• Journal of Korean Institute of Industrial Engineers
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• v.35 no.4
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• pp.266-279
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• 2009

This paper focuses on the problem of scheduling wafer lots with limited waiting times between pairs of consecutive operations in a semiconductor wafer fabrication facility. For the problem of minimizing total tardiness of orders, we develop a priority rule based scheduling method in which a scheduling decision for an operation is made based on the states of workstations for the operation and its successor or predecessor operation. To evaluate performance of the suggested scheduling method, we perform simulation experiments using real factory data as well as randomly generated data sets. Results of the simulation experiments show that the suggested method performs better than a method suggested in other research and the one that has been used in practice.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.1-8
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• 2009

This paper aims mainly to reinterpretate Optimal Bus Scheduling Model by applying System Dynamics Perspective. Traditionally, the study regarding Optimal Bus Scheduling Model stems on the linear relationshp. However, this paper attempted to convert linear relationship based Optimal Bus Scheduling Model to causal loop perspective based Model. In result, the paper present Casual Loop Diagram for Optimal Bus Scheduling Model. Furthermore, the paper also ran a simulation based on Stock & Flow Diagram for Optimal Bus Scheduling Model. The outcome was not much different from the linear relationship based Model due to the similarity of the equation applied on two models.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.107-113
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• 2024

Scheduling of semiconductor test facilities has been crucial since effective scheduling contributes to the profits of semiconductor enterprises and enhances the quality of semiconductor products. This study aims to solve the scheduling problems for the wafer burn-in test facilities of the semiconductor back-end process by utilizing simulation and deep reinforcement learning-based methods. To solve the scheduling problem considered in this study. we propose novel state, action, and reward designs based on the Markov decision process. Furthermore, a neural network is trained by employing the recent RL-based method, named proximal policy optimization. Experimental results showed that the proposed method outperformed traditional heuristic-based scheduling techniques, achieving a higher due date compliance rate of jobs in terms of total job completion time.

• Journal of Preventive Medicine and Public Health
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• v.19 no.1 s.19
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• pp.56-64
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• 1986

This paper describes a GPSS-based, multi-server queueing model that was developed to simulate the patient flow, and to analyze the effectiveness of the patient scheduling system under various conditions. Unpredictable and unacceptably long waits to receive the service at the outpatient department of a general hospital necessitated the study. Arrival and service time distribution needed for the simulation model were generated from actual arrival and service patterns observed during the peak hours. The simulation results show that a change in patient scheduling system (i.e. time interval between appointments, starting time. and the number of physicians) from a current system would significantly reduce the patient wait time. This study provides the hospital administrator with an analysis of patient scheduling system under several conditions, and will be used to plan future scheduling system and staffing. Studies such as this can demonstrate the value of simulation in providing information for use in future planning.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.105-130
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• 1993

This study deals with finding a schedule for the movement of a material handling device (elevator) in a manufacturing plant. Two different algorithm (Traveling Salesman Technique and Greedy Algorithm) are used in the scheduling of the elevators using a simulation technique to determine the proper method of scheduling the elevator movement. Based on the simulation analysis, we have found that the Greedy algorithm serves better than the algorithm based on Traveling Salesman technique for scheduling the movement of a material handling device in the manufacturing plant.

• Journal of the Korea Society for Simulation
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• v.11 no.4
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• pp.57-68
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• 2002

The analysis of the cyclic scheduling problem in FMS using the transitive matrix has been proposed. Since the transitive matrix may explain all the relations between the places and transitions, we propose an algorithm to get good solution after slicing off some subnets from the original net based on machines operations. For analyzing the schedule problem, we considered two time functions such as produce time and waiting time using the P-invariant. In addition, we are shown the effectiveness of proposed algorithm after comparing with unfolding algorithms.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.197-213
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• 1997

It is not easy to find a good production schedule which can be used in practice. Therefore, production scheduling simulation with a simple dispatching rule or a set of dispatching rules is used. However, a simple dispatching rule may not create a robust schedule, for the same rule is blindly applied to all internal production processes. The presumption is that there might be a specific combination of appropriate rules that can improve the efficiency of a total production system for a certain type of orders. In order to acquire a better set of dispatching rules, simulation is used to examine the performance of various combinations of dispatching rule sets. There are innumerable combination of rule sets. Hence it takes too much computer simulation time to find a robust set of dispatching rule for a specific production system. Therefore, we propose a concept of the knowledge based simulation to circumvent the problem. The knowledge based simulation consists of knowledge bases, an inference engine and a simulator. The knowledge base is made of rule sets that is extracted from both simulation and human intuition obtained by the simulation studies. For a certain type of orders, the proposed system provides several sets of dispatching rules that are expected to generate better results. Then the scheduler tries to find the best by simulating all proposed set of rules with the simulator. The knowledge-based simulator armed with the acquired knowledge has produced improved solutions in terms of time and scheduling performance.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.395-398
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• 2011

This paper introduces stochastic cash-flow modeling integrated with simulation based scheduling. The system makes use of CPM schedule data exported from commercial scheduling software, computes the best fit probability distribution functions (PDFs) of historical activity durations, assigns the PDFs identified to respective activities, simulates the schedule network, computes the deterministic and stochastic project cash-flows, plots the corresponding cash flow diagrams, and estimates the best fit PDFs of overdraft and net profit of a project. It analyzes the effect of different distributions of activity durations on the distribution of overdrafts and net profits, and improves reliability compared to deterministic cash flow analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.286-289
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• 1989

The purpose of this paper is to design a job scheduling algorithm utilizing intelligent control technique. Rulebase is built through the evaluation of rule-set scheduling. 24 scheduling rule-sets and meta-rules are employed. An appropriate scheduling rule-set is selected based on this rulebase and current manufacturing system status. Six criteria have been used to evaluate the performance of scheduling. The performance of sheduling is dependent on random breakdown of the major FMS components during simulation.

• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.49-56
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• 2007

Grid provides a platform for the efficient execution of large-scale computing in science and engineering. In this environment, resource management and task scheduling are complex undertaking. We designed and implemented a web-based scheduling toolkit(GridTool), which can model a system and simulate scheduling scheme in Grid computing. The GridTool used GridSim, a toolkit in java-environment, as a tool for simulation. The GridTool is able to perform resource modeling, task modeling, algorithm compiling, simulation, and performance evaluation efficiently in web environment. The GridTool can be applied as a platform for Grid research and can be used to analyze the efficiency of scheduling algorithm.