• Journal of Korean Institute of Industrial Engineers
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• v.31 no.2
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• pp.106-119
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• 2005

Uncertainties inherent in customer demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as customer demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1076-1080
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• 2015

An inventory control system was developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process. The receipt of customer orders by the warehouse proceeded in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm was developed using a periodic square wave model to represent the material flows. The adaptive concept incorporated a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The effectiveness of the scheme was demonstrated using simulations.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1040-1045
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• 2016

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at intervals and in quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model-based control algorithm was developed that uses a periodic square wave model to represent the flows of material between the processes and the storage units. The effectiveness of this approach was demonstrated by performing simulations.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.323-330
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• 2014

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.441-445
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• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• The Korean Journal of Rehabilitation Nursing
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• v.2 no.1
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• pp.95-108
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• 1999

The purpose of this study was to identify the effect of the humor intervention program, administred to the hemodialysis patient as an adaptive coping mechanism. The research design was non-equivalent control group non-synchronized design. The study method had been done by investigating the experimental group and control group through the questionnaire on 36 patients who had been out patient hemodialysis room at B hospital in Pusan from August 18 to September 15, 1998. The humor intervention program consisted of 1 TV comedy, 1 home video and 1 comedy film. The humor intervention program was provided to the experimental group for 20-30 minute 3 times every other day at hemodialysis room. Dependent variables were measured by Spielberger's State Anxiety Inventory, Zung's Self Rating Depression Scale, Lefcourt & s Humor Coping Scale. The analysis of the collected data had been done for the hemogeneity test in which general characteristics of the experimental group and the control group had been tested by $X^2$-test and the hemogeneity test had been tested by t-test before using the humor intervention program which is for anxiety, depression and coping of humor. To test the hypothesis the t-test had been given for the difference of anxiety, depression and coping of humor between the two groups. The result were summarized as follows : 1. Anxiety score in the experimental group and control group was not significant difference. 2. Depression score in the experimental group and control group was not significant difference. 3. Coping of humor score in the experimental group and control group was not significant difference. In conclusion, even though humor intervention program did not have any efficient effect on hemodialysis patients in reacting to anxiety, depression and coping humor, it caused very positive reactions from patients, and it also reducted anxiety of patients among the experimental group a little bit. If this program could be sufficiently applied ac cording to the character of every patients with a little bit different appliences such as selection of humor intervention program, frequency and period, it will be used as an efficient the humor intervention program.

• Korean Journal of Child Education & Care
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• v.19 no.3
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• pp.183-197
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• 2019

Objective: The purpose of this study was to examine the effect of a group program using theraplay on 2-year-olds' prosocial behavior. The changes of prosocial behavior in the process of program were also examined. Methods: Subjects were 12 infants who attended a child care center in W city. Subjects were attached to the experimental or control group. The experimental group participated in 11 group theraplay sessions twice a week. The adaptive social behavior inventory (Hogan et al., 1992) was used for pre and post tests. Wilcoxon rank-sum test was performed to verify the effectiveness of a group theraplay program. Every sessions was video-taped and recorded verbatim. The verbatim were analyzed using the Padgett (2001)'s qualitative data analysis method. Results: Infants who assigned to the experimental group demonstrated significant improvement in prosocial behavior. Their expressive behavior and compliant behavior gradually increased over the sessions. Conclusion/Implications: The present study showed that the use of group program utilizing theraplay was an effective strategy for improving prosocial behavior of 2-year-old infants.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.