• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.53-62
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• 2015

Time-Cost Trade-Off (TCTO) model is an important model in the construction project planning and control area. Two types of Existing TCTO model, continuous and discrete TCTO model, have been developed by researchers. However, Using only one type of model has a limitation to represent a realistic crash scenario of activities in the project. Thus, this paper presents a comprehensive TCTO model that combines a continuous and discrete model. Additional advanced features for non-linear relationship, incentive, and liquidated damage are included in the TCTO model. These features make the proposed model more applicable to the construction project. One CPM network with 6 activities is used to explain the proposed model. The model found an optimal schedule for the example to satisfy all the constraints. The results show that new model can represent more flexible crash scenario in TCTO model.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.433-440
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• 2014

In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and $CO_2$ emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.105-115
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• 2007

It is essential to give a fatal damage to the enemy force by using prompt and accurate fire in order to overcome the lack of artillery force. During the artillery fire operations, minimizing the firing time will secure the adapt ability in tactical operation. In this paper, we developed a mathematical model to schedule the artillery fire on the multiple targets to decrease total fire operation time. To design a program to describe a real firing situation, we consider many possible circumstances of changes such as commander's intention, firing constraints, target priority, and contingency plan to make a fire plan in an artillery unit. In order to work out the target sequencing problem, MIP is developed and the optimum solution is obtained by using ILOG OPL. If this analytical model is applied to a field artillery unit, it will improve the efficiency of the artillery fire force operations.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.124-132
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• 2019

Scheduling problems can be classified into offline and online ones. This paper considers an online scheduling problem to minimize makespan on the identical parallel machines. For dynamically arrived jobs with their ready times, we show that the sequencing order according to the ERD (Earliest Ready Date) rule is optimal to minimize makespan. This paper suggests an algorithm by using the MIP(Mixed Integer Programming) formulation periodically to find a good periodic schedule and evaluates the required computational time and resulted makespan of the algorithm. The comparition with an offline scheduling shows our algorithm makes the schedule very fast and the makespan can be reduced as the period time reduction, so we can conclude that our algorithm is useful for scheduling the jobs under online environment even though the number of jobs and machines is large. We expect that the algorithm is invaluable one to find good schedules for the smart factory and online scheduler using the blockchain mechanism.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.51-64
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• 2018

Recently, the magnitude and frequency of the natural disaster have been increased, the damage has become severer. The importance of disaster response system to relieve the damage has arised continuously. This study has tried to develop the algorithm to solve the facility location and size problem in emergency logistics. A facility in the emergency logistics has various roles in victim care, casualty treatment, relief resource management and relief vehicle assistance. Moreover, the location of facility in emergency logistics has to consider the safety and reliability. To gather these information, information management system with IoT sensors are suggested. The location problem in this study also covers various features to response various demands in disaster. To solve this problem, this study suggested MIP based algorithm. Scenario based simulation experiments are conducted to verify the performance suggested algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.9-21
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• 2018

In South Korea, Jeju Island has a role as a test bed for electric vehicles (EVs). All conventional cars on the island are supposed to be replaced with EVs by 2030. Accordingly, how to effectively set up EV charging stations (EVCSs) that can charge EVs is an urgent research issue. In this paper, we present a case study on planning the locations of EVCS for Jeju Island, South Korea. The objective is to determine where EVCSs to be installed so as to balance the load of EVCSs while satisfying demands. For a public service with EVCSs by some government or non-profit organization, load balancing between EVCS locations may be one of major measures to evaluate or publicize the associated service network. Nevertheless, this measure has not been receiving much attention in the related literature. Thus, we consider the measure as a constraint and an objective in a mixed integer programming model. The model also considers the maximum allowed distance that drivers would detour to recharge their EV instead of using the shortest path to their destination. To solve the problem effectively, we develop a heuristic algorithm. With the proposed heuristic algorithm, a variety of numerical analysis is conducted to identify effects of the maximum allowed detour distance and the tightness of budget for installing EVCSs. From the analysis, we discuss the effects and draw practical implications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.67-75
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• 2020

This paper proposes a methodology for gantry route optimization in order to maximize the productivity of a odd-type surface mount device (SMD). A odd-type SMD is a machine that uses a gantry to mount electronic components on the placement point of a printed circuit board (PCB). The gantry needs a nozzle to move its electronic components. There is a suitability between the nozzle and the electronic component, and the mounting speed varies depending on the suitability. When it is difficult for the nozzle to adsorb electronic components, nozzle exchange is performed, and nozzle exchange takes a certain amount of time. The gantry route optimization problem is divided into the mounting order on PCB and the allocation of nozzles and electronic components to the gantry. Nozzle and electronic component allocation minimized the time incurred by nozzle exchange and nozzle-to-electronic component compatibility by using an mixed integer programming method. Sequence of mounting points on PCB minimizes travel time by using the branch-and-price method. Experimental data was made by randomly picking the location of the mounting point on a PCB of 800mm in width and 800mm in length. The number of mounting points is divided into 25, 50, 75, and 100, and experiments are conducted according to the number of types of electronic components, number of nozzle types, and suitability between nozzles and electronic components, respectively. Because the experimental data are random, the calculation time is not constant, but it is confirmed that the gantry route is found within a reasonable time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.47-54
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• 2020

North Korea has recently developed and deployed missiles with various ranges as asymmetrical forces. Among them, short-range ballistic missiles with improved accuracy are expected to aim at achieving tactical goals by hitting important military facilities in Korea with a small number of missiles. Damage to the air force airfields, one of North Korea's main targets of missiles attack, could limit the operation of air force fighters essential to gaining air superiority. Based on the attack by the short range ballistic missiles, the damage probability of military airfields was simulated. And as the one of the concepts of passive defense, the way to reduce the loss of combat power was studied through the changes of the air force squadrons deployment. As a result, the effective deployment plan could be obtained to reduce the amount of power loss compared to the current deployment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.32-42
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• 2021

The printing process can have to print various colors with a limited capacity of printing facility such as ink containers that are needed cleaning to change color. In each container, cleaning time exists to assign corresponding inks, and it is considered as the setup cost required to reduce the increasing productivity. The existing manual method, which is based on the worker's experience or intuition, is difficult to respond to the diversification of color requirements, mathematical modeling and algorithms are suggested for efficient scheduling. In this study, we propose a new type of scheduling problem for the printing process. First, we suggest a mathematical model that optimizes the color assignment and scheduling. Although the suggested model guarantees global optimality, it needs a lot of computational time to solve. Thus, we decompose the original problem into sequencing orders and allocating ink problems. An approximate function is used to compute the job scheduling, and local search heuristic based on 2-opt algorithm is suggested for reducing computational time. In order to verify the effectiveness of our method, we compared the algorithms' performance. The results show that the suggested decomposition structure can find acceptable solutions within a reasonable time. Also, we present schematized results for field application.