• Smart Structures and Systems
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• v.15 no.3
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• pp.609-625
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• 2015

Management of infrastructure stock is essential in sustainability of society, and its analysis and optimization are studied in the light of control system modeling in this paper. At the first part of the paper, cost of stock management is analyzed based on macroscopic statistics on infrastructure stock and economical growth. Stock management burden relative to economy is observed to become larger at low economic growth periods in developed economies. Then, control system modeling of stock management is introduced and by augmenting maintenance actions as control input, dynamic behavior of stock is simulated and compared with existing time history statistics. Assuming steady state conditions, applicability of the model to cross sectional data is also demonstrated. The proposed model is enhanced so that both preventive and corrective maintenance can be included as system inputs, i.e., feedforward and feedback control inputs. Optimal management strategy to achieve specified deteriorated stock level with minimal cost, expressed in terms of preventive and corrective maintenance actions, is derived based on estimated parameter values for corrosion of steel bridges. Relative cost effectiveness of preventive maintenance is shown when target deteriorated stock level is lower.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.61-70
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• 2021

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.976-995
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• 2016

Workflow scheduling is one of the challenging problems in cloud computing, especially when service reliability is considered. To improve cloud service reliability, fault tolerance techniques such as fault recovery can be employed. Practically, fault recovery has impact on the performance of workflow scheduling. Such impact deserves detailed research. Only few research works on workflow scheduling consider fault recovery and its impact. In this paper, we investigate the problem of workflow scheduling in clouds, considering the probability that cloud resources may fail during execution. We formulate this problem as a multi-objective optimization model. The first optimization objective is to minimize the overall completion time and the second one is to minimize the overall execution cost. Based on the proposed optimization model, we develop a heuristic-based algorithm called Min-min based time and cost tradeoff (MTCT). We perform extensive simulations with four different real world scientific workflows to verify the validity of the proposed model and evaluate the performance of our algorithm. The results show that, as expected, fault recovery has significant impact on the two performance criteria, and the proposed MTCT algorithm is useful for real life workflow scheduling when both of the two optimization objectives are considered.

• Journal of Energy Engineering
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• v.5 no.1
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• pp.87-92
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• 1996

This paper formulates dynamic optimization model for Time-Of-Use Rates when a electric power system consists of three generators and a rating period is divided into three sub-periods. We use Pontryagin's Maximum Principle to derive optimal price and investment policy. Particularly the cross-price elasticities of demand are considered in the objective function. We get the following results. First, the price is equal to short-run marginal cost when the capacity is sufficient. However, if the capacity constraint is active, the capacity cost is included in the price. Therefore it is equal to the long-run marginal cost. Second, The length of rating period affects allocation of capacity cost for each price. Third, the capacity investment in dynamic optimization is proportional to the demand growth rate of electricity. However the scale of investment is affected by not only its own demand growth rate but also that of other rating period.

• Advances in Energy Research
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• v.4 no.1
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• pp.47-68
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• 2016

A well designed hybrid power system (HPS) can deliver electrical energy in a cost effective way. In this paper, model for HPS consisting of photo voltaic (PV) module and wind mill as renewable energy sources (RES) and solar lead acid battery as storage device connected to unidirectional grid is developed for peak demand reduction. Life time energy cost of the system is evaluated. One year hourly site condition and load pattern are taken into account for analysing the HPS. The optimal HPS is determined for least life time energy cost subject to the constraints like state of charge of the battery bank, dump load, renewable energy (RE) generation etc. Optimal solutions are also found out individually for PV module and wind mill. These three systems are compared to find out the most feasible combination. The results show that the HPS can deliver energy in an acceptable cost with reduced peak consumption from the grid. The proposed optimization algorithm is suitable for determining optimal HPS for desired location and load with least energy cost.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1491-1497
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• 2007

This paper considers robust and non-fragile guaranteed cost controller design method for descriptor systems with parameter uncertainties and time delay, and static state feedback controller with gain variations. The existence condition of controller, the design method of controller, the upper bound to minimize guaranteed cost function, and the measure of non-fragility in controller are proposed using linear matrix inequality (LMI) technique, which can be solved efficiently by convex optimization. Therefore, the presented robust and non-fragile guaranteed cost controller guarantees the asymptotic stability and non-fragility of the closed loop systems in spite of parameter uncertainties, time delay, and controller fragility.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.387-392
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• 2005

Double-deck train have studied in the next generation train in KRRI. Double-deck train have more seat capacities compared with single deck vehicles and is a efficient, reliable and comfortable alternative train. Because of heavy weight, weight minimization of double-deck train carbody is imperative to reduce cost and extend life-time of train. Weight minimization problem of the double-deck train car-body is required to decide 66 design variables of thicknesses for large aluminum extruded panel while satisfying stress constraints. Design variables are too many and one execution of structural analysis of double-deck train carbody is time-consuming. Therefore, we adopt approximation technique to save computational cost of optimization process. Metamodels such as response surface model (RSM) and kriging model are used to approximate model-based optimization is described. RSM is easy to obtain and expressed explicit function, but this is not suitable for highly nonlinear and large scaled problems. Kriging model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. Target of this design is to find optimum thickness of AEP to minimize weight of doulbe-deck train carbody. In this study, meta model techniques are introduced to carry out weight minimization of a double-deck train car-body.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.1
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• pp.51-63
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• 1989

This paper considers software release problems based on Goel-Okumoto and S-shaped reliability growth models. Test of the software system is terminated after a preassigned time T, and it is released to the operational phase. It is assumed that correction cost of an error is increasing with test or operation time. Optimum software release time is obtained using total expected cost on the software life time as a criterion for optimization. In addition, optimal software release policies under the constraint of a software reliability requirement are discussed.

• Korean Management Science Review
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• v.31 no.2
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• pp.1-13
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• 2014

In almost all of the organizations, the cost for acquiring and maintaining the inventory takes a considerable portion of the management budget, and thus a certain constraint is set upon the budget itself. The previous studies on inventory control for each item that aimed to improve the fill rate, backorder, and the expenditure on inventory are fitting for the commercially-operated SCM, but show some discrepancies when they are applied to the spare parts for repairing disabled systems. Therefore, many studies on systematic approach concept considering spare parts of various kinds simultaneously have been conducted to achieve effective performance for the inventory control at a lower cost, and primarily, METRIC series models can be named. However, the past studies were limited when dealing with the probability distributions for representing the situation on demand and transportation of the parts, with the (S-1, S) inventory control policy, and so on. To address these shortcomings, the Continuous Time Markov Chain (CTMC) model, which considers the phase-type distributions and the (s, Q) inventory control policies to best describe the real-world situations inclusively, is presented in this study. Additionally, by considering the cost versus the system availability, the optimization of the inventory level, based on this model, is also covered.