
Lei Wu ◽  
Miadreza Shafie-khah ◽  
João P.S. Catalão
Keyword(s):  
Power Systems ◽  
Economic Dispatch ◽  
Milp Model

MILP model for volt-var optimization considering chronological operation of distribution systems containing DERs

2021 ◽  
Vol 129 ◽  
pp. 106761
Author(s):  
Bibiana P. Ferraz ◽  
Mariana Resener ◽  
Luís A. Pereira ◽  
Flávio A.B. Lemos ◽  
Sérgio Haffner
Keyword(s):  
Distribution Systems ◽  
Milp Model

scholarly journals Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

2014 ◽  
Vol 18 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Johanna C Gerdessen ◽  
Olga W Souverein ◽  
Pieter van ‘t Veer ◽  
Jeanne HM de Vries
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Selection Process ◽  
Saturated Fat ◽  
Mixed Integer ◽  
Monounsaturated Fat ◽  
Food Items ◽  
Milp Model ◽  
Selection Of

AbstractObjectiveTo support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.DesignSelection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.ResultsThe food lists generated by the MILP model have good performance in terms of length, coverage and R2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R2 was similar to that of the benchmark.ConclusionsThe results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

Selection of the Optimal Structural Design of a Spar Composite Wing

2021 ◽  
pp. 90-99
Author(s):  
O.V. Tatarnikov ◽  
W.A. Phyo ◽  
Lin Aung Naing
Keyword(s):  
Optimal Design ◽  
Element Analysis ◽  
Minimal Weight ◽  
Wing Structures ◽  
Optimization Parameters ◽  
Nonlinear Static ◽  
Wing Structure ◽  
Pareto Method ◽  
Selection Of ◽  
Composite Wing

This paper describes a method for optimizing the design of a spar-type composite aircraft wing structure based on multi-criterion approach. Two types of composite wing structures such as two-spar and three-spar ones were considered. The optimal design of a wing frame was determined by the Pareto method basing on three criteria: minimal weight, minimal wing deflection, maximal safety factor and minimal weight. Positions of wing frame parts, i.e. spars and ribs, were considered as optimization parameters. As a result, an optimal design of a composite spar-type wing was proposed. All the calculations necessary to select the optimal structural and design of the spar composite wing were performed using nonlinear static finite element analysis in the FEMAP with NX Nastran software package.

scholarly journals A multivariable multiobjective predictive controller

2013 ◽  
Vol 23 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Faten Ben Aicha ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Mimo System ◽  
Weighting Factor ◽  
Nsga Ii ◽  
Weighted Sum Method ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Pareto Method ◽  
The Cost

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

A MILP Model and a GRASP Algorithm for the Helicopter Routing Problem with Multi-Trips and Time Windows

2019 ◽  
pp. 204-218
Author(s):  
André Manhães Machado ◽  
Geraldo Regis Mauri ◽  
Maria Claudia Silva Boeres ◽  
Rodrigo de Alvarenga Rosa
Keyword(s):  
Time Windows ◽  
Routing Problem ◽  
Milp Model

scholarly journals Multi-Objective Cross-Layer Optimization for Selection of Cooperative Path Pairs in Multihop Wireless Ad hoc Networks

2013 ◽  
Vol 9 (3) ◽  
pp. 170 ◽  
Author(s):  
Nyoman Gunantara ◽  
Gamantyo Hendrantoro
Keyword(s):  
Power Consumption ◽  
Ad Hoc Networks ◽  
Wireless Ad Hoc Networks ◽  
Ad Hoc ◽  
Cross Layer ◽  
Multihop Wireless ◽  
Cross Layer Optimization ◽  
Hoc Networks ◽  
Pareto Method ◽  
Selection Of

This paper focuses in the selection of an optimal path pair for cooperative diversity based on cross-layer optimization in multihop wireless ad hoc networks. Cross-layer performance indicators, including power consumption, signal-to-noise ratio, and load variance are optimized using multi-objective optimization (MOO) with Pareto method. Consequently, optimization can be performed simultaneously to obtain a compromise among three resources over all possible path pairs. The Pareto method is further compared to the scalarization method in achieving fairness to each resource. We examine the statistics of power consumption, SNR, and load variance for both methods through simulations. In addition, the complexity of the optimization of both methods is evaluated based on the required computing time.

Sign in / Sign up

Export Citation Format

Share Document