• Advances in Computational Design
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• v.2 no.3
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• pp.241-256
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• 2017

This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.

• Ocean Systems Engineering
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• v.5 no.2
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• pp.91-107
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• 2015

Ship hull optimization is categorized as a bound, multi variable, multi objective problem with nonlinear constraints. In such analysis, where the objective function representing the performance of the ship generally requires computationally involved hydrodynamic interaction evaluation methods, the objective functions are not smooth. Hence, the evolutionary techniques to attain the optimum hull forms is considered as the most practical strategy. In this study, a parametric ship hull form represented by B-Spline curves is optimized for multiple performance criteria using Genetic Algorithm. The methodology applied to automate the hull form generation, selection of optimization solvers and hydrodynamic parameter calculation for objective function and constraint definition are discussed here.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.1
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• pp.51-65
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• 2000

Multiple U-typed producton lines are increasingly accepted in modern marufacturing system for the flexbility to adjust to changes in demand. This paper considers multiple U line balancing with the objective of minimizing cycle time considering the moving time of workforce given the number of workstation. Like the traditional line balancing problem this problem is NP-hard. In this paper, we show how genetic algorithm can be used to solve multiple U line balancing problem. For this, an encoding and a decoding method suitable to the problem are presented. Proper genetic operators are also employed. Extensive computational experiments are carried out to show the performance of the performance of the purposed algorithm. The computational results show that the algorithm is promising in solution quality.

• IE interfaces
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• v.14 no.4
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• pp.374-384
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• 2001

The objective of this paper is to design the dynamic route guidance system(DRGS) and develop a genetic algorithm(GA) for finding the multiple shortest paths in real traffic network. The proposed GA finds a collection of paths between source and destination considering turn-restrictions, U-turn, and P-turn that are genetically evolved until an acceptable solution is reached. This paper also shows the procedure to find the multiple shortest paths in traffic network of Seoul.

• Industrial Engineering and Management Systems
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• v.3 no.2
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• pp.116-122
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• 2004

This paper presents a process plan selection model with multiple objectives. The process plans for all parts should be selected under multiple objective environment as follows: (1) minimizing the sum of machine processing and material handling time of all the parts considering realistic shop factors such as production volume, processing time, machine capacity, and capacity of transfer device. (2) balancing the load between machines. A multiple objective mathematical model is proposed and an evolutionary algorithm with the adaptive recombination strategy is developed to solve the model. To illustrate the efficiency of proposed approach, numerical examples are presented. The proposed approach is found to be effective in offering a set of satisfactory Pareto solutions within a satisfactory CPU time in a multiple objective environment.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.501-514
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• 1999

Multiple U-typed production lines are increasingly accepted in modern manufacturing system for the flexibility to adjust to changes in demand. This paper considers multiple U line balancing with the objective of minimizing cycle time considering of moving time of workforce given the number of workstation. Like the traditional line balancing problem this problem is NP-hard. In this paper, we show how genetic algorithm can be used to solve multiple U line balancing. For this, an encoding and a decoding method suitable to the problem are presented. Proper genetic operators are also employed. Extensive computational experiments are carried out to show the performance of the proposed algorithm. The computational results show that the algorithm is promising in solution quality.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.3
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• pp.275-283
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• 2012

This paper presents a design method of a robust tracking controller satisfying multiple constraints using genetic algorithm. A robust $H_{\infty}$ constraint with loop shaping is used to address disturbance attenuation with error limits and a loop gain constraint is considered so as not to enlarge the tracking loop gain and bandwidth unnecessarily. The robust $H_{\infty}$ constraint is expressed by a matrix inequality and the loop gain constraint is considered as an objective function so that genetic algorithm can be applied. Finally, a robust tracking controller can be obtained by integrating genetic algorithm with LMI approach. The proposed tracking controller design method is applied to the track-following system of an optical DVD recording drive and is evaluated through the experimental results.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.203-208
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• 2002

In case of the problems with multiple plants, alternative operation sequence, alternative machine, setup time, and transportation time between plants, we need a robust methodology for the integration of process planning and scheduling in supply chain. The objective of this model is to minimize the tardiness and to maximize the resource utilization. So, we propose a multi-objective model with limited-capacity constraint. To solve this model, we develope an efficient and flexible model using adaptive genetic algorithm(AGA), compared to traditional genetic algorithm(TGA)

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.677-689
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• 2022

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.