다목적 시뮬레이션 통합 하이브리드 유전자 알고리즘을 사용한 수동 조립라인의 동기 작업 모델

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 40권 / 4호
ㆍ저자명 : 무하마드 임란, 강창욱

목차

1. Introduction
2. Literature Review
3. Development of Mathematical Model
3.1 Problem Statement
3.2 Model Assumptions
3.3 Notations
4. Multi-Objective Simulation IntegratedHybrid Genetic Algorithm(MO-SHGA)
4.1 The Proposed Multi-Objective Approach forMO-GA and MO-SHGA
4.2 Model Behavior
4.3 Numerical Example
5. Results and Discussion
5.1 CASE I : Deterministic Multi-ObjectiveSimulation Integrated Hybrid GeneticAlgorithm (MO-SHGA)
5.2 CASE-II : Stochastic Multi-ObjectiveSimulation Integrated Hybrid GeneticAlgorithm (MO-SHGA)
6. Conclusions
Acknowledgment
References

영어 초록

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

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