Recent studies showed that tight junctions (TJs) integrity and assembly are required for blastocyst development in mouse and pig models. However, the biological functions of TJs associated with embryo implantation and maintenance of pregnancy were not investigated yet. To examine whether disrupted TJs affect further embryo development, we employed RNAi approach and inhibitor treatment. The embryos were injected with Cxadr (Coxsackievirus and adenovirus receptor) siRNA for knock down (KD) and treated with Adam10 (A Disintegrin and Metalloproteinase specific inhibitor 10; GI254023X; SI). We compared blastocyst development and paracellular sealing assay using FITC dextran uptake between control and KD or SI embryos. Finally, we transferred control and Cxadr KD or Adam 10 SI treated blastocyst to uteri of recipients. Cxadr KD and Adam 10 SI showed lower blastocyst development and more permeable to FITC-dextran. Moreover, we observed that half of KD and inhibited embryos failed to maintain pregnancies after the second trimester. Our findings suggested that TJs integrity is required for the maintenance of pregnancy and can be used as a selective marker for the successful application of assisted reproduction technologies.

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.

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 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 with multiple 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. 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.

본 연구는 기획단계, 설계단계 및 시공단계에서 사용가능한, 모듈러 강교량 상부구조 정보모델 생성의 효율성을 향상하기 위하여, 파라메트릭 모델링(parametric modeling)을 통해 상부모듈을 생성하고 이들을 조합할 수 있는 방안을 제시하였다. 파라메트릭 모델링을 적용하기 위한 요소 도출을 위하여 상부구조 구성요소 분류를 수행하였고, 분류된 요소를 결합 조건에 따라 13개의 종으로 구분하였다. 또한 모듈러 강교량 공장 제작 단위인 상부모듈의 조합을 통해 안정적인 상부구조 생성을 위한 세 가지 조합조건을 도출하였다. 파라메트릭 모델링을 통한 상부모듈 생성 시 구성요소 형상에 조합조건을 반영하는 변수를 도출하였다. 이를 본 연구에서 개발한 Building Information Model(BIM) 소프트웨어기반의 생성 인터페이스를 활용하여 타입과 변수에 따른 상부모듈 구성요소들을 생성하였다. 이때 생성된 상부모듈에는 상부구조로 조합하기 위한 정보를 반영하였으며, 이를 검증하기 위하여 Industry Foundation Classes(IFC) 기반의 정보모델을 생성하고, 반영한 속성정보를 확인함으로써 상부구조 생성에 활용할 수 있음을 확인하였다.

This paper considers the sequencing of products in mixed model assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time, the number of utility workers, and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search is a heuristic method which can provide a near optimal solution in real time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

The assembly line balancing problem has been focused by many research works because the efficient management of the assembly line might influence not only the quality of the products but also the working conditions for the workers. This paper deals with U

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

In this thesis presents line balancing problems of two-sided and mixed model assembly line widely used in practical fields using genetic algorithm for reducing throughput time, cost of tools and fixtures and improving flexibility of assembly lines. Two-sided and mixed model assembly line is a special type of production line where variety of product similar in product characteristics are assembled in both sides. This thesis proposes the genetic algorithm adequate to each step in tow-sided and mixed model assembly line with suitable presentation, individual, evaluation function, selection and genetic parameter. To confirm proposed genetic algorithm, we apply to increase the number of tasks in case study. And for evaluation the performance of proposed genetic algorithm, we compare to existing algorithm of one-sided and mixed model assembly line. The results show that the algorithm is outstanding in the problems with a larger number of stations or larger number of tasks.

The assembly tolerance design methods have applied linear or nonlinear programming methods and used simulation method and search algorithms to optimize the tolerance allocation of each part in an assembly. However, those methods are only considered to the relationship between tolerance and manufacturing cost, which do not consider a quality loss cost for each part tolerance. In this paper, the integrated simulation model used genetic algorithm and the Monte-Carlo simulation method was developed for the allocation of the optimal tolerance considering the manufacturing cost and quality loss cost.