Developing a New Integrated Bi-Objective Model for Buffer and Process Time Optimization Problem using Optimization via Simulation Approach

2018 ◽  
Vol 10 (3) ◽  
pp. 373-386
Author(s):  
P. Azimi ◽  
N. Farhadi
Keyword(s):  
Optimization Problem ◽  
Process Time ◽  
Simulation Approach ◽  
Time Optimization ◽  
Optimization Via Simulation ◽  
Objective Model

Segmentation of Certificate With Connected Component Labeling Method

2020 ◽  
Vol 8 (2) ◽  
pp. 119
Author(s):  
Cokorda Gde Teresna Jaya ◽  
I Gede Arta Wibawa
Keyword(s):  
Test Data ◽  
Optimization Problem ◽  
Initial Step ◽  
Connected Component ◽  
Important Data ◽  
Connected Component Labeling ◽  
Time Optimization ◽  
Segmentation Process ◽  
Verification Process ◽  
Labeling Method

Certificate is one of the documents that can be used as evidence of ownership or an event. For example, when certificate used as requirement to participate in an event. If a document is made as a requirement, of course the file verification process will be done. Seeing the time optimization problem when verifying the file, the authors carry out research by segmenting important data contained in a certificate as an initial step in the development of an automatic document verification system. The segmentation process carried out in this study uses the Connected Component Labeling method in determining the area to be segmented and Automatic Cropping to cut the results of the segmentation process. By using these two methods obtained an accuracy of 60% with a total of 15 pieces of test data

RIE process time optimization from real time sensory data

1999 ◽  
Vol 32 (2) ◽  
pp. 443-448
Author(s):  
Suttipan Limanond ◽  
Jennie Si
Keyword(s):  
Real Time ◽  
Process Time ◽  
Sensory Data ◽  
Time Optimization

scholarly journals ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS

2016 ◽  
Vol 56 (1) ◽  
pp. 67 ◽  
Author(s):  
Amanda Prorok ◽  
M. Ani Hsieh ◽  
Vijay Kumar
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Method ◽  
Transition Rates ◽  
Trait Distribution ◽  
Time Optimization ◽  
Heterogeneous Robots ◽  
Real Time Optimization ◽  
Optimal Set

We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

Robust Real-Time Optimization of a Solid Oxide Fuel Cell Stack

2011 ◽  
Vol 8 (5) ◽  
Author(s):  
A. Marchetti ◽  
A. Gopalakrishnan ◽  
B. Chachuat ◽  
D. Bonvin ◽  
L. Tsikonis ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Real Time ◽  
Optimization Problem ◽  
Solid Oxide ◽  
Step Response ◽  
Oxide Fuel ◽  
Power Demand ◽  
Time Optimization ◽  
Real Time Optimization

On-line control and optimization can improve the efficiency of fuel cell systems, whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigates the real-time optimization (RTO) of a solid oxide fuel cell (SOFC) stack. An optimization problem maximizing the efficiency subject to operating constraints is defined. Due to inevitable model inaccuracies, the open-loop implementation of optimal inputs evaluated off-line may be suboptimal, or worse, infeasible. Infeasibility can be avoided by controlling the constrained quantities. However, the constraints that determine optimal operation might switch with varying power demand, thus requiring a change in the regulator structure. In this paper, a control strategy that can handle plant-model mismatch and changing constraints in the face of varying power demand is presented and illustrated. The strategy consists in the integration of RTO and model predictive control (MPC). A lumped model of the SOFC is utilized at the RTO level. The measurements are not used to re-estimate the parameters of the SOFC model at different operating points, but to simply adapt the constraints in the optimization problem. The optimal solution generated by RTO is implemented using MPC that uses a step-response model in this case. Simulation results show that near-optimality can be obtained, and constraints are respected despite model inaccuracies and large variations in the power demand.

A new modifier adaptation methodology for real-time optimization

2017 ◽  
Vol 40 (4) ◽  
pp. 1320-1327
Author(s):  
Chunhua Chen ◽  
Mingxing Jia ◽  
Fuqiang You ◽  
Fuli Wang ◽  
Wenqi Kou
Keyword(s):  
Real Time ◽  
Flue Gas ◽  
Optimization Problem ◽  
Gradient Estimation ◽  
Search Area ◽  
Time Optimization ◽  
Decision Variables ◽  
Optimal Setting ◽  
Simulation Results ◽  
Real Time Optimization

The traditional modifier adaptation can be used to deal with the optimization problem of mismatched model, and it shows good performance in most cases. However, the method cannot be used directly, when the gradients of the model outputs, with respect to the decision variables, are difficult to calculate directly. Also, the simulation results show that the method cannot achieve the optimum in theory when the gradient estimation is particularly inaccurate. Therefore, a new modifier adaptation methodology for real-time optimization is proposed in this paper. A method similar to Proportion integration differentiation is used to deal with the deviation between the actual gradient and the model gradient and to improve the method of modifier terms computation. In addition, we find that the appropriate relaxation of certain constraints can expand the search area and improve the effectiveness of the optimization. The validation of the method is demonstrated by the solution of an artificial example and the optimal setting problem of the converter entrance temperatures in flue gas acid-making.

scholarly journals Multi-objective model predictive control for microgrids

2020 ◽  
Vol 68 (8) ◽  
pp. 687-702
Author(s):  
Thomas Schmitt ◽  
Tobias Rodemann ◽  
Jürgen Adamy
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pareto Front ◽  
Optimization Problem ◽  
German Industry ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Time Step ◽  
Economic Model Predictive Control ◽  
Objective Model

AbstractEconomic model predictive control is applied to a simplified linear microgrid model. Monetary costs and thermal comfort are simultaneously optimized by using Pareto optimal solutions in every time step. The effects of different metrics and normalization schemes for selecting knee points from the Pareto front are investigated. For German industry pricing with nonlinear peak costs, a linear programming trick is applied to reformulate the optimization problem. Thus, together with an efficient weight determination scheme, the Pareto front for a horizon of 48 steps is determined in less than 4 s.

Consumption, investment and life insurance under different tax regimes

2012 ◽  
Vol 7 (2) ◽  
pp. 210-235 ◽  
Author(s):  
Kenneth Bruhn
Keyword(s):  
Life Insurance ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Retirement Savings ◽  
Tax Effects ◽  
Retirement Saving ◽  
Lifetime Income ◽  
Time Optimization ◽  
Tax Regimes ◽  
Parameter Values

AbstractWe study the effects of introducing taxation in classical continuous-time optimization problems with utility from consumption, bequest and retirement savings. Inspired by actual tax favoured retirement savings programs, we formulate and solve the optimization problem for various tax regimes, and compare tax effects on consumption/savings contributions, investment and purchase of life insurance under the regimes. The optimization problems have analytical solutions, which allow for easy comparison of tax effects under the different regimes. To substantiate the results we also present a numerical analysis of the results based on realistic parameter values and regimes. Based on American and Danish tax regimes we estimate the values of existing retirement saving favouring to be 1 – 2 percent of lifetime income.

Multi-Objective Optimization Model for Non-Suspending Runway Reconstruction Project Scheduling in Busy Airports

2014 ◽  
Vol 587-589 ◽  
pp. 1884-1887
Author(s):  
Yu Lin Yang ◽  
Zheng Hao Ma
Keyword(s):  
Critical Path ◽  
Estimation Method ◽  
Pso Algorithm ◽  
Point Estimation ◽  
Process Time ◽  
Multi Objective ◽  
Objective Model ◽  
Scheduling Plan ◽  
Point Estimation Method ◽  
Reconstruction Time

As a result of the urgency of runway reconstruction and the inevitability of taking non-suspending reconstruction, time, cost and quality are taken as three basic objectives of the optimization with the complex environment of the runway, and the special related constraints are taken into consideration as well. Combining the other two objectives with the time objective and quantifying three objectives in the same way help to present the multi-objective model that is based on the multi-attribute utility function theory. Establishing the project network of asphalt repaving project and using the critical path method contribute to dealing with the uncertainty and randomness based on the distribution of process time by the three-point estimation method. Particle swarm optimization (PSO) algorithm helps solve the model and offers a scheduling plan of the critical path. In the end, one non-suspending the construction project of the runway is taken as an example and it proves the validity of the model compared with the related researches and the actual applied schedule.

The least-time optimization for abnormity control of a complex industrial process

2018 ◽  
Vol 41 (5) ◽  
pp. 1468-1476
Author(s):  
Hui Li ◽  
Fuli Wang ◽  
Hongru Li ◽  
Xu Wang
Keyword(s):  
Control Problem ◽  
Control Strategy ◽  
Optimization Problem ◽  
External Environment ◽  
Industrial Process ◽  
Economic Loss ◽  
Industrial Processes ◽  
Mechanical Constraints ◽  
Time Optimization ◽  
Simulation Results

Modern complex industrial processes are prone to errors because of interactions between humans, the external environment and the equipment. When the abnormity degree of a system increases, the system can generate failures or even accidents, which result in serious economic loss or even personal casualties. Therefore, it is necessary to take effective measures to remove the abnormity as soon as possible. This problem can be described as the least-time optimization problem. This paper analyses an abnormity by summarizing and comparing related concepts in the researched results. Based on these concepts, a control strategy for the abnormity in a complex industrial process is proposed by analysing the experience of operators on site. Taking the abnormity in the thickening process of gold hydrometallurgy as an example, this paper explores how the abnormity control problem can be transformed into the least-time optimization problem. Technical and mechanical constraints are described. Simulation results indicate that the proposed strategy can assist the operators to regulate the control variables and recover the abnormity as soon as possible. This produces better performance than the existing regulations on site.

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