• Journal of Electrical Engineering and Technology
• /
• v.9 no.2
• /
• pp.415-422
• /
• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Korean Journal of Construction Engineering and Management
• /
• v.8 no.1 s.35
• /
• pp.87-95
• /
• 2007

If construction process operate composing work team by judgment manager's experience, possibility that progress of construction process becomes as inefficient is much. But, If produce optimal proposal of resources allocation, construction cost and duration through simulation at work plan step, work schedule because do quantification efficient operate do on. When plan construction process in this research, resources allocation by change of work team operation change, resources cost loss, total cost, optimal proposal of construction duration tentative plan of numerical formula model that can do simulation propose. Apply and revealed to apartment house framework which horizontal work area of process and vertical work area are composed as is each different construction process to verify proposed numerical formula model. Achieved efficiency than work team's operation results which apply numerical formula model that is presented in this research and enforce simulation is applied in actuality example construction.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.8
• /
• pp.344-351
• /
• 2016

In the point of view of a train operator, the main concern with a train operation is not only to maintain a time schedule, but also to decrease the energy consumption as much as possible. Generally for a manual drive, a train conductor controls the train acceleration and deceleration by controlling the notches not to exceed the regulation velocity by considering the given maximum velocity profile for an operation route. For this case, the guideline for a conductor is needed to choose the proper notches by applying the notch optimization so as to drive at the regulation velocity and minimize energy consumption simultaneously. In this paper, the real-time notch optimization plan is suggested using a genetic algorithm that optimizes the notches for the remaining route in real time when the event occurs that track information or regulation velocity profile of the remaining route changes during train operation as well as a normal operation situation. An energy saving effect and the convergence behavior of the optimal solution obtained was analyzed in a genetic algorithm.

• Journal of Korea Port Economic Association
• /
• v.33 no.2
• /
• pp.83-96
• /
• 2017

As a way to address global warming, among the renewable energy sources, there have been heavy investments worldwide for the development of offshore wind farms. However, such development has a drawback: investment costs are higher than those for onshore wind farms due to required operations such as offshore transportation and installation. In particular, delays in installation due to adverse maritime weather conditions are factors that affect the economics of offshore wind farms' operation. Therefore, in this study, we analyze the optimal schedule of the construction of an offshore wind farm from a macro perspective by considering the weather conditions in Korea. For this purpose, we develop a mathematical model and apply it to a 2.5 GW offshore wind farm project on the southwestern coast of the country. We use data from the Korea Meteorological Agency for maritime weather conditions and attempt to reflect the actual input data based on precedent cases overseas. The results show that it takes 6 months to install 35 offshore wind turbines. More specifically, it is pointed out that it is possible to minimize costs by not working in winter.

• Journal of Korean Society of Transportation
• /
• v.31 no.2
• /
• pp.80-89
• /
• 2013

The purpose of this research is to optimize the line planning of the intercity passenger railway. In this study, the line planning problem has been formulated into a mixed integer programming by minimizing both user costs (passenger's total travel time) and operator costs (operation, maintenance and vehicle costs) with multiple train types. As a solution algorithm, the branch-and-bound method is used to solve this problem. The change of travel demand, train speed and the number of schedules have been tested through sensitivity analysis. The optimal stop-schedules and frequency as well as system split with respect to each train type have been found in the case study of Kyoung-bu railway line in Korea. The model and results of this research are useful to make a decision for railway operation strategy, to analyze the efficiency of new railway systems and to evaluate the social costs of users and operators.

• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.4
• /
• pp.581-589
• /
• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.6
• /
• pp.1639-1651
• /
• 1998

In this paper, we present analytical and simulation models for evaluating the operation of a uniprocessor computer which utilizes a time redundant approach (such as recomputation by shilted operands) for lault-tolerant computing. In the proposed approach, all incoming jobs to the uniprocessor are duplicated, thus two versions 01 each job must be processed. Three methods for appropriately scheduling the primary and sL'Condary versions of the jobs are proposed and analyzed. The proposed scheduling methods take into account the load and the fault rate of the uniprocessor to evaluate two figures of merit for cost and profit with respect to a delay in response time due to faults and fault tolerance. Our model utilizes a fault-tolerant schedule according to which it is possible to find an optimal delay (given by $\kappa$) based on empiric parameters such as cost, the load and the fault rate of the uniprocessor.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.3
• /
• pp.384-392
• /
• 2015

This paper presents an economic assessment of large-scale Li-ion battery energy storage systems applied to Korean power system. There are many applications of the battery energy storage systems (BESSs) and they can provide various benefits to power systems. We consider BESSs to the energy time-shift application to Korean power system and evaluate the benefits from the application of BESS in the social perspective. The mixed integer programming (MIP) algorithm is used to resolve the optimal operation schedule of the BESS. The social benefits can include the savings of the fuel cost from generating units, deferral effects of the generation capacity, delay of transmission and distribution infra construction, and incremental CO2 emission cost impacts, etc. The economic evaluation of the BESS is separately applied into Korean power systems of the Main-land and Jeju island to reflect the differences of the load and generation patterns.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.6
• /
• pp.115-121
• /
• 2015

In this paper, we proposed an iterative data-flow optimal scheduling algorithm based on genetic algorithm for high-performance multiprocessor. The basic hardware model can be extended to include detailed features of the multiprocessor architecture. This is illustrated by implementing a hardware model that requires routing the data transfers over a communication network with a limited capacity. The scheduling method consists of three layers. In the top layer a genetic algorithm takes care of the optimization. It generates different permutations of operations, that are passed on to the middle layer. The global scheduling makes the main scheduling decisions based on a permutation of operations. Details of the hardware model are not considered in this layer. This is done in the bottom layer by the black-box scheduling. It completes the scheduling of an operation and ensures that the detailed hardware model is obeyed. Both scheduling method can insert cycles in the schedule to ensure that a valid schedule is always found quickly. In order to test the performance of the scheduling method, the results of benchmark of the five filters show that the scheduling method is able to find good quality schedules in reasonable time.

• Journal of Intelligence and Information Systems
• /
• v.6 no.2
• /
• pp.1-14
• /
• 2000

The berth and crane scheduling problem in a container terminal encompasses the whole process of assigning berth to each ship, determining the duration of berthing, assigning container cranes to each ship, and determining the specific start and end time of each crane service, for all the ships scheduled to be arriving at the terminal during a certain scheduling horizon. This problem is basically a constraint satisfaction problem in which cranes and berths should be assigned in such a way that all the spatial and temporal constraints are satisfied without any interference. However, it is also an optimization problem because the requested arrival and departure time should be met for as many of the scheduled ships as possible, while the operation cost of the terminal should be minimized. In this paper, we present an effective and efficient approach to solving this type of problem, which combines constrain satisfaction search and heuristic repair. We first employ a constraint satisfaction search to find a feasib1e solution. Then, the feasible solution is modified to a more optimal one by iteratively applying our heuristic repair operations within the framework of constraint satisfaction search. Experimental results with a real data from Pusan East Container Terminal showed that our approach can derive a schedule of satisfactory quality in a very short time.