• ETRI Journal
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• v.32 no.4
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• pp.634-637
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• 2010

This letter presents a model for a dynamic collaboration (DC) platform among cloud providers (CPs) that prevents adverse business impacts, cloud vendor lock-in and violation of service level agreements with consumers, and also offers collaborative cloud services to consumers. We consider two major challenges. The first challenge is to find an appropriate market model in order to enable the DC platform. The second is to select suitable collaborative partners to provide services. We propose a novel combinatorial auction-based cloud market model that enables a DC platform among CPs. We also propose a new promising multi-objective optimization model to quantitatively evaluate the partners. Simulation experiments were conducted to verify both of the proposed models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.499-506
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• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several sub design structure matrices (DSMs) and processing them in parallel This paper proposes a new method for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multi-objective genetic algorithm and two sample test cases are presented to show the effect of the suggested decomposition method.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.170-175
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• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• The Journal of Society for e-Business Studies
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• v.7 no.2
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• pp.143-156
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• 2002

The objective of supply chain planning is to satisfy the requirements for minimizing inventory costs, transportation costs, and lead times throughout the supply, production and distribution stage dispersed geographically. Therefore, the supply chain planning system should have functionalities to resolve complex optimization problems that have characteristics of multi-stage and multi-product. Ant the system should also support collaborative decision making among distributed business partners. In this study, we proposed a distributed architecture for the supply chain planning system. To do this, we analyzed functional requirements by using IDEF-0(ICAM Definition-0) methodology, defined required components, and designed each component by using object-oriented methodology. We implemented a prototype system based on CORBA (Common Object Request Broker Architecture) to show that the proposed distributed architecture based on component technology is feasible and can solve supply chain planning problem collaboratively.