• 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)

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.225-225
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• 2021

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• International Journal of Contents
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• v.6 no.2
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• pp.10-13
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• 2010

For applications that are grid enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. When jobs communicate with each other, the internet, or with storage resources, an advanced scheduler could schedule them to minimize communications traffic or minimize the distance of the communications. We propose an intelligent load distribution algorithm to minimize communications traffic and distance of the communications using genetic algorithm. The experiments show the proposed load redistribution algorithm performs efficiently in the variance of load in grid environments.

• International Journal of Contents
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• v.3 no.1
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• pp.34-38
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• 2007

Load redistribution is a critical resource in computer system. In sender-initiated load redistribution algorithms, the sender continues to send unnecessary request messages for load transfer until a receiver is found while the system load is heavy. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, we propose a genetic algorithm based approach for improved sender-initiated load redistribution in distributed systems. Compared with the conventional sender-initiated algorithms, the proposed algorithm decreases the response time and task processing time.

• Journal of Microbiology and Biotechnology
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• v.21 no.4
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• pp.374-378
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• 2011

To find alternative genetic resources for D-serine dehydratase (E.C. 4.3.1.18, dsdA) mediating the deamination of D-serine into pyruvate, metagenomic libraries were screened. The chromosomal dsdA gene of a wild-type Escherichia coli W3110 strain was disrupted by inserting the tetracycline resistance gene (tet), using double-crossover, for use as a screening host. The W3110 dsdA::tet strain was not able to grow in a medium containing D-serine as a sole carbon source, whereas wild-type W3110 and the complement W3110 dsdA::tet strain containing a dsdA-expression plasmid were able to grow. After introducing metagenome libraries into the screening host, a strain containing a 40-kb DNA fragment obtained from the metagenomic souce derived from a compost was selected based on its capability to grow on the agar plate containing D-serine as a sole carbon source. For identification of the genetic resource responsible for the D-serine degrading capability, transposon-${\mu}$ was randomly inserted into the 40-kb metagenome. Two strains that had lost their D-serine degrading ability were negatively selected, and the two 6-kb contigs responsible for the D-serine degrading capability were sequenced and deposited (GenBank code: HQ829474.1 and HQ829475.1). Therefore, new alternative genetic resources for D-serine dehydratase was found from the metagenomic resource, and the corresponding ORFs are discussed.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.10
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• pp.1381-1386
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• 2014

In spite of variation in coat color, size, and production traits among indigenous Bangladeshi cattle populations, genetic differences among most of the populations have not been investigated or exploited. In this study, we used a high-density bovine single nucleotide polymorphism (SNP) 80K Bead Chip derived from Bos indicus breeds to assess genetic diversity and population structure of 2 Bangladeshi zebu cattle populations (red Chittagong, n = 28 and non-descript deshi, n = 28) and a semi-domesticated population (gayal, n = 17). Overall, 95% and 58% of the total SNPs (69,804) showed polymorphisms in the zebu and gayal populations, respectively. Similarly, the average minor allele frequency value was as high 0.29 in zebu and as low as 0.09 in gayal. The mean expected heterozygosity varied from $0.42{\pm}0.14$ in zebu to $0.148{\pm}0.14$ in gayal with significant heterozygosity deficiency of 0.06 ($F_{IS}$) in the latter. Coancestry estimations revealed that the two zebu populations are weakly differentiated, with over 99% of the total genetic variation retained within populations and less than 1% accounted for between populations. Conversely, strong genetic differentiation ($F_{ST}=0.33$) was observed between zebu and gayal populations. Results of population structure and principal component analyses suggest that gayal is distinct from Bos indicus and that the two zebu populations were weakly structured. This study provides basic information about the genetic diversity and structure of Bangladeshi cattle and the semi-domesticated gayal population that can be used for future appraisal of breed utilization and management strategies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2350-2353
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• 2009

For applications that are grid enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. When jobs communicate with each other, the internet, with storage resources, an advanced scheduler could schedule them to minimize communications traffic or minimize the distance of the communications. We propose an load redistribution algorithm to minimize communication traffic and distance of the communication using genetic algorithm. The experiments show the proposed load redistribution algorithm performs efficiently in the variance of load in grid environments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.