• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.453-461
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• 2010

A critical issue in wireless sensor networks is an energy-efficiency since the sensor batteries have limited energy power and, in most cases, are not rechargeable. The most practical manner relate to this issue is to use a node wake-up scheduling protocol that some sensor nodes stay active to provide sensing service, while the others are inactive for conserving their energy. Especially, CTC (Connected Target Coverage) problem has been considered as a representative energy-efficiency problem considering connectivity as well as target coverage. In this paper, we propose a new energy consumption model considering multiple-targets and create a new problem, CMTC (Connected Multiple-Target Coverage) problem, of which objective is to maximize the network lifetime based on the energy consumption model. Also, we present SPT (Shortest Path based on Targets)-Greedy algorithm to solve the problem. Our simulation results show that SPT-Greedy algorithm performs much better than previous algorithm in terms of the network lifetime.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.45-57
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• 2010

This paper considers a 2-stage assembly flowshop scheduling problem where each job is completed by assembling multiple components. The problem has the objective measure of minimizing total completion time. The problem is shown to be NP-complete in the strong sense. Thus, we derive some solution properties and propose three heuristic algorithms. Also, a mixed-integer programming model is developed and used to generate a lower bound for evaluating the performance of proposed heuristics. Numerical experiments demonstrate that the proposed heuristics are superior over those of previous research.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.369-374
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• 2003

Disassembly scheduling is the problem or determining the ordering and disassembly schedules or used or end-of-life products while satisfying the demand or their parts or components over a certain planning horizon. To represent and optimally solve the basic case. i e ., single product type without parts commonality, an Integer programming model is suggested for the objective or minimizing the sum or purchase, setup, inventory holding, and disassembly operation costs. Then, the basic model is offended to consider the cases or single and multiple product types with parts commonality. Computational experiments done on the examples obtained from the literature show that the integer programming approach suggested in this paper works well.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1133-1138
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• 2006

This paper considers a hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. The hybrid flow shop consists of two stages in series, each of which has multiple identical parallel machines, and the problem is to determine the allocation and sequence of jobs at each stage. A branch and bound algorithm that gives the optimal solutions is suggested that incorporates the methods to obtain the lower and upper bounds. Dominance properties are also derived to reduce the search space. To show the performance of the algorithm, computational experiments are done on randomly generated problems, and the results are reported.

• Korean Management Science Review
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• v.31 no.3
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• pp.13-26
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• 2014

This paper considers an integrated one-vendor multi-buyer production-inventory model where the vendor manufactures multiple products in lot at their associated finite production rates. In the model, it is allowed for each product to be shipped in lot to the buyers even before the whole product production is not completed yet. Each product lot is dispatched to the associated buyer in a number of shipments. The buyers consume their products at fixed rates. The objective is to the production and shipment schedules in the integrated system, which minimizes the total cost per unit time. The total cost consists of production setup cost, inventory holding cost and shipment cost. For the model, an iterative optimal solution procedure with shipment consolidation policy incorporated. It is then tested through numerical experiments to show how efficient and effective the shipment consolidation policy is.

• IE interfaces
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• v.11 no.2
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• pp.13-24
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• 1998

A distribution system is composed of multiple levels from a producer to customers, and it's objective is to supply customers with goods timely with a prescribed level of quality at a minimum cost. For the installation and operation of multi-echelon distribution system, DRP(Distribution Resource Planning) is widely used. However, because of the characteristic difference of material flow dynamic of each distribution center, it is almost impossible to get the optimal distribution scheduling. In this paper, an improved DRP method to schedule multi-echelon distribution network is proposed so that the lot-size and order point is dynamically obtained to meet the change of demand rate and timing. The experiment is done with various demand pattern, forecast errors of demand and lead times of central distribution center. The proposed method is compared with traditional statistical approach.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.85-92
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• 2018

We consider a project scheduling problem in which the jobs can be compressed by using additional resource to meet the corresponding due dates, referred to as a time-cost tradeoff problem. The project consists of two independent subprojects of which precedence graph is a chain. The due dates of jobs constituting the project can be interpreted as the multiple assessments in the life of project. The penalty cost occurs from the tardiness of the job, while it may be avoided through the compression of some jobs which requires an additional cost. The objective is to find the amount of compression that minimizes the total tardy penalty and compression costs. Firstly, we show that the problem can be decomposed into several subproblems whose number is bounded by the polynomial function in n, where n is the total number of jobs. Then, we prove that the problem can be solved in polynomial time by developing the efficient approach to obtain an optimal schedule for each subproblem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.39-49
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• 2018

In the satellite operation phase, a ground station should continuously monitor the status of the satellite and sends out a tasking order, and a satellite should transmit data acquired in the space to the Earth. Therefore, the communication between the satellites and the ground stations is essential. However, a satellite and a ground station located in a specific region on Earth can be connected for a limited time because the satellite is continuously orbiting the Earth, and the communication between satellites and ground stations is only possible on a one-to-one basis. That is, one satellite can not communicate with plural ground stations, and one ground station can communicate with plural satellites concurrently. For such reasons, the efficiency of the communication schedule directly affects the utilization of the satellites. Thus, in this research, considering aforementioned unique situations of spacial communication, the mixed integer programming (MIP) model for the optimal communication planning between multiple satellites and multiple ground stations (MS-MG) is proposed. Furthermore, some numerical experiments are performed to verify and validate the mathematical model. The practical example for them is constructed based on the information of existing satellites and ground stations. The communicable time slots between them were obtained by STK (System Tool Kit), which is a well known professional software for space flight simulation. In the MIP model for the MS-MG problems, the objective function is also considered the minimization of communication cost, and ILOG CPLEX software searches the optimal schedule. Furthermore, it is confirmed that this study can be applied to the location selection of the ground stations.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.70-79
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• 2010

Makespan and cost minimization are two important factors in project investment. This paper considers a multi-mode resource-constrained project scheduling problem with the objective of minimizing costs, subject to a deadline constraint. A number of studies have focused on minimizing makespan or resource availability cost with a specified deadline. This problem assumes a fixed cost for the availability of each renewable resource per period, and the project cost to be minimized is the sum of the variable cost associated with the execution mode of each activity. The presented memetic algorithm (MA) consists of three features: (1) a truncated branch and bound heuristic that serves as effective preprocessing in forming the initial population; (2) a strategy that maintains two populations, which respectively store deadline-feasible and infeasible solutions, enabling the MA to explore quality solutions in a broader resource-feasible space; (3) a repair-and-improvement local search scheme that refines each offspring and updates the two populations. The MA is tested via ProGen generated instances with problem sizes of 18, 20, and 30. The experimental results indicate that the MA performs exceptionally well in both effectiveness and efficiency using the optimal solutions or the current best solutions for the comparison standard.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.421-424
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• 2004

This paper focuses on the problem of determining the quantity and timing of disassembling used products while satisfying the demand of their parts or components over a planning horizon. The case of multiple product types with parts commonality is considered for the objective of minimizing the sum of setup, disassembly operation, and inventory holding costs. A heuristic is suggested, in which an initial solution is obtained using a linear programming relaxation method, and then improved by perturbing the given solution using a dynamic programming approach and a look-ahead check while considering the trade-offs among different cost factors.