• Korean Management Science Review
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• v.28 no.3
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• pp.15-29
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• 2011

In this paper, we consider a system working at both primary service and secondary service. A server can switch between the primary service and the secondary service or it can be assigned to secondary service as a dedicated server. A service policy is characterized by the number of servers dedicated to the secondary service and a rule for switching the remaining servers between two services. The primary service system is modelled as a Markovian queueing system and the throughput is a function of the number of servers, buffer capacity, and service policy. And the secondary service system has a service level requirement strategically determined to perform the service assigned. There is a revenue obtained from throughput and costs due to servers and buffers. We study the problem of simultaneously determining the optimal total number of servers, buffers, and service policy to maximize profit of the system subject to both an expected customer waiting time constraint of the primary service and a service level constraint of the secondary service and develop an algorithm which can be successfully applied with the small number of computations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.92-102
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• 2011

Visibility of the flow of goods is an important service element when considering how to improve the quality of a logistics service. Registered mail service of Korea Post, one of logistics service providers, currently supply information on the handling of postal items, such as receipts and delivery and arrival times to customers. However, internally problems related to excessive spending on operating expenses and less reliable tracking and tracing information exist due to errors in the information acquisition and link handling processes, leading to unnecessarily duplicated tasks. Therefore, the purpose of this paper is to propose process optimization method for improving the quality of tracking and tracing information for registered mail items. We analyzed the process and information control system of the current registered mail service and identified the basic causes of the existence of less reliable tracking and tracing information. And we present optimized mail handling and information management process for removing internal problems. Test results using tracking and tracing information examples show a significant performance of the proposed method.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.39-47
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• 2005

We adopt the P/sub λ, T//sup M/ policy of dam to introduce a service policy with alternating service rates for a Poisson arrival queue, in which the service rate alternates depending on the number of customers in the system. The stationary distribution of the number of customers in the system is derived and, after operating costs being assigned to the system, the optimization of the policy is studied.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.5
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• pp.311-321
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• 2020

In this study, a model to optimize residual chlorine concentrations in a water supply system was developed using a multi-objective genetic algorithm. Moreover, to quantify the effects of optimized residual chlorine concentration management and to consider customer service requirements, this study developed indices to quantify the spatial and temporal distributions of residual chlorine concentration. Based on the results, the most economical operational method to manage booster chlorination was derived, which would supply water that satisfies the service level required by consumers, as well as the cost-effectiveness and operation requirements relevant to the service providers. A simulation model was then created based on an actual water supply system (i.e., the Multi-regional Water Supply W in Korea). Simulated optimizations were successful, evidencing that it is possible to meet the residual chlorine concentration demanded by consumers at a low cost.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.17-31
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• 2007

In this paper, we consider a service system with bi-functional servers, which can switch between the primary service room and the secondary room. A service policy is characterized by the switching paints which depend on the queue length in the primary service room and the service level requirement constraint of the secondary room. The primary service room is modeled as a Markovian queueing system and the throughput of the primary service room is function of the total number of bi-functional servers. the buffer capacity of the primary service room, and the service policy. There is a revenue obtained from throughput and costs due to servers and buffers. We study the problem of simuitaneously determining the optimal number of servers, buffer capacity, and service policy to maximize profit of the service system, and develop an algorithm which can be successfully applied with the small number of computations.

• Korean Management Science Review
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• v.9 no.1
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• pp.3-15
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• 1992

In this paper, we study a manufacturing system of serial stages with general service times, in which the production of each stage and the coordination of stages are controlled by Kanban discipline. This Kanban discipline is modeled as a Discrete Event Dynamic System and a system of recursive equations is applied to study the dynamics of the system. The recursive relationship enables us to compare this Kanban discipline with the other blocking disciplines such as transfer blocking, service blocking, block-and-hold b, and block-and-hold K, and the Kanban is shown to be superior to the other disciplines in terms of makespan and throughput. As a special case, two stages Kanban system is modeled as $C_2/C_2/1/N$ queueing system, and a recursive algorithm is developed to calculate the system performance. In optimizing the system performance, the stochastic optimization approach of Robbins-Monro is employed via perturbation analysis, the way to estimate the stochastic partial derivative based on only one sample trajectory of the system, and the required commuting condition is verified. Then the stochastic convexity result is established to provide second-order optimality condition for this parametric optimization problem.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.337-345
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• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.1-6
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• 2020

Fast service access involves keeping track of the location of mobile users, while they are moving around the mobile network for a satisfactory level of QoS (Quality of Service) in a cost-effective manner. The location databases are used to keep track of Mobile Terminals (MT) so that incoming calls can be directed to requested mobile terminals at all times. MT reporting cell system used in location management is to designate each cell in the network as a reporting cell or a non-reporting cell. Determination of an optimal number of reporting cells (or reporting cell configuration) for a given network is reporting cell planning (RCP) problem. This is a difficult combinatorial optimization problem which has an exponential complexity. We can see that a cell in a network is either a reporting cell or a non-reporting cell. Hence, for a given network with N cells, the number of possible solutions is 2^N. We propose a biogeography based optimization (BBO) for design of mobile station location management system in wireless communication network. The number and locations of reporting cells should be determined to balance the registration for location update and paging operations for search the mobile stations to minimize the cost of system. Experimental results show that our proposed BBO is a fairly effective and competitive approach with respect to solution quality for optimally designing location management system because BBO is suitable for combinatorial optimization and multi-functional problems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.236-239
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• 2006

A wireless MAC scheduler that provides a high level of quality-of-service (QoS) for video-on-demand (VOD) applications while achieving a reasonable level of system throughput is proposed. The proposed scheduler considers both channel qualities of mobiles and the urgency of real-time packets coming from VOD applications in a cross-layer approach between application and MAC layers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.