• Journal of the Korean Operations Research and Management Science Society
• /
• v.29 no.3
• /
• pp.111-127
• /
• 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 Society for Quality Management
• /
• v.32 no.4
• /
• pp.156-168
• /
• 2004

This research investigates the statistical efficiency of variable sampling size & sampling interval(VSSI) $\bar{X}$ charts under two assignable causes. Algorithms for calculating the average run length(ARL) and average time to signal(ATS) of the VSSI $\bar{X}$ chart are proposed by employing Markov chain method. States of the process are defined according to the process characteristics after the occurrence of an assignable cause. Transition probabilities are carefully derived from the state definition. Statistical properties of the proposed chart are also investigated. A simple procedure for designing the proposed chart is presented based on the properties. Extensive sensitivity analyses show that the VSSI $\bar{X}$ chart is superior to the VSS or VSI $\bar{X}$ chart as well as to the Shewhart $\bar{X}$ chart in statistical sense, even tinder two assignable causes.

• Communications of the Korean Mathematical Society
• /
• v.28 no.2
• /
• pp.383-396
• /
• 2013

We consider an M/PH/1 queue with deterministic impatience time. An exact analytical expression for the stationary distribution of the workload is derived. By modifying the workload process and using Markovian structure of the phase-type distribution for service times, we are able to construct a new Markov process. The stationary distribution of the new Markov process allows us to find the stationary distribution of the workload. By using the stationary distribution of the workload, we obtain performance measures such as the loss probability, the waiting time distribution and the queue size distribution.

• ETRI Journal
• /
• v.19 no.1
• /
• pp.13-25
• /
• 1997

In this paper, we propose a new queuing model, MMDP/MMDP/1/K, for an asynchronous transfer mode(ATM) multiplexer with multiple quality of service(QoS) variable bit rate (VBR) traffic in broadband-integrated services digital network (B-ISDN). We use the Markov Modulated Deterministic Process(MMDP) to approximate the actual arrival process and another MMDP for service process Using queuing analysis, we derive a formula for the cell loss probability of the ATM multiplexer in terms of the limiting probabilities of a Markov chain. The cell loss probability can be used for connection admission control in ATM multiplexer and the calculation of equivalent bandwidth for arrival traffic, The major advantages of this approach are simplicity in analysis, accuracy of analysis by comparison of simulation, and numerical stability.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2004.04a
• /
• pp.413-418
• /
• 2004

The bounded adjustment is known to be more efficient than repeated adjustment when the cost is incurred for engineering process control. The procedure of the bounded adjustment is to adjust the process when the one-step predicted deviation exceeds the adjustment limit by the amount of the prediction. In this paper, two run rules are proposed and studied in order to improve the efficiency of the traditional bounded adjustment procedure. The efficiency is studied in terms of the standardized cost through Monte Carlo simulation when the procedure is operated with and without the run rules. The adjustment procedure operated with run rules turns out to be more robust for changes in the process and cost parameters. The Markov chain approach for calculating the properties of the run rules is also studied.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.16 no.1
• /
• pp.135-146
• /
• 1991

This paper shows the infinite horizon model of Partially Observable Markov Decision Process with lagged information. The lagged information is uncertain delayed observation of the process under control. Even though the optimal policy of the model exists, finding the optimal policy is very time consuming. Thus, the aim of this study is to find an .eplison.-optimal stationary policy minimizing the expected discounted total cost of the model. .EPSILON.- optimal policy is found by using a modified version of the well known policy iteration algorithm. The modification focuses to the value determination routine of the algorithm. Some properties of the approximation functions for the expected discounted cost of a stationary policy are presented. The expected discounted cost of a stationary policy is approximated based on these properties. A numerical example is also shown.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.4
• /
• pp.344-350
• /
• 2002

An alternative to traditional production planning and control systems such as MRP and JIT is the drum-buffer-rope(DBR). Using the DBR system, companies can achieve a large reduction of work-in-process (WIP) and finished-goods inventories (FGI), significant improvement in scheduling performance, and substantial earnings increase. The purpose of this paper is to analyze the effect of the DBR system in a serial production line. Using Markov process, we modeled a DBR system with three stages. For the model developed, we analyze the system characteristics and then present an optimization model for system design. The system performance is also analyzed through sensitivity analysis.

• Communications of the Korean Mathematical Society
• /
• v.3 no.2
• /
• pp.205-212
• /
• 1988

• Communications of the Korean Mathematical Society
• /
• v.6 no.2
• /
• pp.239-247
• /
• 1991

• Journal of the Korea Society for Simulation
• /
• v.27 no.1
• /
• pp.1-13
• /
• 2018

Earthwork planning is one of the critical issues in a construction process management. For the construction process management, there are some different approaches such as optimizing construction with either mathematical methodologies or heuristics with simulations. This paper propose a simulated earthwork scenario and an optimal path for the simulation using a reinforcement learning. For reinforcement learning, we use two different Markov decision process, or MDP, formulations with interacting excavator agent and truck agent, sequenced learning, and independent learning. The simulation result shows that two different formulations can reach the optimal planning for a simulated earthwork scenario. This planning could be a basis for an automatic construction management.