• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.6 s.336
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• pp.49-58
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• 2005

In this paper, we propose an optimal ILP scheduling algorithm for multiprocessor design on LDFG(Linear Data-Flow Graph) that can be represented by homogeneous synchronous data-flow. The proposed computation in this paper does not contain data-dependent, all scheduling decisions for such algorithms can be taken at compile time, only fully static overlapped schedules are considered. It means that all linear have the same schedule and the same processor assignment. In this paper, the resource-constrained problem is addressed, for the LDFG optimization for multiprocessor design problem formulating ILP solution available to provide optimal solution. The results show that the scheduling method is able to find good quality schedules in reasonable time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.134-141
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• 2022

Transportation in urban area has been getting hard to fulfill the demand on time. There are various uncertainties and obstacles related with road conditions, traffic congestions, and accidents to interrupt the on-time deliveries. With this situation, the last mile logistics has been a keen issue for researchers and practitioners to find the best strategy of the problem. A way to resolve the problem is to use parcel lockers. Parcel locker is a storage that customers can pick up their products. Transportation vehicles deliver the products to parcel lockers instead of all customer sites. Using the parcel lockers, the total delivery costs can be reduced. However, the inconvenience of customer has to increase. Thus, we have to optimal solution to balance between the total delivery costs and customers' inconvenience. This paper formulates a mathematical model to find the optimal solution for the vehicle routing problem and the location problem of parcel lockers. Experimental results provide the viability to find optimal strategy for the routing problem as well as the location problem.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.5
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• pp.649-656
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• 2024

This report describes the test results of decontamination of Bacillus atrophaeus spores. The spore solution at 10⁹ cfu/mL concentration was treated with chemical decontaminants, hydrogen peroxide, DF-200, sodium dichloroisocyanurate(NaDCC), and perasasfe. DF-200 was not suitable for decontamination of the spore solution. Among them, the optimal decontamination reaction conditions of perasafe and NaDCC were established from test tube experiments in a mL-scale, and they were validated in a bio-reaction system in a L-scale. The optimal conditions of perasafe and NaDCC were 1.0 % of concentration with 5 minutes of reaction time and 0.3 % of concentration with 10 minutes of reaction time, respectively. Also, both decontaminants required adequate agitation for decontamination. The results in the bio-reaction system were similar with the results from the smaller scale using test tubes, showing that the optimal conditions from the smaller scale experiments can be applied to the larger scale.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.111-117
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• 2021

The delivery problem on a graph is that of minimizing the object delivery time from one vertex to another vertex on a graph with m vertices using n various speed robot agents. In this paper, we propose two optimal solution algorithms for the delivery problem on a graph with time complexity of O(㎥n) and O(㎥). After preprocessing to obtain the shortest path for all pairs of the graph, our algorithm processed by obtaining the shortest delivery path in the order of the vertices with the least delivery time. Assuming that the graph reflects the terrain on which to solve the problem, our O(㎥) algorithm actually has a time complexity of O(㎡n) as only one preprocessing is required for the various deployment of n robot agents.

• IE interfaces
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• v.22 no.4
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• pp.359-367
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• 2009

The vehicle travel time between the demand points in downtown area is greatly influenced by complex road condition and traffic situation that change real time to various external environments. Most of research in the vehicle routing problems compose vehicle routes only considering travel distance and average vehicle speed between the demand points, however did not consider dynamic external environments such as traffic situation by service time zones. A realistic vehicle routing problem which considers traffic situation of smooth, delaying, and stagnating by three service time zones such as going to work, afternoon, and going home was suggested in this study. A mathematical programming model was suggested and it gives an optimal solution when using ILOG CPLEX. A hybrid genetic algorithm was also suggested to chooses a vehicle route considering traffic situation to minimize the total travel time. By comparing the result considering the traffic situation, the suggested algorithm gives better solution than existing algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.201-209
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• 2014

This paper proposes a genetic algorithm to maximize the connectivity among the mobile nodes for the cooperative communication in ad-hoc networks. In general, as the movement of the mobile nodes in the networks increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time for a high-density network, we propose a genetic algorithm to obtain the optimal solution for maximizing the connectivity. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the maximum number of connections and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• Korean Management Science Review
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• v.25 no.1
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• pp.107-121
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• 2008

As to more efficiently manage the inventory in the retail supply chain and to meet the customer demand in a timely manner, vendor-managed inventory (VMI) has been widely accepted, which manages inventory in the retail supply chain via sharing information and collaborating with the retailers. Applying VMI generates vendor-managed inventory/distribution problem (VMIDP), which involves inventory management for both the vendor and the retailers, and the design of vehicle routes for delivery, to minimize the total operating cost in the supply chain. In this paper, we suggest a mixed integer programming (MIP) model to obtain the optimal solution for VMIDP in a two-echelon retail supply chain, and develop an efficient heuristic based on the operating principles of the MIP model. To evaluate the performance of the heuristic, its solution was compared with the one of the MIP model on a total of twenty seven test problems. As a result, the heuristic found optimal solutions on seven problems in a significantly reduced time, and generated a 4.3% error rate of total cost in average for all problems. The heuristic is applied to the case problem of the local famous franchise company together with GIS, showing that it is capable of providing a solution efficiently in a relatively short time even in the real world situation.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.807-810
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• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.61-68
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• 2015

This paper deals with the pre-marshalling problem that the containers of container yard at container terminal are relocated in consensus sequence of loading schedule of container vessel. This problem is essential to improvement of competitive power of terminal. This problem has to relocate the all of containers in a bay with minimum number of movement. There are various algorithms such as metaheuristic as genetic algorithm and heuristic algorithm in order to find the solution of this problem. Nevertheless, there is no unique general algorithm that is suitable for various many data. And the main drawback of metaheuristic methods are not the solution finding rule but can be find the approximated solution with many random trials and by coincidence. This paper can be obtain the solution with O(m) time complexity that this problem deals with bin packing problem for m stack bins with descending order of take out ranking. For various experimental data, the proposed algorithm can be obtain the optimal solutions for all of data. And to conclude, this algorithm can be show that most simple and general algorithm with simple optimal solution finding rule.