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 "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 

scholarly journals MATHEMATICAL MODELING OF OPTIMAL HEIGHTS OF EXTERNAL GEODESIC SIGNS

2021 ◽  
Vol 1 (161) ◽  
pp. 109-115
Author(s):  
O. Voronkov ◽  
O. Baistryk ◽  
A. Danylyuk
Keyword(s):  
Quadratic Programming ◽  
Objective Function ◽  
Optimization Model ◽  
Optimization Problems ◽  
Geodetic Network ◽  
Local Maximum ◽  
Quadratic Model ◽  
Design Stage ◽  
Mathematical Apparatus ◽  
Financial Costs

Due to the great importance of geodetic networks for the formation of a unified coordinate system on the territory of Ukraine, external geodetic signs have been established, which need to be restored and further developed. At the design stage, the calculation of the heights of geodetic signs is performed on topographic maps. The cost of erection of geodetic signs on average is 50 - 60% of the total cost of creating a geodetic network, so there is a need to pay close attention to the choice of places to build signs that provide their optimal height. The article presents a methodical approach to determining the heights of external geodetic signs, based on the mathematical apparatus used for modeling and solving optimization problems. The principle of construction of the optimization model of the problem during the design of external geodetic signs in the conditions when their direct visibility should be provided is considered. The article considers in detail the types and structures of external geodetic signs, identifies the features of their location and construction. The resulting optimization model includes objective function, which is a quadratic form, and line restriction. This model is a model of quadratic programming, that belongs to a class of nonlinear programming models, but have their particular case and the simplest of nonlinear. This is because property quadratic model, which consists in the fact that since the problem of quadratic programming set of feasible solutions is convex, then, if the objective function is concave, any local maximum is global, and if the objective function is convex, then any local minimum is also global. The necessity of solving the problem of optimizing the heights of geodetic signs is substantiated, which is still connected with the financial costs for their construction and reconstruction. It is concluded that the approach to determining the heights of external geodetic signs presented in the article, which uses a mathematical apparatus for solving optimization problems, is an effective and efficient approach, and allows to numerically justify the minimum required and sufficient height of external geodetic signs. Using the present approach to the determination of geodetic heights external signs to optimize the financial costs of their construction, which is essential.

scholarly journals Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

2018 ◽  
Vol 246 ◽  
pp. 01003
Author(s):  
Xinyuan Liu ◽  
Yonghui Zhu ◽  
Lingyun Li ◽  
Lu Chen
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Complex Problem ◽  
Optimization Techniques ◽  
Decision Variables ◽  
Handling Methods ◽  
Optimality Algorithm ◽  
Parameter Values

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

Multi-Objective Optimization Model of Export and Transit Containers Storage in a Transshipment Port Yard

2012 ◽  
Vol 220-223 ◽  
pp. 272-278 ◽  
Author(s):  
Bin Wang ◽  
Tao Yang
Keyword(s):  
Integer Programming ◽  
Objective Function ◽  
Optimization Model ◽  
Storage Capacity ◽  
Multi Objective Optimization ◽  
Planning Period ◽  
Multi Objective ◽  
Numerical Example ◽  
Decision Variables ◽  
Operational Capacity

To effectively improve the competitiveness of port enterprises, container yard stacking optimization is an important way to raise their benefit. A multi-objective optimization model for containers stacking in the storage yard based on 0-1mixed integer programming is built to improve its efficiency. The objective function is to minimize the number of yard cranes used in the storage yard and balance the workload among different blocks during the planning period. The decision variables include the number of transit and export containers assigned to yard-bits, yard cranes distributed to blocks, yard-bits with high and low workload in a block. The constraints include meeting the shipping requirement, storage capacity and operational capacity of yard cranes. A numerical example is given and solved by Lingo9.0. The simulation is done to recover the relation between workload level and the number of yard crane used and the workload balance. The model can be used to yard stacking management and lift its level for a transshipment port.

scholarly journals A Stochastic Trust Region Method for Unconstrained Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Ningning Li ◽  
Dan Xue ◽  
Wenyu Sun ◽  
Jing Wang
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Unconstrained Minimization ◽  
Quadratic Model ◽  
Convergence Properties ◽  
Region Method ◽  
Unconstrained Optimization Problems

In this paper, a stochastic trust region method is proposed to solve unconstrained minimization problems with stochastic objectives. Particularly, this method can be used to deal with nonconvex problems. At each iteration, we construct a quadratic model of the objective function. In the model, stochastic gradient is used to take the place of deterministic gradient for both the determination of descent directions and the approximation of the Hessians of the objective function. The behavior and the convergence properties of the proposed method are discussed under some reasonable conditions. Some preliminary numerical results show that our method is potentially efficient.

Multi-Objective and Stochastic Optimization Model of Transit Containers Storage in a Transshipment Port Yard

2013 ◽  
Vol 411-414 ◽  
pp. 2680-2683
Author(s):  
Bin Wang ◽  
Tao Yang
Keyword(s):  
Integer Programming ◽  
Stochastic Programming ◽  
Stochastic Optimization ◽  
Objective Function ◽  
Optimization Model ◽  
Storage Capacity ◽  
Planning Period ◽  
Multi Objective ◽  
Decision Variables ◽  
Operational Capacity

The load/unload task in a transshipment port yard is more heavy and the time requirmement is more tight than an export port.A multi-objective and stochastic programming optimization model for containers stacking in the storage yard of a transshipment port is built to improve its efficiency. The objective function is to minimize the number of yard cranes used in the storage yard and balance the workload among different blocks during the planning period. The decision variables include the number of transit containers assigned to yard-bits, yard cranes distributed to blocks, yard-bits with high and low workload in a block. The constraints include meeting the shipping requirement, storage capacity and operational capacity of yard cranes. The numbers of transit containers are stochastic.The model is tranfered into an integer programming and solved by Lingo9.0. The simulation is done to recover the relation between workload level and the number of yard crane used and the workload balance. The model can be used to yard stacking management and lift its level for a transshipment port.

scholarly journals Optimization In A Finite-Dimensional Euclide Space

2020 ◽  
Vol 7 (1) ◽  
pp. 20-28
Author(s):  
A.I. Kosolap ◽  
Keyword(s):  
Euclidean Space ◽  
Objective Function ◽  
Optimization Model ◽  
Optimization Problems ◽  
Complexity Class ◽  
Feasible Region ◽  
Complexity Classes ◽  
The Third ◽  
Primal Dual ◽  
Multimodal Problems

In this paper, optimization models in Euclidean space are divided into four complexity classes. Ef-fective algorithms have been developed to solve the problems of the first two classes of complexity. These are the primal-dual interior-point methods. Discrete and combinatorial optimization problems of the third complexity class are recommended to be converted to the fourth complexity class with continuous change of variables. Effective algorithms have not been developed for problems of the third and fourth complexity classes, with the exception of a narrow class of problems that are unimodal. The general optimization problem is formulated as a minimum (maximum) objective function in the presence of constraints. The complexity of the problem depends on the structure of the objective function and its feasible region. If the functions that determine the optimization model are quadratic or polynomial, then semidefinite programming can be used to obtain estimates of so-lutions in such problems. Effective methods have been developed for semidefinite optimization problems. Sometimes it’s enough to develop an algorithm without building a mathematical model. We see such an example when sorting an array of numbers. Effective algorithms have been devel-oped to solve this problem. In the work for sorting problems, an optimization model is constructed, and it coincides with the model of the assignment problem. It follows from this that the sorting problem is unimodal. Effective algorithms have not been developed to solve multimodal problems. The paper proposes a simple and effective algorithm for the optimal allocation of resources in mul-tiprocessor systems. This problem is multimodal. In the general case, for solving multimodal prob-lems, a method of exact quadratic regularization is proposed. This method has proven its compara-tive effectiveness in solving many test problems of various dimensions. Keywords: Euclidean space, optimization, unimodal problems, multimodal problems, complexity classes, numerical methods.

scholarly journals Least-Cost Structural Optimization Oriented Preliminary Design

2001 ◽  
Vol 17 (04) ◽  
pp. 202-215 ◽  
Author(s):  
Philippe Rigo
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Plate Thickness ◽  
Construction Cost ◽  
Global Structure ◽  
Design Stage ◽  
Preliminary Design ◽  
Labor Costs ◽  
Rational Analysis ◽  
Design Variables

A computer design package is presented that provides optimum midship scantlings(plating, longitudinal members and frames). Basic characteristics such as L,B,T,Cb, the global structure layout, and applied loads are the requested data. It is not necessary to provide a feasible initial scantling. Within about one hour of computation time with a usual PC or laptop the LBR-5software automatically provides a rational optimum design. This software is an optimization tool dedicated to preliminary design. Its main advantages, in the early stage of design, are ease of structural modeling, rapid 3-D rational analysis of a ship's hold, and scantling optimization. Preliminary design is the most relevant and the least expensive time to modify design scantling and to compare different alternatives. Unfortunately, it is often too early for efficient use of many commercial software systems, such as FEM. This paper explains how it is now possible to perform optimization at the early design stage, including a 3-D numerical structural analysis. LBR-5 is based on the Module Oriented Approach. Design variables are the dimensions of the longitudinal and transversal members, plate thickness and spacing between members. The software contains three major modules. First, the Cost Module to assess the construction cost which is the objective function (least construction cost). So, unit material costs (Euro/kg or $/kg), welding, cutting, fairing, productivity (man-hours/m) and basic labor costs(Euro/man-hour) have to be specified by the user to define an explicit objective function. Then, there is the Constraint Module to perform a rational analysis of the global structure. This structure is modeled using stiffened plate and stiffened cylindrical shell elements. Finally, the Opti Module which contains a mathematical programming code (CONLIN) to solve constrained nonlinear optimization problems with a reduced number of re-analyses. Usually less than 15 analyses are required even with hundreds of design variables and hundreds of constraints. Optimum analysis of a FSO unit (Floating Storage Offloading) is presented as an example of the performance of the LBR-5 tool.

Convex Grey Optimization

2019 ◽  
Vol 53 (1) ◽  
pp. 339-349
Author(s):  
Surafel Luleseged Tilahun
Keyword(s):  
Objective Function ◽  
Optimization Problem ◽  
Fuzzy Numbers ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Optimal Solution ◽  
Decision Variables ◽  
Constraint Set ◽  
Grey Number ◽  
Real Scenario

Many optimization problems are formulated from a real scenario involving incomplete information due to uncertainty in reality. The uncertainties can be expressed with appropriate probability distributions or fuzzy numbers with a membership function, if enough information can be accessed for the construction of either the probability density function or the membership of the fuzzy numbers. However, in some cases there may not be enough information for that and grey numbers need to be used. A grey number is an interval number to represent the value of a quantity. Its exact value or the likelihood is not known but the maximum and/or the minimum possible values are. Applications in space exploration, robotics and engineering can be mentioned which involves such a scenario. An optimization problem is called a grey optimization problem if it involves a grey number in the objective function and/or constraint set. Unlike its wide applications, not much research is done in the field. Hence, in this paper, a convex grey optimization problem will be discussed. It will be shown that an optimal solution for a convex grey optimization problem is a grey number where the lower and upper limit are computed by solving the problem in an optimistic and pessimistic way. The optimistic way is when the decision maker counts the grey numbers as decision variables and optimize the objective function for all the decision variables whereas the pessimistic way is solving a minimax or maximin problem over the decision variables and over the grey numbers.

Power Generation for India with Higher Efficiency

2018 ◽  
Vol 6 (7) ◽  
pp. 267
Author(s):  
Umeshkannan P ◽  
Muthurajan KG
Keyword(s):  
Power Generation ◽  
Objective Function ◽  
Fossil Fuels ◽  
Programming Model ◽  
Developed Countries ◽  
Power Requirement ◽  
Average Efficiency ◽  
Potential Demand ◽  
The Developed Countries ◽  
Developed Nations

The developed countries are consuming more amount of energy in all forms including electricity continuously with advanced technologies.  Developing  nation’s  energy usage trend rises quickly but very less in comparison with their population and  their  method of generating power is not  seems  to  be  as  advanced  as  developed  nations. The   objective   function   of   this   linear   programming model is to maximize the average efficiency of power generation inIndia for 2020 by giving preference to energy efficient technologies. This model is subjected to various constraints like potential, demand, running cost and Hydrogen / Carbon ratio, isolated load, emission and already installed capacities. Tora package is used to solve this linear program. Coal,  Gas,  Hydro  and  Nuclear  sources can are  supply around 87 %  of  power  requirement .  It’s concluded that we can produce power  at  overall  efficiency  of  37%  while  meeting  a  huge demand  of  13,00,000  GWh  of  electricity.  The objective function shows the scenario of highaverage efficiency with presence of 9% renewables. Maximum value   is   restricted   by   low   renewable   source’s efficiencies, emission constraints on fossil fuels and cost restriction on some of efficient technologies. This    model    shows    that    maximum    18%    of    total requirement   can   be   met   by   renewable itself which reduces average efficiency to 35.8%.   Improving technologies  of  renewable  sources  and  necessary  capacity addition  to  them in  regular  interval  will  enhance  their  role and existence against fossil fuels in future. The work involves conceptualizing, modeling, gathering information for data’s to be used in model for problem solving and presenting different scenarios for same objective.

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