• 한국공간구조학회논문집
• /
• 제2권3호
• /
• pp.93-102
• /
• 2002

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• 한국공간정보시스템학회:학술대회논문집
• /
• 한국공간정보시스템학회 2004년도 춘계 학술발표회 논문집 제1권1호(통권1호)
• /
• pp.43-52
• /
• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Architectural research
• /
• 제4권1호
• /
• pp.33-38
• /
• 2002

The objective of this study is the development of discrete optimum design algorithms which is based on the genetic algorithms. The developed algorithms was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses structures and the constraints are stresses and displacements. This study solves the problem by introducing the genetic algorithms. The genetic algorithms consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed discrete optimum design algorithms was verified by applying the algorithms to optimum design examples.

• 한국공간구조학회논문집
• /
• 제1권1호
• /
• pp.125-134
• /
• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• 한국강구조학회 논문집
• /
• 제13권6호
• /
• pp.673-681
• /
• 2001

본 연구에서는 다설계 변수와 다제약 조건으로 구성된 단면, 형상 및 위상을 동시에 고려하는 구조물의 이산화 최적설계문제를 유전자알고리즘을 이용하여 체계화하였다. 본 연구에서는 유전자알고리즘의 적용방법을 초기화절차, 진화적 절차 그리고 유전적 절차로 구성하였다. 초기화절차에서는 한 세대의 개체 수만큼 염색체를 생성하고 진화적 절차는 구조해석의 결과를 분석하여 적합도를 계산하였다. 그리고 유전적 절차는 번식과 교배 및 돌연변이를 통하여 다음세대의 유전자를 생성하게된다. 이렇게 진화적 절차와 유전적 절차를 반복 수행하여 최적 해를 탐색한다. 본 연구에서는 설계자가 궁극적 목표로 하는 구조물의 응력 해석과 단면, 형상 및 위상최적설계를 동시에 수행할 수 있는 이산화 최적설계프로그램을 개발하고, 설계 예를 들어 비교 고찰하였다.

• 한국정밀공학회지
• /
• 제15권3호
• /
• pp.82-87
• /
• 1998

In order to provide a manufacturing system with efficiency and flexibility to cope with the changes in shop floor status, the integration of process planning and operations scheduling is required. In this paper, an integrated system of process planning and operations scheduling based on the concept of process net model and genetic algorithm is suggested. The process net model includes the alternative process plans. The integrated system is applied for prismatic parts.

• 한국시뮬레이션학회:학술대회논문집
• /
• 한국시뮬레이션학회 1998년도 추계학술대회 및 정기총회
• /
• pp.199-203
• /
• 1998

Process planning and scheduling are traditionally regarded as separate tasks performed sequentially. But if two tasks are performed concurrently, greater performance can be achieved. In this study, we propose new approach to integration of process planning and scheduling. We propose new process planning combinations selection method using simulation based genetic algorithms. Computational experiments show that proposed method yield better performance when compared with existing methods.

• 한국정밀공학회지
• /
• 제15권12호
• /
• pp.72-80
• /
• 1998

In this study, a design approach based on genetic algorithm is proposed to solve the manufacturing cell design problem considering alternative process plans and alternative machines. The problem is formulated as a 0-1 integer programming model which considers several manufacturing parameters, such as demand and processing time of part, machine capacity, manufacturing cell size, and the number of machines in a machine cell. A genetic algorithm is used to determine process plan for each part, part family and machine cell simultaneously.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2002년도 추계학술대회 논문집
• /
• pp.612-615
• /
• 2002

The objective of this study is to develop an integrated process planning system which can flexibly cope with the status changes in a shop floor by utilizing the concept of Non-Linear and Closed-Loop Process Planning(NCPP). In this paper, Genetic Algorithm(GA) is employed in order to quickly generate feasible setup sequences for minimizing the makespan and tardiness under an NCPP. The genetic algorithm developed in this study for getting the machine load utilizes differentiated mutation rate and method in order to increase the chance to avoid a local optimum and to reach a global optimum. Also, it adopts a double gene structure for the sake of convenient modeling of the shop floor. The last step in this system is a simulation process which selects a proper process plan among alternative process plans.

• 한국CDE학회논문집
• /
• 제18권2호
• /
• pp.129-137
• /
• 2013

This paper proposed the scheduling method for tire mixing processes using the genetic algorithm. The characteristics of tire mixing process have the manufacturing routing, operation machine and operation time by compound types. Therefore, the production scheduling has to consider characteristics of the tire mixing process. For the reflection of the characteristics, we reviewed tire mixing processes. Also, this paper introduces the genetic algorithm using the crossover and elitist preserving selection strategy. Fitness is measured by the makespan. The proposed genetic algorithm has been implemented and tested with two examples. Experimental results showed that the proposed algorithm is superior to conventional heuristic algorithm.