• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.206-206
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• 2021

The necessity for appropriate management of water resources infrastructures such as reservoirs, levees, and dikes is increasing due to unexpected hydro-climate irregularities and rising water demands. To meet this need, past studies have focused on advancing theoretical optimization algorithms such as nonlinear programming, dynamic programming (DP), and genetic programming. Yet, the optimally derived theoretical solutions are limited to be directly implemented in making release decisions in the real-world systems for a variety of reasons. This study first aims to comparatively analyze the two prominent optimization methods, DP and evolutionary multi-objective direct policy search (EMODPS), under historical inflow series using K-fold cross validation. A total of six optimization models are formed each with a specific formulation. Then, one of the optimization models was coupled with the actual zone-based hedging rule that has been adopted in practice. The proposed methodology was applied to Boryeong Dam located in South Korea with conflicting objectives between supply and demand. As a result, the EMODPS models demonstrated a better performance than the DP models in terms of proximity to the ideal. Moreover, the incorporation of the real-world policy with the optimal solutions improved in all indices in terms of the supply side, while widening the range of the trade-off between frequency and magnitude measured in the sides of demand. The results from this study once again highlight the necessity of closing the gap between the theoretical solutions with the real-world implementable policies.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.175-187
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• 2004

We consider the simultaneous selection of part routes for multiple part types in Flexible Manufacturing Systems (FMSs). Using an optimization framework we investigate two alternative route assignment policies. The one, called routing mix policy in the literature, specifies the optimal proportion of each part type to be produced along its alternative routes, assuming that the proportions can be kept during execution. The other one, which we propose and call pallet allocation policy, partitions the pallets assigned to each part type among the routes. The optimization framework used is a nonlinear programming superimposed on a closed queueing network model of an FMS which produces multiple part types with distinct repeated visits to certain workstations. The objective is to maximize the weighted throughput. Our study shows that the simultaneous use of multiple routes leads to reduced bottleneck utilization, improved workload balance, and a significant increase in the FMS's weighted throughput, without any additional capital investments. Based on numerical work, we also conjecture that pallet allocation policy is more robust than routing mix policy, operationally easier to implement, and may yield higher revenues.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.346-357
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• 2010

Opportunistic scheduling provides the capability of resource management in wireless networks by taking advantage of multiuser diversity and by allowing delay variation in delivering data packets. It generally aims to maximize system throughput or guarantee fairness and quality of service (QoS) requirements. In this paper, we develop an extended proportional fair (PF) scheduling policy that can statistically guarantee three kinds of QoS. The scheduling policy is derived by solving the optimization problems in an ideal system according to QoS constraints. We prove that the practical version of the scheduling policy is optimal in opportunistic scheduling systems. As each scheduling policy has some parameters, we also consider practical parameter adaptation algorithms that require low implementation complexity and show their convergences mathematically. Through simulations, we confirm that our proposed schedulers show good fairness performance in addition to guaranteeing each user's QoS requirements.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.170-175
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• 2010

Maintaining a complex repairable system can be achieved by repairing, replacing, or any other activities. This paper proposes a joint optimization policy that is composed with ordering and replacing under minimal repair for the complex system. For this purpose, we derive the expected cost due to the minimal repair, ordering, downtime, inventory costs, and salvage value of units that follow generally distribution. Some properties about the optimum ordering policy that are suggested for our purpose shows that the optimum ordering policy minimizing the expected cost is either one of the two typical policies : (1) the original unit is replaced as soon as the ordered spare is delivered, or (2) the delivered spare is used as inventory part until the original unit fails.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.81-86
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• 2005

This paper deals with power system stabilization problem using optimal foraging theory, which formulates foraging as an optimization problem and via computational or analytical methods can provide an optimal foraging policy that specifies how foraging decisions are made. It is possible that the local environment where a population of bacteria live changes either gradually (e.g., via consumption of nutrients) or suddenly due to some other influence. This objective scrutinizes to possibilities for power system stabilization by utilizing how mobile behaviors in both individual and groups of bacteria implement foraging and optimization.

• 한국디지털정책학회:학술대회논문집
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• 2003.12a
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• pp.263-273
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• 2003

Recognizing the importance of the database query optimization design methods, we implemented mobile database with mobile program (J2ME) which is a useful database procedures. In doing so, we emphasize the logical query optimization which brings mobile database to performance improvement. The research implies that the suggested mobile program (J2ME) would contribute to the realization of the efficient mobile database as the related technology develops in the future.

• Environmental Engineering Research
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• v.21 no.4
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• pp.409-419
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• 2016

As a major energy consumption province, the issue about the carbon emissions in Hebei Province, China has been concerned by the government. The carbon emissions can be effectively reduced due to a more rational energy consumption structure. Thus, in this paper the constraint of low carbon emissions is considered as a foundation and four energies--coal, petroleum, natural gas and electricity including wind power, nuclear power and hydro-power etc are selected as the main analysis objects of the adjustment of energy structure. This paper takes energy cost minimum and carbon trading cost minimum as the objective functions based on the economic growth, energy saving and emission reduction targets and constructs an optimization model of energy consumption structure. And empirical research about energy consumption structure optimization in 2015 and 2020 is carried out based on the energy consumption data in Hebei Province, China during the period 1995-2013, which indicates that the energy consumption in Hebei dominated by coal cannot be replaced in the next seven years, from 2014 to 2020, when the coal consumption proportion is still up to 85.93%. Finally, the corresponding policy suggestions are put forward, according to the results of the energy structure optimization in Hebei Province.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.239-246
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• 2021

This article substantiates the scientific provisions for modelling the level of Ukraine's public financial security taking into account the impact of budget policy, in the process of which identified indicators of budget policy that significantly affect the public financial security and the factors of budget policy based on regression analysis do not interact closely with each other. A seven-factor regression equation is constructed, which is statistically significant, reliable, economically logical, and devoid of autocorrelation. The objective function of maximizing the level of public financial security is constructed and strategic guidelines of budget policy in the context of Ukraine's public financial security are developed, in particular: optimization of the structure of budget revenues through the expansion of the resource base; reduction of the budget deficit while ensuring faster growth rates of state and local budget revenues compared to their expenditures; optimization of debt serviced from the budget through raising funds from the sale of domestic government bonds, mainly on a long-term basis; minimization of budgetary risks and existing threats to the public financial security by ensuring long-term stability of budgets etc.

• Journal of the Korea Society for Simulation
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• v.27 no.4
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• pp.1-8
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• 2018

The most common way to control a target point using artificial intelligence is through reinforcement learning. However, it had to process complicated calculations that were difficult to implement in order to process reinforcement learning. In this paper, the enhanced Proximal Policy Optimization (PPO) algorithm was used to simulate finding the planned flight trajectory to reach the target point in the virtual environment. In this paper, we simulated how this problem was used to find the planned flight trajectory to reach the target point in the virtual environment using the enhanced Proximal Policy Optimization(PPO) algorithm. In addition, variables such as changes in trajectory, effects of rewards, and external winds are added to determine the zero conditions of external environmental factors on flight trajectory learning, and the effects on trajectory learning performance and learning speed are compared. From this result, the simulation results have shown that the agent can find the optimal trajectory in spite of changes in the various external environments, which will be applicable to the actual vehicle.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.28-36
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• 2016

Recently, owing to the development of ICT industry and wide spread of smart phone, the number of people who use car sharing service are increased rapidly. Currently two-way car sharing system with same rental and return locations are mainly operated since this system can be easily implemented and maintained. Currently the demand of one-way car sharing service has increase explosively. But this system have several obstacle in operation, especially, vehicle stock imbalance issues which invoke vehicle relocation. Hence in this study, we present an optimization approach to depot location and relocation policy in one-way car sharing systems. At first, we modelled as mixed-integer programming models whose objective is to maximize the profits of a car sharing organization considering all the revenues and costs involved and several constraints of relocation policy. And to solve this problem efficiently, we proposed a new method based on particle swarm optimization, which is one of powerful meta-heuristic method. The practical usefulness of the approach is illustrated with a case study involving satellite cities in Seoul Metrolitan Area including several candidate area where this kind systems have not been installed yet and already operating area. Our proposed approach produced plausible solutions with rapid computational time and a little deviation from optimal solution obtained by CPLEX Optimizer. Also we can find that particle swarm optimization method can be used as efficient method with various constraints. Hence based on this results, we can grasp a clear insight into the impact of depot location and relocation policy schemes on the profitability of such systems.