scholarly journals "Opti-Marine-Ware" (Optimization of Vessel's Parameters through Spreadsheet Model)

2009 ◽  
Vol 3 (2) ◽  
pp. 49-58
Author(s):  
Abhijit De ◽  
Ashish Kumar
Keyword(s):  
Objective Function ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Design Stage ◽  
Power Prediction ◽  
Power Requirement ◽  
Cost Of Transport ◽  
Spreadsheet Model

[THIS PAPER IS PLAGIARIZED FROM 'Artana, K. B. and  Ishida, K.(2003): The Determination of Optimum Ship’s Design and Power Prediction Using Spreadsheet Model, Journal of the JIME, Vol. 37, No. 6', http://www.mesj.or.jp/mesj_e/english/pub/ap_papers/pdf/2003AP7.pdf]The objective of this paper is to describe and evaluate a scheme of engineering-economic analysis for determining optimum ship's main dimensions and power requirement at basic design stage. We have divided the optimization problem into five main parts, namely, Input, Equation, Constraint, Output and Objective Function. The constraints, which are the considerations to be fulfilled, become the director of this process and a minimum and a maximum value are set on each constraint so as to give the working area of the optimization. The outputs (decision variables) are optimized in favor of minimizing the objective function. Microsoft Excel-Premium Solver Platform (a spreadsheet modeling tool is utilized to model the optimization problem). This paper is commenced by the description of the general optimization problems, and is followed by the model construction of the optimization. A case study on the determination of ship's main dimensions and its power requirement is performed with the main objective to minimize the Economic Cost of Transport (ECT). After simulating the model and verifying the results, it is observed that the spreadsheet model yields considerably comparable results with the main dimensions and power requirement data of the real operated ships (tanker). It is also experienced that this kind of optimization process needs no exhaustive efforts in producing programming codes, if the problem and the optimization model have been well defined.Keywords: Optimization; design; Ship power requirementDOI: 10.3329/jname.v3i2.919Journal of Naval Architecture and Marine Engineering 3(2006) 49-58 

Spreadsheet Modeling to Determine Optimum Ship Main Dimensions and Power Requirements at Basic Design Stage

2003 ◽  
Vol 40 (01) ◽  
pp. 61-70
Author(s):  
Ketut Buda Artana ◽  
Kenji Ishida
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Optimization Problems ◽  
Economic Cost ◽  
Design Stage ◽  
Basic Design ◽  
Power Requirement ◽  
Cost Of Transport ◽  
Decision Variables

The objective of this paper is to describe and evaluate a scheme of engineering-economic analysis in determining optimum ship main dimensions and power requirements at the basic design stage. An optimization model designs the problem and is arranged into five main parts, namely, Input, Equation, Constraint, Output and Objective Function. The constraints, which are the considerations to be fulfilled, become the director of this process and a minimum and a maximum value are set on each constraint so as to give the working area of the optimization process. The outputs (decision variables) are optimized in favor of minimizing the objective function. Microsoft Excel-Premium Solver Platform (PSP), a spreadsheet modeling tool is utilized to model the optimization problem. The first part of this paper contains a description on general optimization problems, followed by model construction of the optimization program. A case study on the determination of ship main dimensions and its power requirements for a tanker is introduced with the main objective being to minimize the economic cost of transport (ECT). After simulating the model and verifying the results, it is observed that this method yields considerably comparable results with the main dimensions and power requirement database of the real operated ships (tanker). It is also believed that this process needs no painful and exhaustive efforts to produce the programming code, if the problem and optimization model have been well defined.

scholarly journals METHODS OF FORMULATING AND SOLVING OPTIMIZATION PROBLEMS OF CUTTING LOGS OF LARGE SIZE BY BAR-SAWING METHOD WITH CUTTING OUT ONE BAR AND FIVE PAIRS OF SIDE EDGING BOARDS WITH SUBSEQUENT SAWING LUMBER FOR EDGED BOARDS

2017 ◽  
Vol 7 (1) ◽  
pp. 137-150
Author(s):  
Агапов ◽  
Aleksandr Agapov
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Target Function ◽  
Cross Sectional ◽  
Optimization Task ◽  
Cutting Out ◽  
The Mathematical Model ◽  
The Relationship

For the first time the mathematical model of task optimization for this scheme of cutting logs, including the objective function and six equations of connection. The article discusses Pythagorean area of the logs. Therefore, the target function is represented as the sum of the cross-sectional areas of edging boards. Equation of the relationship represents the relationship of the diameter of the logs in the vertex end with the size of the resulting edging boards. This relationship is described through the use of the Pythagorean Theorem. Such a representation of the mathematical model of optimization task is considered a classic one. However, the solution of this mathematical model by the classic method is proved to be problematic. For the solution of the mathematical model we used the method of Lagrange multipliers. Solution algorithm to determine the optimal dimensions of the beams and side edging boards taking into account the width of cut is suggested. Using a numerical method, optimal dimensions of the beams and planks are determined, in which the objective function takes the maximum value. It turned out that with the increase of the width of the cut, thickness of the beam increases and the dimensions of the side edging boards reduce. Dimensions of the extreme side planks to increase the width of cut is reduced to a greater extent than the side boards, which are located closer to the center of the log. The algorithm for solving the optimization problem is recommended to use for calculation and preparation of sawing schedule in the design and operation of sawmill lines for timber production. When using the proposed algorithm for solving the optimization problem the output of lumber can be increased to 3-5 %.

scholarly journals The Determination of Optimum Ship's Design and Power Prediction Using Spreadsheet Model.

2002 ◽  
Vol 37 (6) ◽  
pp. 476-486 ◽  
Author(s):  
Ketut Buda Artana ◽  
Kenji Ishida
Keyword(s):  
Power Prediction ◽  
Spreadsheet Model

A Unified Optimization Design Model Under Uncertainty

2009 ◽  
Author(s):  
Zhonglai Wang ◽  
Hong-Zhong Huang ◽  
Huanwei Xu ◽  
Xiaoling Zhang
Keyword(s):  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Problem ◽  
Sequential Optimization ◽  
Multi Objective Optimization ◽  
Probabilistic Characteristic ◽  
Unified Framework ◽  
Reliability Based Design ◽  
Practical Engineering

It is necessary to combine reliability-based design and robust design in the practical engineering. In this paper, a unified framework for integrated reliability-based design and robust design is proposed. In the proposed framework, traditional multi-objective optimization problem is converted to a single objective optimization problem to integrate reliability-based design and robust design without weight factors. The conversion from probabilistic objective function to deterministic objective function is achieved by inverse reliability strategy under the consideration of the probabilistic characteristic of the objective function. After that, an improved sequential optimization and reliability assessment (SORA) method is proposed to deal with the unified framework. Overall, two examples are implemented to illustrate the benefits of the proposed methods.

scholarly journals Model Building and Optimization Analysis of MDF Continuous Hot-Pressing Process by Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-16
Author(s):  
Qingfa Li ◽  
Yaqiu Liu ◽  
Liangkuan Zhu
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Convex Subset ◽  
Hot Pressing ◽  
Optimization Problem ◽  
Model Building ◽  
Optimization Problems ◽  
Initial Point ◽  
Data Set ◽  
Pressing Process

We propose a one-layer neural network for solving a class of constrained optimization problems, which is brought forward from the MDF continuous hot-pressing process. The objective function of the optimization problem is the sum of a nonsmooth convex function and a smooth nonconvex pseudoconvex function, and the feasible set consists of two parts, one is a closed convex subset ofRn, and the other is defined by a class of smooth convex functions. By the theories of smoothing techniques, projection, penalty function, and regularization term, the proposed network is modeled by a differential equation, which can be implemented easily. Without any other condition, we prove the global existence of the solutions of the proposed neural network with any initial point in the closed convex subset. We show that any accumulation point of the solutions of the proposed neural network is not only a feasible point, but also an optimal solution of the considered optimization problem though the objective function is not convex. Numerical experiments on the MDF hot-pressing process including the model building and parameter optimization are tested based on the real data set, which indicate the good performance of the proposed neural network in applications.

Biobjective Optimization of Well Placement: Algorithm, Validation, and Field Testing

SPE Journal ◽  
2021 ◽  
pp. 1-28
Author(s):  
Faruk Alpak ◽  
Vivek Jain ◽  
Yixuan Wang ◽  
Guohua Gao
Keyword(s):  
Multiobjective Optimization ◽  
Objective Function ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Field Testing ◽  
Efficient Implementation ◽  
Field Development ◽  
Biobjective Optimization ◽  
Single Objective

Summary We describe the development and validation of a novel algorithm for field-development optimization problems and document field-testing results. Our algorithm is founded on recent developments in bound-constrained multiobjective optimization of nonsmooth functions for problems in which the structure of the objective functions either cannot be exploited or are nonexistent. Such situations typically arise when the functions are computed as the result of numerical modeling, such as reservoir-flow simulation within the context of field-development planning and reservoir management. We propose an efficient implementation of a novel parallel algorithm, namely BiMADS++, for the biobjective optimization problem. Biobjective optimization is a special case of multiobjective optimization with the property that Pareto points may be ordered, which is extensively exploited by the BiMADS++ algorithm. The optimization algorithm generates an approximation of the Pareto front by solving a series of single-objective formulations of the biobjective optimization problem. These single-objective problems are solved using a new and more efficient implementation of the mesh adaptive direct search (MADS) algorithm, developed for nonsmooth optimization problems that arise within reservoir-simulation-based optimization workflows. The MADS algorithm is extensively benchmarked against alternative single-objective optimization techniques before the BiMADS++ implementation. Both the MADS optimization engine and the master BiMADS++ algorithm are implemented from the ground up by resorting to a distributed parallel computing paradigm using message passing interface (MPI) for efficiency in industrial-scaleproblems. BiMADS++ is validated and field tested on well-location optimization (WLO) problems. We first validate and benchmark the accuracy and computational performance of the MADS implementation against a number of alternative parallel optimizers [e.g., particle-swarm optimization (PSO), genetic algorithm (GA), and simultaneous perturbation and multivariate interpolation (SPMI)] within the context of single-objective optimization. We also validate the BiMADS++ implementation using a challenging analytical problem that gives rise to a discontinuous Pareto front. We then present BiMADS++ WLO applications on two simple, intuitive, and yet realistic problems, and a model for a real problem with known Pareto front. Finally, we discuss the results of the field-testing work on three real-field deepwater models. The BiMADS++ implementation enables the user to identify various compromise solutions of the WLO problem with a single optimization run without resorting to ad hoc adjustments of penalty weights in the objective function. Elimination of this “trial-and-error” procedure and distributed parallel implementation renders BiMADS++ easy to use and significantly more efficient in terms of computational speed needed to determine alternative compromise solutions of a given WLO problem at hand. In a field-testing example, BiMADS++ delivered a workflow speedup of greater than fourfold with a single biobjective optimization run over the weighted-sumsobjective-function approach, which requires multiple single-objective-function optimization runs.

scholarly journals A Methodology to Find the Elementary Landscape Decomposition of Combinatorial Optimization Problems

2011 ◽  
Vol 19 (4) ◽  
pp. 597-637 ◽  
Author(s):  
Francisco Chicano ◽  
L. Darrell Whitley ◽  
Enrique Alba
Keyword(s):  
Combinatorial Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Combinatorial Optimization Problem ◽  
Quadratic Assignment ◽  
Neighborhood Structure ◽  
Combinatorial Optimization Problems ◽  
Elementary Landscape

A small number of combinatorial optimization problems have search spaces that correspond to elementary landscapes, where the objective function f is an eigenfunction of the Laplacian that describes the neighborhood structure of the search space. Many problems are not elementary; however, the objective function of a combinatorial optimization problem can always be expressed as a superposition of multiple elementary landscapes if the underlying neighborhood used is symmetric. This paper presents theoretical results that provide the foundation for algebraic methods that can be used to decompose the objective function of an arbitrary combinatorial optimization problem into a sum of subfunctions, where each subfunction is an elementary landscape. Many steps of this process can be automated, and indeed a software tool could be developed that assists the researcher in finding a landscape decomposition. This methodology is then used to show that the subset sum problem is a superposition of two elementary landscapes, and to show that the quadratic assignment problem is a superposition of three elementary landscapes.

Topology Optimization Design of Bars Structure Based on SKO Method

2013 ◽  
Vol 394 ◽  
pp. 515-520 ◽  
Author(s):  
Wen Jun Li ◽  
Qi Cai Zhou ◽  
Xu Hui Zhang ◽  
Xiao Lei Xiong ◽  
Jiong Zhao
Keyword(s):  
Global Optimization ◽  
Topology Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Topology Optimization Problem ◽  
Proposed Model ◽  
Continuum Structure ◽  
The Impact

There are less topology optimization methods for bars structure than those for continuum structure. Bionic intelligent method is a powerful way to solve the topology optimization problems of bars structure since it is of good global optimization capacity and convenient for numerical calculation. This article presents a SKO topology optimization model for bars structure based on SKO (Soft Kill Option) method derived from adaptive growth rules of trees, bones, etc. The model has been applied to solve the topology optimization problem of a space frame. It uses three optimization strategies, which are constant, decreasing and increasing material removed rate. The impact on the optimization processes and results of different strategies are discussed, and the validity of the proposed model is proved.

Fractal Analysis of the Optimal Objective Function

2014 ◽  
Vol 519-520 ◽  
pp. 811-815
Author(s):  
Xiao Hong Qiu ◽  
Yong Bo Tan ◽  
Bo Li
Keyword(s):  
Objective Function ◽  
Detrended Fluctuation Analysis ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Fluctuation Analysis ◽  
Hurst Index ◽  
Test Functions ◽  
Objective Functions ◽  
Fractal Parameters ◽  
Detrended Fluctuation

The fractals of the optimization problems are first discussed. The multi-fractal parameters of the optimal objective function are computed by the Detrended Fluctuation Analysis (DFA) method. The multi-fractal general Hurst Index is related to the difficulty to solve the optimization problem. These features are verified by analyzing the first six test functions proposed on 2005 IEEE Congress on Evolutionary Computation. The results show that the different objective functions have obvious different multifractal and the general Hurst Index can be used to evaluate the difficulty to solve the optimization problem.

scholarly journals Characteristic feature analysis of continuous optimization problems based on Variability Map of objective function for optimization algorithm configuration

2020 ◽  
Vol 10 (1) ◽  
pp. 97-111
Author(s):  
Taleh Agasiev
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Landscape Analysis ◽  
Decomposition Approach ◽  
Analysis Methods ◽  
Algorithm Configuration ◽  
Sample Points ◽  
Characteristic Features

AbstractAdvanced optimization algorithms with a variety of configurable parameters become increasingly difficult to apply effectively to solving optimization problems. Appropriate algorithm configuration becomes highly relevant, still remaining a computationally expensive operation. Development of machine learning methods allows to model and predict the efficiency of different solving strategies and algorithm configurations depending on properties of optimization problem to be solved. The paper suggests the Dependency Decomposition approach to reduce computational complexity of modeling the efficiency of optimization algorithm, also considering the amount of computational resources available for optimization problem solving. The approach requires development of explicit Exploratory Landscape Analysis methods to assess a variety of significant characteristic features of optimization problems. The results of feature assessment depend on the number of sample points analyzed and their location in the design space, on top of that some of methods require additional evaluations of objective function. The paper proposes new landscape analysis methods based on given points without the need of any additional objective function evaluations. An algorithm of building a so-called Full Variability Map is suggested based on informativeness criteria formulated for groups of sample points. The paper suggests Generalized Information Content method for analysis of Full Variability Map which allows to get accurate and stable estimations of objective function features. The Sectorization method of Variability Map analysis is proposed to assess characteristic features reflecting such properties of objective function that are critical for optimization algorithm efficiency. The resulting features are invariant to the scale of objective function gradients which positively affects the generalizing ability of problems classification algorithm. The procedure of the comparative study of effectiveness of landscape analysis algorithms is introduced. The results of computational experiments indicate reliability of applying the suggested landscape analysis methods to optimization problem characterization and classification.

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