• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.13-34
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• 1991

A production/inventory system is considered in which a production facility produces one type of product. The demand for the product is given by a compound Poison process and is supplied directly from inventory when inventory is available and is lost when inventory is out of stock. The processing time to produce one item is assumes to follow a general distribution. An (s, S) policy is considered in which production stops at the instant the stock on hand reachs S and the setup of the production facility begins at an inspection point when the stock on hand drops to or below s for the first time. The time interval between two successive inspection points during a non-production period is a random variable which follows a general distribution.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.111-127
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• 2004

We consider a supply chain model with a make-to-order production facility and a single supplier. The model we treat here is a special case of a two-echelon inventory model. Unlike classical two-echelon systems, the demand process at the supplier is affected by production process at the production facility as well as customer order arrival process. In this paper, we address that how the demand variability impacts on the optimal replenishment policy. To this end, we incorporate Erlang and phase-type demand distributions into the model. Formulating the model as a Markov decision problem, we investigate the structure of the optimal replenishment policy. We also implement a sensitivity analysis on the optimal policy and establish its monotonicity with respect to system cost parameters.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.537-541
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• 2003

The synchronization of production-delivery artivities is one of crucial factors to get competitive collaboration benefits between the manufacturer and the retailor(s). There were several researches to study on He optimal delivery policy to minimize the total cost of integrated system of both manufacturer and retailor(s). In this research, we investigate the joint optimal shipment policy in case that a manufacturer produces multiple products sharing a single production facility in the manufacturer side and retailor(s) deploys JIT delivery pattern with equal-size shipment policy. We formulate this problem as a form of 'Common Cycle Approach' in classical ELSP (Economic Lot Scheduling Problem) and provide simple optimal solution procedure.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.137-158
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• 1992

We study the Multiple Product Single Facility Stockout Avoidance Problem (SAP). That is the problem of determining, given initial inventories, whether there is a multiple product single facility production schedule that avoids stockouts over a given time horizon. The optimization version of the SAP where stockouts are pnelized linearly is also studied. We call this problem the Weighted Stockout Problem (WSP). Both problems are NP-hard in the strong sense. We develop Mixed Integer Linear Programming (MIP) formulations for both the SAP and the WSP. In addition, several heuristic algorithms are presented and performances are tested using computational experiments. We show that there exist polynomial algorithms for some special cases of the SAP and the WSP. We also present a method to phase into a target cyclic schedule for infinite horizon problems. These can be used as a practical scheduling tool for temporarily overloaded facilities or to reschedule production after a disruption.

• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.151-170
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• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• Korean journal of food and cookery science
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• v.33 no.3
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• pp.342-352
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• 2017

Purpose: The purposes of the study were to evaluate dietitian's practices for sustainability management and identify barriers of sustainability management at school foodservice. Methods: A total of 220 dietitians working in Daejeon and Chungnam area were surveyed. 187 responses were analyzed for the study. Data were analyzed using SPSS Windows. Results: The respondents were 36% in their 20 s, 38% in their 30 s, 39.6% in elementary schools, 33.7% in middle schools and 26.4% in high schools. Among dietitian's practices for sustainability management, the item with the highest self-evaluation was purchase eco-friendly food (3.75). The category with the highest score was procurement (3.52), by waste management (3.48), production (3.39), menu management (3.36), facility and energy management (3.20), personnel management (3.18), and nutrition education (3.04). In the area of menu management, production management, facility and energy management, nutrition education, and personnel management, scores of elementary school working dietitians were the highest, followed by middle school and high school (p<0.001). The most perceived factor for barriers to sustainability management was principal's indifference to sustainability management (4.10 out of 5 points). ietitian's sustainability management practices and barriers showed a negative correlation. Conclusion: It is necessary to dietitians, employees, and principals' awareness about sustainability management in order to perform effective sustainable management school foodservice.

• Journal of the East Asian Society of Dietary Life
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• v.24 no.5
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• pp.585-603
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• 2014

The purposes of this study were to investigate managers' and chefs' perception on importance and performance of operation management of Korean restaurants, and to examine Importance-Performance Analysis (IPA) for operation management. A total of 342 managers and chefs working at Korean restaurants in Seoul and Gyeonggi province were surveyed and 314 responses were returned. Excluding responses with missing data, 250 responses were used for data analysis. In terms of importance of operation management attributes, 'sanitation management (4.38)' category received the highest scores, followed by 'facility and equipment management (4.35)', 'service management (4.17)', 'production management (4.04)', 'inventory and financial management (4.04)' and 'marketing (3.25)'. In terms of performance, the highest operation management attribute was associated with 'sanitation management (4.00)', followed by 'facility and equipment management (3.80)', 'production management (3.69)', 'inventory and financial management (3.55)', 'service management (3.51)' and 'marketing (2.53)'. As the results of IPA, 'customer care and hospitality education for hall servers', 'training hall servers for menu explanation to customers', and 'neat appearances and clean uniforms of the hall servers' fell into the Quadrant II(concentrate here).