• Journal of the Korea Society for Simulation
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• v.10 no.1
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• pp.35-50
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• 2001

Managing multi-echelon inventory systems has gained importance over the last decade mainly because integrated control of supply chains consisting of several processing and distribution stages has become feasible through modern information technology. Determination of optimal inventory policy for multi-echelon supply chain is made difficult by the complex interaction between the different levels. In this paper, we investigate performance of five inventory policies (fixed quantity order policy, fixed interval order policy, compromised order policy, lead time-fixed quantity order policy, and mixed order policy) in a multi-echelon supply chain by using a simulation model constructed with AweSim simulation language. The results of the simulation study show that the mixed order policy is the best among five inventory policies in the most test problems except the case when the stockout cost per unit is much higher than the inventory holding and transportation costs per unit.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.155-162
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• 2003

Today's network consists of a large number of routers and servers running a variety of applications. Policy-based network provides a means by which the management process can be simplified and largely automated. In this paper we build a foundation of policy-based network modeling and simulation environment. The procedure and structure for the induction of policy rules from vulnerabilities stored in SVDB (Simulation based Vulnerability Data Base) are developed. The structure also transforms the policy rules into PCIM (Policy Core Information Model). The effect on a particular policy can be tested and analyzed through the simulation with the PCIMs and SVDB.

• IE interfaces
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• v.21 no.4
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• pp.418-434
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• 2008

This paper studied issues in decision making on the production control policy of a cathode plate manufacturing process in zinc refining plant. The present production system has a long lead time from raw materials (aluminum plate) to products (cathode plate) due to many WIP inventories. Because WIP inventories are stocked at each process and moved from one place to another frequently, they are the main cause of inefficiency in the process. In this paper, to solve this problem, several production control policies have been identified and studied. Several simulation models are used to compare the performances of these production control policies. The output lead time and WIP (Work In Process) of real production system are compared with those of simulation models. PUSH, CONWIP, DBR, KANBAN and CONWIP-DBR models have been used to simulate and review the optimized production control policy that achieves the target output quantities with decreased lead time and WIP. The simulation results of each production control policy show that CONWIP and CONWIP-DBR models are the good production control policy under the present production system. Especially in present production system, CONWIP with one parameter is easier control policy than CONWIP-DBR with two parameters. Therefore CONWIP has been selected as the best optimum production control policy. With CONWIP, lead time has been reduced by 97% (from 6,653 to 187 minute) and WIP has been reduced from 1,488 to 53, compared to the present system.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.830-839
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• 2020

As a popular mathematical framework for modeling decision making, Markov decision process (MDP) has been widely used to solve problem in many engineering fields. MDP consists of a set of discrete states, a finite set of actions, and rewards received after reaching a new state by taking action from the previous state. The objective of MDP is to find an optimal policy, that is, to find the best action to be taken in each state to maximize the expected discounted reward of policy (EDR). In practice, MDP is typically unknown, so simulation-based policy improvement (SBPI), which improves a given base policy sequentially by selecting the best action in each state depending on rewards observed via simulation, can be a practical way to find the optimal policy. However, the efficiency of SBPI is still a concern since many simulation samples are required to precisely estimate EDR for each action in each state. In this paper, we propose a method to select the best action accurately in each state using a small number of simulation samples, thereby improving the efficiency of SBPI. The proposed method accumulates the simulation samples observed in the previous states, so it is possible to precisely estimate EDR even with a small number of samples in the current state. The results of comparative experiments on the existing method demonstrate that the proposed method can improve the efficiency of SBPI.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.10-26
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• 2005

In our model, we keep inventory to satisfy uncertain demands which arrives irregularly. In this situation, we have additional two constraints. First, we need to have certain amount of order consolidation (consolidation constraint) for the orders to replenish the inventory because of production or purchase constraint. And also, if we order at a certain date which was set by administrative convenience, we have amount constraint to order the consolidated order demands (capacity constraint). We showed this variant inventory policy is needed in steel industry and note that there will be possible similar case in industry. To deal with this case, we invented a variant replenishment policy and show this policy is superior to other possible polices in the consolidation constraint case by extensive simulation. And we derive a combined solution method for dealing with the capacity constraints in addition to the consolidation constraints. For this, we suggest a combined solution method of integer programming and simulation.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.99-112
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• 2006

In our model, we keep inventory to satisfy uncertain demands which arrives irregularly. In this situation, we have additional two constraints. First, we need to have certain amount of order consolidation (consolidation constraint) for the orders to replenish the inventory because of production or purchase amount constraint. And also, if we order at a certain date which was set by administrative convenience, we have capacity constraint to order the consolidated order demands (capacity constraint). We show this variant inventory policy is needed in steel industry and note that there will be possible similar case in industry. To deal with this case, we invent a variant replenishment policy and show this policy is superior to other possible polices in the consolidation constraint case by extensive simulation. And we derive a combined solution method for dealing with the capacity constraints in addition to the consolidation constraints. For this, we suggest a combined solution method of integer programming and simulation.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.17 no.3
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• pp.371-381
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• 2010

Purpose: The purpose of this study was to develop a scenario and evaluate students' performance in simulation learning of care for patients with asthma in emergency units. Methods: Meetings of experts were used to develop a scenario based on actual patients and textbook material. An evaluation protocol was developed to evaluate the simulation learning. The scenario was used in 2006 with six groups of 26 senior nursing students who participated voluntarily. Results: The scenario was developed according to the nursing process for 15 minutes of simulation learning. The nursing students were able to demonstrate their knowledge and skills. The results showed a need to improve problem solving ability. In the self-evaluation, the students reported that simulation learning helped them to integrate their knowledge to practice and recognize their weaknesses and strengths. However, the scores for self-confidence about patient care after the simulation learning were low (4.8/10). Conclusion: The scenario in this study gave the students the experience of providing qualified and secure nursing care under conditions similar to reality. Further development of a variety of scenarios for simulation learning is needed.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.171-197
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• 2008

By applying Systems Simulation technique, this paper aims to investigates the dynamics underlying the coevolution of IT(information technology) and the society. Particularly, a series of basic questions are explored to answer by developing a simulation model for the mechanisms underlying the 'hype curve' ever occurring in the course of technology diffusion into society: First, why hype curve appears in the process of technology and society coevolution. Second, how to enhance the tapering level at the final stage of coevolution. Third, what are the key policy leverages and when is the right time for the policy intervention. As now, inflated expectations regarding ubiquitous information technology (u-IT) are growing very fast and higher than those for the previous technologies, which would result in overshoot followed by collapse of visibility and thus incur tremendous amount of social costs. In this regard implications drawn from this study perhaps give some insights not necessarily to the academics but also to the practitioners and policy makers facing the advent of u-IT as a new emerging horizon of information society.

• Korean System Dynamics Review
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• v.5 no.2
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• pp.125-147
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• 2004

System dynamics model has been developed and computer simulation has been peformed for the evaluation of R&D policy. One of the main results of the basecase scenario is as follows. After simulation of nuclear R&D resource allocation strategies, we discovered that their net benefit value was maximum at 130% nuclear R&D budget case. And after simulation of human resource management strategies and policy research program strategies, we confirmed that it is beneficial to allocate budgets in the early phase for human resources management program and research program for the policy.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.511-520
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• 2015

Inventory, cost, and the level of service are three interrelated key metrics that most supply chain organizations are striving to optimize. One way to achieve this goal is to create a simulation model to conduct sensitivity analysis and optimization on several different supply chain policies that can be implemented in actual operation. In this paper, a case of Xerox global supply chain modeling and analysis to assess several "what if" scenarios for the consumable policy safety stock is presented. The simulation model, combined with analytical cost model and optimization module, is used to optimize the policy safety stock level to achieve the lowest total value chain cost. It was shown quantitatively that the policy safety stock can be reduced, but it is offset by the inbound premium transportation cost to expedite supplies in shortage, and the outbound premium transportation cost to send supplies to customers via express shipment, requiring fine balance.