• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.205-210
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• 2007

The integration method of binary and real encoding in genetic algorithm is proposed to deal with design variables of various types in gear drive design. The method is applied to optimum design of multi-stage gear drive. Integer and Discrete type design variables represent the number of teeth and module, and continuous type design variables represent face width, helix angle and addendum modification factor etc. The proposed genetic algorithm is applied for the gear ratio optimization and the volume optimization(minimization) of multi-stage geared motor which is used in field. In result, the proposed design optimization method shows an effectiveness in optimum design process and the new design has a better results compared with the existing design.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.299-299
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• 2000

In this paper, a determination method of number of AGVs fer introducing to the multi-path material handling systems is presented by using genetic algorithm. For serving the raw material to each work stations automatically, there needs to introduce a AGVs for transfer the raw martial. To reduce the overall production cost in the material handling systems, however, a trade off exists between the amount of inventory hold on the shop floor and the number of AGVs needed to provide adequate service. In this paper, firstly a objective function which included the net present fixed costs of each stations and each purchased AGVs, delivering cost. stock inventory cost, and safety stock inventory cost is presented. Secondly by using genetic algorithm, the optimal reorder quantity at each stations is decided, where the number of AGVs is increased step by step. From a simulation with different GA parameters, we can determine a optimal number of AGVs to reduce the overall production cost. Thus, the effectiveness of GA for determining the number of AGVs is verified in automated material handling systems.

• Earthquakes and Structures
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• v.11 no.2
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• pp.347-369
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• 2016

The main objective of this paper is the robust multi-objective optimization design of semi-active tuned mass damper (STMD) system using genetic algorithms and fuzzy logic. For optimal design of this system, it is required that the uncertainties which may exist in the system be taken into account. This consideration is performed through the robust design optimization (RDO) procedure. To evaluate the optimal values of the design parameters, three non-commensurable objective functions namely: normalized values of the maximum displacement, velocity, and acceleration of each story level are considered to minimize simultaneously. For this purpose, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) approach is used to find a set of Pareto-optimal solutions. The torsional effects due to irregularities of the building and/or unsymmetrical placements of the dampers are taken into account through the 3-D modeling of the building. Finally, the comparison of the results shows that the probabilistic robust STMD system is capable of providing a reduction of about 52%, 42.5%, and 37.24% on the maximum displacement, velocity, and acceleration of the building top story, respectively.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.233-242
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• 2015

This paper analyzes a multi-product inbound lot-sizing and outbound dispatching problem with multi-vehicle types in a third-party logistics distribution center. The product must be delivered to the customers within the delivery time window and backlogging is not allowed. Replenishing orders are shipped by several types of vehicles with two types of the freight costs, i.e., uniform and decreasing, are considered. The objective of this study is to determine the lot-size and dispatching schedules to minimize the total cost with the sum of inbound and outbound transportation and inventory costs over the entire time horizon. In this study, we mathematically derive a mixed-integer programming model and propose a genetic algorithm (GA1) based on a local search heuristic algorithm to solve large-scale problems. In addition, we suggest a new genetic algorithm (GA2) with an adjusting algorithm to improve the performance of GA1. The basic mechanism of the GA2 is to provide an unidirectional partial move of products to available containers in the previous period. Finally, we analyze the results of GA1 and GA2 by evaluate the relative performance using the gap between the objective values of CPLEX and the each algorithm.

• The KSFM Journal of Fluid Machinery
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• v.17 no.4
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• pp.19-29
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• 2014

In addition to the development of micro hydro turbine, the challenge in micro hydro turbine design as sustainable hydro devices is focused on the optimization of turbine runner blade which have decisive effect on the turbine performance to reach higher efficiency. A multi-objective optimization method to optimize the performance of runner blade of propeller turbine for micro turbine has been studied. For the initial design of planar blade cascade, singularity distribution method and the combination of the Bezier curve parametric technology is used. A non-dominated sorting genetic algorithm II(NSGA II) is developed based on the multi-objective optimization design method. The comparision with model test show that the blade charachteristics is optimized by NSGA-II has a good efficiency and load distribution. From model test and scale up calculation, the maximum prototype efficiency of the runner blade reaches as high as 90.87%.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.115-127
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• 2020

In this paper, a sustainable close-loop supply chain (SCLSC) model is proposed for effectively managing the production, distribution and handling process of mobile phone. The proposed SCLSC model aims at maximizing total profit as economic factor, minimizing total CO₂ emission amount as environmental factor, and maximizing social influence as social factor in order to reinforce sustainability in it. Since these three factors are represented as each objective function in modeling, the proposed SCLSC model can be taken into consideration as a multi-objective optimization problem and solved using a hybrid genetic algorithm (HGA) approach. In numerical experiment, three different scales of the SCLSC model are presented and the efficiency of the HGA approach is proved using various measures of performance.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.637-642
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• 2004

This paper presents a new shape design method for the multi-mode sensor that can detect selected multiple modes for the active vibration control of mechanical structures. The structure used for this study is an isotropic cantilever beam type with a PVDF(polyvinylidene fluoride) which is bonded onto the structure as a sensor. Characteristic behaviors of the sensor are related with the electrode shapes of PVDF. The shape optimization problem is solved by defining a new multi-objective function and using the genetic algorithm. Resulting electrode shape functions have good performances to detect the multiple vibration modes. The results of analytical simulations are compared with those of experiment works. The results agree well each other. Hence, the obtained experimental results give evidence for the validity of the presented theoretical analysis of the electrode shape design problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.109-117
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• 2022

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

• Journal of Power System Engineering
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• v.2 no.1
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• pp.52-58
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• 1998

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is applied to many engineering algorithm recently. In this paper, the combined optimization algorithm is proposed for multi-objective problem by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed algorithm is identified by using multi-peak function which have many local optimums and identification of the flexural rigidity for wire rope model.