• Journal of the Korea Safety Management & Science
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• v.2 no.3
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• pp.81-88
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• 2000

In this research, the system is assumed to carry a single item of which the demand types vary. Demand type is defined as a management's classification of the item according to the demand source or to the service purpose. The purpose of this research is to find the optimal inventory control policy when the system carries a single item which consists of multiple demand types. In this research, the optimizing algorithm contains a heuristic, therefore, the optimal is not guaranteed by the algorithm. At least, this research provides the solution to the problems that have not been solved by the existing algorithms.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.127-130
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• 2000

In this research, the system is assumed to carry a single item of which the demand types vary. Demand type is defined as a management's classification of the item according to the demand source or to the service purpose. The purpose of this research is to find the optimal inventory control policy when the system carries a single item which consists of multiple demand types. In this research, the optimizing algorithm contains a heuristic, therefore, the optimal Is not guaranteed by the algorithm. At least, this research provides the solution to the problems that have not been solved by the existing algorithms.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.97-109
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• 2014

This paper considers a reverse supply chain with simultaneous recovery of used products and manufacturing of brand-new ones. Recovered products are downgraded and have to be sold in a market different from that of brand-new products at a different price. In case of a shortage of recovered product inventory, a brand-new item, if available, can be offered at the price of a recovered product. In other words, one-way demand substitution is allowed. We address the joint decision of when to manufacture brand-new product, when to recover returned product, and how to control demand substitution to maximize the hybrid production system's profits. To this end, we propose a Markov decision Process model and investigate the structure of the optimal policy. Performance comparison is numerically implemented between the models with and without downward demand substitution option under different operating conditions of the system parameters.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.51-57
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• 2006

Association rule mining searches for interesting relationships among Items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule support and confidence and lift. In this paper we present association rule mining based antecedent variables. We call these rules to antecedent association rules. An antecedent variable is a variable that occurs before the independent variable and the dependent variable. For example, in politics, a special interest group may want to support a politician who backs their cause. The group would look for a candidate who supports their views and support his election. Once in office, the politician would then conduct policy that supports the interest group.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.167-179
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• 2016

The safety of SOC facilities is constantly under threat by the globally increasing abnormal climate. Responding to disasters requires prompt decision-making such as suggesting evacuation paths. For doing so, spatio-temporal information with convergence of disaster information and SOC facility information must be utilized. Such information is being collected separately by the government or related organizations, but not collectively. The collective control of the separately collected disaster information and the generation of SOC facility safety and damage information are required for prompt disaster response. Also, as disaster information requires spatio-temporal convergence in its nature, the construction of an inventory that integrates related information and assists disaster response decision-making is required. A plan to construct a facility importance, risk, and damage estimation inventory for assisting prompt disaster response decision-making is suggested in this study. Through this study, the disaster and SOC facility-related data, which are being managed separately, can be collected and standardized. The integrated information required for the estimation of facility importance, risk, and damage can be provided. The suggested system is expected to be used as a decision-making tool for proactive disaster response.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.151-157
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• 1993

In this paper, we investigate the effects of yield randomness for lot-sizing in a multi-stage production system. The practical importance of incorporating yield randomness into production models has been emphasized by many researchers. Yield randomness, especially in semiconductor manufacturing, poses a mojor challenge for production planning and control. The task becomes even more difficult if the demand for final product is uncertain. An attempt to meet the demand with a higher level of confidence forces one to release more input in the fabrication line. This leads to excessive work-in-process (WIP) inventories which cause jobs to spend unpredictably longer time waiting for the machines. The result is that it is more difficult to meet demand with exceptionally long cycle time and puts further pressure to increase the safety stocks. Due to this spiral effect, it is common to find that the capital tied in inventory is the msot significant factor undermining profitability. We propose a policy to determine the quantity to be processed at each stage of a multi-stage production system in which the yield at each stage may be random and may need rework.

• Journal of the Korea Safety Management & Science
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• v.3 no.3
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• pp.55-64
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• 2001

The main objective of this research is to develop a model to select the optimal input service level for a distribution center-multi branch inventory distribution system. With the continuous review policy, the distribution center places an order for specific order quantity to an outside supplier, and the order quantity is replenished after a certain lead time Also, each branch places an order for particular order quantity to the distribution center to satisfy the customer demands, and receives the replenishment after a lead time. When an out of stock condition occurs during an order cycle, a backorder is placed to the upper level to fill the unfilled demands. With these situation, variable demand and variable lead time are used for better industrial practice. Further, actual lead times with a generic lead time distribution are used in developing the control model. Under the actual lead time model, the customer service measures actually attained for the distribution center and each branch are explained as the effective customer service measures. Thus, throughout the optimal control (using computer search procedures), we can select the optimal input service levels for the distribution center and each branch to attain the effective service levels for each branch which is consistent with the goal level of service for each branch. At the same time, the entire distribution system keeps minimum inventories.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.517-533
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• 2018

We utilize the CAMx (Comprehensive Air Quality Model with eXtensions) system and the PSAT (Particulate Source Apportionment Technology) diagnostic tool to determine the $PM_{2.5}$ concentration and to perform its source apportionment in the southeastern region of South Korea. For a year-long simulation, eight local authorities in the region such as Pohang, Daegu, Gyeongju, Ulsan, Busan-Gimhae, Gosung-Changwon, Hadong, and all remaining areas in Gyeongsangnam-do, are selected as source areas based on the emission rates of $NO_x$, $SO_x$, VOC, and primary PM in CAPSS (Clean Air Policy Support System) 2013 emissions inventory. The CAMx-PSAT simulation shows that Pohang has the highest $PM_{2.5}$ self-contribution rate (25%), followed by Hadong (15%) and Busan-Gimhae (14%). With the exception of Pohang, which has intense fugitive dust emissions, other authorities are strongly affected by emissions from their neighboring areas. This may be measured as much as 1 to 2 times higher than that of the self-contribution rate. Based on these estimations, we conclude that the efficiency of emission reduction measures to mitigate $PM_{2.5}$ concentrations in the southeastern region of South Korea can be maximized when the efforts of local or regional emission controls are combined with those from neighboring regions. A comprehensive control policy planning based on the collaboration between neighboring jurisdictional boundaries is required.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.299-310
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• 2007

It will be necessary to make proper management plans to preserve the air quality in good level for the public. In order to make these plans, source information and detail emission inventories of the city and near industrial areas should be given. However, lack of the source measurements data makes us more difficult to complete the source inventory. VOC source Inventory could be utilized for the feasibility study to estimate the contribution of VOC sources presenting to the receptor such as residential area. It may give policy maker an idea how to control the air quality, and improve their social environment in the area. This study shows data that measured VOCs concentrations from the local industrial areas in Jeonju during from May 2005 to January 2006. The samples were collected from the near sources in 7 major factories in the industrial park as well as 5 general sources in near city Jeonju area to elucidate the abundances of speciated VOCs and their spacial and temporal distributions depending on source bases. Industrial sources are as follows; chemical, food, paper, wood, metal, non-metal (glass), and painting (coating) industries. The 5 general sources are sampled from tunnel, gasoline gas station, dry cleaning shop, printing (copy) shop, and road pavement working place in urban area. To understand the near source effect at receptor, samples from the 2 receptor sites (one is at center of the industrial complex and the other site is at distance residential area downwind from the center) were collected and analyzed for the comparison to source concentration. The mass contributions of the speciated VOC to total mass of VOCs measured from the different sources and ambient (2 receptors) were presented and discussed.