• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.1-12
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• 2011

The error of onboard propellants mass which is mostly occupied in total mass of launch vehicle and The error of residual affect the performance of launch vehicle seriously. In other words, the errors directly cause the error of total impulse. Therefore, optimization of performance of launch vehicle can be achieved by the minimization of the residual. For minimizing the residuals, the active control for completely depleting the propellants and the calculation method using probability for minimizing the residuals have been researched. In this paper, the added fuel was calculated for minimizing the residual and the minimized residual was predicted by the presented method.

• Journal of the Korean Solar Energy Society
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• v.31 no.5
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• pp.119-125
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• 2011

In this paper, we have studied about minimizing the Energy Storage System (ESS) capacity for mitigating the fluctuation of Wind Turbine Generation System (WTGS) by using Electric Double Layer Capacitor (EDLC) and Battery Energy Storage System (BESS). In this case, they have some different characteristics: The EDLC has the ability of generating the output power at high frequency. Thus, it is able to reduce the fluctuation of WTGS in spite of high cost. The BESS, by using Li-Ion battery, takes the advantage of high energy density, however it is limited to use at low frequency response. To verify the effectiveness of the proposed method, simulations are carried out with the actual data of 2MW WTGS in case of worst fluctuation of WTGS is happened. By comparing simulation results, this method shows the excellent performance. Therefore, it is very useful for understanding and minimizing the ESS capacity for mitigating the fluctuation of WTGS.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.458-466
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• 2011

In war time, minimizing the logistics response time for supporting military operations is strongly needed. In this paper, i propose the mathematical formulation for minimizing the maximal acquisition completion time in wartime or during a state of emergency. The main structure of this formulation is based on the traveling purchaser problem (TPP), which is a generalized form of the well-known traveling salesman problem (TSP). In the case of the general TPP, an objective function is to minimize the sum of the traveling cost and the purchase cost. However, in this study, the objective function is to minimize the traveling cost only. That's why it's more important to minimize the traveling cost (time or distance) than the purchase cost in wartime or in a state of emergency. I generate a specific instance and find out the optimal solution of this instance by using ILOG OPL STUDIO (CPLEX version 11.1).

• IE interfaces
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• v.12 no.1
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• pp.10-18
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• 1999

Job shop manufacturing environments are using the concept of cellular manufacturing systems(CMS) which has several advantages in reducing production lead times, setup times, work-in-process, etc. Utilizing the similarities between cell-machine, part-machine, and the shape/size of parts, CMS can group machines and parts resulting in improved efficiency of this system. However, when grouping machines and parts in machine cells, there inevitably occurs exceptional elements(EEs), which can not operate in the same machine cell. Minimizing these EEs in CMS is a critical point that improving production efficiency. Constraints in machine duplication cost, machining process technology, machining capability, and factory space limitations are main problems that prevent achiving the goal of maintaining an ideal CMS environment. This paper presents an algorithm that minimizes EEs under the constraints of machine duplication cost and factory space limitation. Developing exceptional operation similarity(EOS) by cell-machine incidence matrix and part-machine incidence matrix, it brings the machine cells that operate the parts or not. A mathematical model to minimize machine duplication is developed by EOS, followed by a heuristic algorithm in order to reflect dynamic situation resulting from minimizing exceptional elements process and the mathematical model. A numerical example is provided to illustrate the algorithm.

• Journal of Science Education
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• v.44 no.2
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• pp.259-272
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• 2020

This study aims at investigating the effects of collective promotion on the attainment of literacy, numeracy, and essential life skills by primary school pupils and also to find out if the policy of collective promotion meets its objective of minimization of wastage in basic education. The study used written tests for pupils in the final class (Level II, class 6) to collect data in some selected English-speaking primary schools in Meme Division of Cameroon. Descriptive statistics and a one way ANOVA were used for analyzing data. The results revealed that the policy of collective promotion negatively affects the attainment of literacy, numeracy, and essential life skills of pupils in Kumba, Meme Division. Teachers who assisted in the study through personal communication with the researcher argued that collective promotion in basic education does not achieve its objective of minimizing wastage of educational resources; neither does it positively improve the literacy, numeracy, and essential life skills of pupils. This study recommends that the policy of collective promotion can be revisited and that focus be placed not only on minimizing wastage of resources but also on investing on quality education system so as to equip the would-be leaders of tomorrow with skills, knowledge, and attitudes which will make them functional and responsible citizens in their society.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2099-2106
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• 2006

In this paper, we addressed the node positioning method for minimizing the sensing energy consumption in wireless sensor networks with adjustable sensing ranges. It is necessary for minimizing the sensing energy consumption to minimize the overlapped sensing area by neighboring nodes. To find a optimal node position, we derived a optimal equations by using the overlapped areas, each node's radiuses and expended angles of opposite neighboring nodes. Based on it, we devised a new node positioning method, called as ASRC(Adjustable Sensing Ranges Control). Unlike existing condition based model, our proposed method was derived from mathematical formula, and we confirmed its validity of sensing energy consumption through simulations.

• Journal of Korean Port Research
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• v.1 no.1
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• pp.21-47
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• 1987

Nowaday all the countries of the world have studied the various problems caused in operating their own ports efficiently. Ship delay in the port is attributal to the inefficient operation in the navigation aids, the cargo handling, the storage and transfer facilities, and to the inefficient allocation of gangs or to a bad service for ships. Among these elements the allocation of gangs is the predominating factor in minimizing ship's turn round time. At present, in the case of Pusan Port. the labour union and stevedoring companies allocate gangs in every hatches of ships by a rule of thumb, just placing emphasis on minimizing ship's turn round time, without applying the principle of allocation during the cargo handling. Owing to this the efficiency of the cargo handling could not be expected to be maximized and this unsystematic operation result in supplying human resources of much unnecessary surplus gangs. Therefore in this paper the optimal size and allocation of gangs for minimizing the ship's turn round time is studied and formularized. For the determination of the priority for allocation the evaluation function, namely $F=PHi^{n}{\times}(W+H)$, can be obtained; where, PHI : Principal Hatch Index W : Total Cargo Weight represented in Gang-Shifts H : Total Number of Ship's hatches and also for the optimal size of gangs the average number of gang allocated per shift (Ng), namely Ng=W/PHI, is used. The proposed algorithm is applied to Pusan Port and its validity is verified.

• Journal of Drive and Control
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• v.21 no.1
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• pp.46-52
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• 2024

The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A^*, and RRT^*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.