Layout Method of a FPSO (Floating, Production, Storage, and Off-Loading Unit) Using the Optimization Technique

2014 ◽  
Author(s):  
Namkug Ku ◽  
Se-Yong Jeong ◽  
Myung-Il Roh ◽  
Hyun-Kyoung Shin ◽  
Sol Ha ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Optimization Problem ◽  
Optimization Technique ◽  
Salt Content ◽  
Layout Design ◽  
Mixed Integer ◽  
Optimal Layout ◽  
Special Environment

In the case of a floating offshore plant such as FPSO (Floating, Production, Storage, and Off-loading unit), many equipment should be installed in the limited space, as compared with an onshore plant. At this time, special conditions, such as the movement due to external force by wind and wave, salt content, and so on, should be also considered because the floating offshore plant should be operated in the special environment of ocean. The requirement for an optimal layout method of the plant has been raised due to much considerations for layout design. Thus, a layout method of the floating offshore plant was proposed in this study. For this, an optimization problem for layout was mathematically formulated, and then an optimization algorithm was implemented based on the genetic algorithm or mixed integer linear programming in order to solve it. To evaluate the applicability of the proposed method, it was applied to an example of FPSO and LNG FPSO topsides. As a result, it was shown that the proposed method can be applied to layout design of the floating offshore plant.

APPLICATION OF A POLYGENERATION OPTIMIZATION TECHNIQUE FOR A HOSPITAL IN NORTHERN ONTARIO

2014 ◽  
Vol 38 (1) ◽  
pp. 45-62 ◽  
Author(s):  
Alberto Romero ◽  
Monica Carvalho ◽  
Dean L. Millar
Keyword(s):  
Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Primary Objective ◽  
Mixed Integer ◽  
Energy Costs ◽  
Northern Ontario ◽  
Polygeneration System ◽  
To Receive

An optimization technique based on Mixed Integer Linear Programming (MILP) was applied to a hospital located in Sudbury, Ontario, Canada. The energy services included electricity, heat, steam, and coolth. Different options for the system’s operation were also considered in the optimization procedure. The optimization model determines the optimal configuration of the polygeneration system and the best operational strategy. The primary objective for optimization is to minimize the hospital’s annual energy costs. This objective can be achieved by consuming low-priced natural gas, operating the gas engines at full load to generate electricity, and selling all of this electricity into the provincial grid, which ensures the hospital’s eligibility to receive government payments for cogeneration.

A NEW APPROACH TO SELECT THE BEST SUBSET OF PREDICTORS IN LINEAR REGRESSION MODELLING: BI-OBJECTIVE MIXED INTEGER LINEAR PROGRAMMING

2019 ◽  
Vol 61 (1) ◽  
pp. 64-75 ◽  
Author(s):  
HADI CHARKHGARD ◽  
ALI ESHRAGH
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Optimization Problem ◽  
Computational Study ◽  
Mixed Integer ◽  
Programming Approach ◽  
Objective Functions ◽  
New Approach

We study the problem of choosing the best subset of $p$ features in linear regression, given $n$ observations. This problem naturally contains two objective functions including minimizing the amount of bias and minimizing the number of predictors. The existing approaches transform the problem into a single-objective optimization problem. We explain the main weaknesses of existing approaches and, to overcome their drawbacks, we propose a bi-objective mixed integer linear programming approach. A computational study shows the efficacy of the proposed approach.

Reliability optimization in stochastic domain via genetic algorithm

2014 ◽  
Vol 31 (6) ◽  
pp. 698-717 ◽  
Author(s):  
Laxminarayan Sahoo ◽  
Asoke Kumar Bhunia ◽  
Dilip Roy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Mixed Integer ◽  
Reliability Optimization ◽  
Programming Technique ◽  
Content Type ◽  
Real Coded Genetic Algorithm ◽  
Interval Valued

Purpose – The purpose of this paper is to formulate the reliability optimization problem in stochastic and interval domain and also to solve the same under different stochastic set up. Design/methodology/approach – Stochastic programming technique has been used to convert the chance constraints into deterministic form and the corresponding problem is transformed to mixed-integer constrained optimization problem with interval objective. Then the reduced problem has been converted to unconstrained optimization problem with interval objective by Big-M penalty technique. The resulting problem has been solved by advanced real coded genetic algorithm with interval fitness, tournament selection, intermediate crossover and one-neighbourhood mutation. Findings – A new optimization technique has been developed in stochastic domain and the concept of interval valued parameters has been integrated with the stochastic setup so as to increase the applicability of the resultant solution to the interval valued nonlinear optimization problems. Practical implications – The concept of probability distribution with interval valued parameters has been introduced. This concept will motivate the researchers to carry out the research in this new direction. Originality/value – The application of genetic algorithm is extended to solve the reliability optimization problem in stochastic and interval domain.

Sign in / Sign up

Export Citation Format

Share Document