• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.197-198
• /
• 2008

In this paper, we study an opportunistic scheduling scheme for the OFDMA system with streaming services. The service is modeled by using the appropriate utility function. We formulate a stochastic optimization problem that aims at maximizing network utility while satisfying the QoS requirement of each user. The problem is solved by using the dual approach and the stochastic sub-gradient algorithm.

• Proceedings of the KIEE Conference
• /
• 2003.11a
• /
• pp.447-449
• /
• 2003

In competitive electricity markets, maintenance schedule is submitted by Genco's and Transco's, and coordinated by ISO with the adequacy criterion. This paper presents an alternative coordination procedure by ISO on the maintenance schedule.

• Journal of Communications and Networks
• /
• v.14 no.5
• /
• pp.554-565
• /
• 2012

In this paper, we study a cross-layer and end-to-end optimization problem for the integrated wireless and wireline network that consists of one wireline core network and multiple wireless access networks. We consider joint end-to-end flow control/distribution at the transport and network layers and opportunistic scheduling at the data link and physical layers. We formulate a single stochastic optimization problem and solve it by using a dual approach and a stochastic sub-gradient algorithm. The developed algorithm can be implemented in a distributed way, vertically among communication layers and horizontally among all entities in the network, clearly showing what should be done at each layer and each entity and what parameters should be exchanged between layers and between entities. Numerical results show that our cross-layer and end-to-end optimization approach provides more efficient resource allocation than the conventional layered and separated optimization approach.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1986.10a
• /
• pp.114-118
• /
• 1986

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1999.04a
• /
• pp.279-279
• /
• 1999

• Water Engineering Research
• /
• v.3 no.1
• /
• pp.63-74
• /
• 2002

• Journal of Korea Water Resources Association
• /
• v.52 no.8
• /
• pp.565-573
• /
• 2019

An optimization model of water intake planning is developed based on a linear programming (LP) for the intelligent water purification plant operation system. The proposed optimization model minimizes the water treatment costs of raw water purification by considering a time-delay of treatment process and hourly electricity tariff, which is subject to various operation constraints, such as water intake limit, storage tank capacity, and water demand forecasts. For demonstration, the developed model is applied to H water purification center. Here, we have tested three optimization strategies and the results are compared and analyzed in economic and safety aspects. The optimization model is expected to be used as a decision support tool for optimal water intake scheduling of domestic water purification centers.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.392-392
• /
• 2000

This study performs optimization of paper mill scheduling using MINLP(Mixed-Integer Non-Linear Programming) method and 2-step decomposing strategy. Paper mill process is normally composed of five units: paper machine, coater, rewinder, sheet cutter and roll wrapper/ream wrapper. Various kinds of papers are produced through these units. The bottleneck of this process is how to cut product papers efficiently from raw paper reel and this is called trim loss problem or cutting stock problem. As the trim must be burned or recycled through energy consumption, minimizing quantity of the trim is important. To minimize it, the trim loss problem is mathematically formulated in MINLP form of minimizing cutting patterns and trim as well as satisfying customer's elder. The MINLP form of the problem includes bilinearity causing non-linearity and non-convexity. Bilinearity is eliminated by parameterization of one variable and the MINLP form is decomposed to MILP(Mixed-Integer Linear programming) form. And the MILP problem is optimized by means of the optimization package. Thus trim loss problem is efficiently minimized by this 2-step optimization method.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.29 no.12
• /
• pp.638-644
• /
• 2017

Optimal cooling operation algorithm was developed based on a simulation case of a single family house model equipped with renewable energy facility. EnergyPlus simulation results were used as virtual test data. The model contained three energy storage elements: thermal heat capacity of the living room, chilled water storage tank, and battery. Their charging and discharging schedules were optimized so that daily electricity bill became minimal. As an optimization tool, linear programming was considered because it was possible to obtain results in real time. For its adoption, EnergyPlus-based house model had to be linearly approximated. Results of this study revealed that dynamic cooling load of the living room could be approximated by a linear RC model. Scheduling based on the linear programming was then compared to that by a nonlinear optimization algorithm which was made using GenOpt developed by a national lab in USA. They showed quite similar performances. Therefore, linear programming can be a practical solution to optimal operation scheduling if linear dynamic models are tuned to simulate their real equivalents with reasonable accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2282-2303
• /
• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.