• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2187-2195
• /
• 2017

In order to solve the AC optimal power flow (OPF) problem considering the generators' on/off status, it is necessary to model the problem as mixed-integer nonlinear programming (MINLP). Because the computation time to find the optimal solution to the mixed-integer AC OPF problem increases significantly as the system becomes larger, most of the existing solutions simplify the problem either by deciding the on/off status of generators using a separate unit commitment algorithm or by ignoring the minimum output of the generators. Even though this kind of simplification may make the overall computation time tractable, the results can be significantly erroneous. This paper proposes a novel algorithm for the mixed-integer AC OPF problem, which can provide a near-optimal solution quickly and efficiently. The proposed method is based on a combination of the outer approximation method and the relaxed AC OPF theory. The method is applied to a real-scale power system that has 457 generators and 2132 buses, and the result is compared to the branch-and-bound (B&B) method and the genetic algorithm. The results of the proposed method are almost identical to those of the compared methods, but computation time is significantly shorter.

• ETRI Journal
• /
• v.44 no.5
• /
• pp.746-758
• /
• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.7
• /
• pp.684-692
• /
• 2010

Based on the planar restricted three body problem formulation, optimized trajectories for the Earth-Moon transfer are obtained. Mixed impulsive and continuous thrust are assumed to be used, respectively, during the Earth departure and Earth-Moon transfer/Moon capture phases. The continuous, dynamic trajectory optimization problem is reformulated in the form of discrete optimization problem by using the method of direct transcription and collocation, and then is solved using the nonlinear programming software. Representative results show that the shape of optimized trajectory near the Earth departure and the Moon capture phases is dependent upon the relative weight between the impulsive and the continuous thrust.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.721-736
• /
• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.4081-4098
• /
• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• Journal of Biosystems Engineering
• /
• v.10 no.2
• /
• pp.55-62
• /
• 1985

박(朴)(1982, 1983, 1984 및 1985)이 개발(開發)한 배합사료공장(配合飼料工場)의 투자비용(投資費用) 및 운전비용(運轉費用)의 수학적모형(數學的模型)을 이용(利用)하여, 배합사료공장(配合飼料工場)의 적정(適正) 시스템의 설계(設計)를 예(例)를 들어 소개하였다. 적정(適正)시스템의 설계(設計)를 위(爲)하여 비선형(非線型) 프로그램의 "Single Objective Programming Problem(단일목적함수(單一目的函數))"와 "'Multiple Objective Decision Making Method(다목적함수(多目的函數))"의 2가지 방법(方法)을 적용(適用)하였다. Single Objective Programming Problem에서는 "Generalized Reduced Gradient(GRG) Method"를 이용(利用)하였고, Multiple Objective Decision Making Method(MODM)에서는 "Interactive Nonlinear Goal Program(INGP)"를 이용(利用)하였으며 그 결과(結果)는 다음의 몇가지로 요약(要約)할 수 있다. 1. 박(朴)이 개발(閒發)한 수학적(數學的) 모형(模型)들은 2 가지 방법(方法) 모두 사료공장(飼料工場)의 최적화(最適化) 설계(設計)에 효과적으로 이용(利用)할 수가 있었다. 2. MODM방법(方法)에 의(依)하여 얻어진 최적(最適)시스템은 Single Objective Program Problem에서 구(求)한 결과(結果)보다 균형(均衡)이 있는 시스템이었으며 장래(將來)의 사료원료(飼料原料), 사료구매시장(飼料購買市場), 기타 다른 조건(條件)들의 변화)에 대(對)해서 보다 탄력(彈力)이 있는 시스템으로 나타났다. 3. 엄밀한 의미(意味)에서 절대적(絶對的)인 최적사료공장(最適飼料工場)이란 있을 수 없으며, 주위의 조건(條件), 원료가격(原料價格), 사료가격(飼料價格), 공장주(工場主)의 취향 및 설계조건등(設計條件等)에 따라 최적(最適) 시스템은 각각(各各) 다르게 나타난다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.14 no.1
• /
• pp.231-243
• /
• 1994

A stochastic programming model for river water quality management was developed. River water quality, river flow, quality and flowrate of the wastewater treatment plant inflow were treated as random variables in the model. Withdrawal for water supply and submerged weir reaeration were included in the model itself. A probabilistic model was formulated to compute the expectation and variance of water quality using Streeter-Phelps equation. Chance constraints of the optimization problem were converted to deterministic equivalents by chance constrained method. Objective function was total annual treatment cost of all wastewater treatment plants in the region. Construction cost function and O & M cost function were derived in the form of nonlinear equations that are functions of treatment efficiency and capacity of treatment plant. The optimization problem was solved by nonlinear programming. This model was applied to the lower Han River. The results show that the reliability to meet the DO standards of the year 1996 is about 50% when the treatment level of four wastewater treatment plants in Seoul is secondary treatment, and BOD load from the tributary inflows is the same as present time. And when BOD load from Tanchon, Jungrangchon, and Anyangchon is decreased to 50%, the reliability to meet the DO standards of the year 1996 is above 60%. This results indicated that for the sake of the water quality conservation of the lower Han River, water quality of the tributaries must be improved, and at least secondary level of treatment is required in the wastewater treatment plants.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.14 no.6
• /
• pp.1391-1407
• /
• 1990

In this paper, optimization procedures are presented considering the static and dynamic constraints for laminated composite plate and hybrid laminated composite plate subject to concentrated load on center of the plates. Design variables for this problem are ply angle or ply thickness. Deflection, natural frequency and specific damping capacity are considered as constraints. Using a recursive linear programming method, the nonlinear optimization problems are solved. By introducing the design scaling factor, the number of iterations is reduced significantly. Composite plates could be designed optimally combined with FEM analysis under various conditions. In the optimization procedure, verification for both analysis and design of the laminated composite plates are compared with the results of the others. Various design results are presented for the laminated composite plates and hybrid laminated composite plates.

• Management Science and Financial Engineering
• /
• v.17 no.2
• /
• pp.63-84
• /
• 2011

The ordinary multiple criteria decision making (MCDM) approach requires two types of input, alternative values and criterion weights, and employs two schemes of alternative prioritization, dominance and potential optimality. This paper allows for incomplete information on both types of input and gives rise to the dominance relationships and potential optimality of alternatives. Unlike the earlier studies, we emphasize that incomplete information frequently takes the form of strict inequalities, such as strict orders and strict bounds, rather than weak inequalities. Then the issues of rising importance include: (1) The standard mathematical programming approach to prioritize alternatives cannot be used directly, because the feasible region for the permissible decision parameters becomes an open set. (2) We show that the earlier methods replacing the strict inequalities with weak ones, by employing a small positive number or zeroes, which closes the feasible set, may cause a serious problem and yield unacceptable prioritization results. Therefore, we address these important issues and develop a useful and simple method, without selecting any small value for the strict preference information. Given strict information on both types of decision parameters, we first construct a nonlinear program, transform it into a linear programming equivalent, and finally solve it via a two-stage method. An application is also demonstrated herein.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.540-549
• /
• 2018

This paper proposes the method of active distribution network expansion planning considering distributed generation integration and distribution network reconfiguration. The distribution network reconfiguration is taken as the expansion planning alternative with zero investment cost of the branches. During the process of the reconfiguration in expansion planning, all the branches are taken as the alternative branches. The objective is to minimize the total costs of the distribution network in the planning period. The expansion alternatives such as active management, new lines, new substations, substation expansion and Distributed Generation (DG) installation are considered. Distribution network reconfiguration is a complex mixed-integer nonlinear programming problem, with integration of DGs and active managements, the active distribution network expansion planning considering distribution network reconfiguration becomes much more complex. This paper converts the dual-level expansion model to Second-Order Cone Programming (SOCP) model, which can be solved with commercial solver GUROBI. The proposed model and method are tested on the modified IEEE 33-bus system and Portugal 54-bus system.