• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.1-18
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• 1993

The family disaggregation model of hierarchical production planning (HPP) is the problem of (0 -1) mixed integer programming that minimizes the total sum of setup costs and inventory holding costs over the planning horizon. This problem is hard in a practical sense since optimal solution algorithms have failed to solve it within reasonable computation times. Thus effective familoy disaggregation algorithm should be developed for HPP. The family disaggregation algorithm developed in this paper consists of the first stage of finding initial solutions and the second stage of improving initial solutions. Some experimental results are given to verify the effectiveness of developed disaggregation algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.1-10
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• 1989

This paper deals with a single-facility multiproduction model concerning the basic cyclic scheduling. The aim is to obtain the production order of each product in a cycle and the cycle frequency for minimizing the setup costs and inventory carrying costs of all products. The problem is formulated by LP and it shows that the optimal solution derived dominates the solution of non-cyclic scheduling model on some conditions.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.93-113
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• 1997

In studying an EOQ-like inventory model for a manufacturing process, a number of findings were made. The system can "go out of control" resulting in a relatively minor problem state or "break-down". When the production system is in the minor problem statei produces a number of defective items. It is assumed that each defective piece requires rework cost and related operations. Once the machine breakdown takes place, the production system produces severely defective items that are completely unusable. Each completely unusuable item is immediately discarded and incurs handling cost, scrapped raw material cost and related operations. Two investment options in improving the production process are introduced : (1) reducing the probability of machine breakdown, breakdowns, and (2) simultaneously reducing the probability of machine breakdowns and setup costs. By assuming specific forms of investment cost function, the optimal investment policies are obtained explicitly. Finally, to better understand the model in this paper, the sensitivity of these solutions to changes in parameter values and numerical examples are provided.amples are provided.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.563-568
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• 2013

The purpose of this study is to find the analytic solution for determining the optimal capacity (lot-size) of a batch-storage network to meet the finished product demand under infrequent shutdowns. Batch processes are bound to experience random but infrequent operating time losses. Two common remedies for these failures are duplicating another process or increasing the process and storage capacity, both of which are very costly in modern manufacturing systems. An optimization model minimizing the total cost composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units is pursued with the framework of a batch-storage network of which flows are susceptible to infrequent shutdowns. The superstructure of the plant consists of a network of serially and/or parallel interlinked batch processes and storage units. The processes transform a set of feedstock materials into another set of products with constant conversion factors.A novel production and inventory analysis method, the PSW (Periodic Square Wave) model, is applied. The advantage of the PSW model stems from the fact it provides a set of simple analytic solutions in spite of a realistic description of the material flow between processes and storage units. The resulting simple analytic solution can greatly enhance a proper and quick investment decision at the early plant design stagewhen confronted with diverse economic situations.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.246-253
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• 2007

All of the machines in a production line can be classified into bottleneck and non-bottleneck machines. A bottleneck is a resource whose capacity limits the throughput of the whole production facility. This paper addresses a batch sizing problem at the bottleneck machine. Traditionally, most batch sizing decisions have been made based on the EOQ (economic order quantity) model where setup and inventory costs are considered while throughput rate is assumed to be given. However, since batch size affects the capacity of the bottleneck machine, the throughput rate may not be constant. As the batch size increases, the frequency of the setup decreases. The saved setup time can be transferred to processing time, which results in higher throughput. But, the larger batch size may also result in longer lead time and larger WIP inventory level. This paper presents an alternative method to determine batch size at the bottleneck machine in a manufacturing line. A linear search algorithm is introduced to find optimal throughput rate and batch size at the same time. Numerical examples are provided to see how the proposed method works and to investigate the effects of some parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.129-138
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• 2014

This paper is to analyze the cycle time of the vendor in a single-vendor multi-buyers supply chain. The vendor is the manufacturer and the buyers are the retailers. The cycle time of the vendor is the elapse time from the beginning time of the production to the beginning time of the next production. The cycle time of the vendor that minimizes the total cost in a supply chain is analyzed. The cost factors are the production setup cost and the inventory holding cost of the vendor, the ordering cost and the inventory holding cost of the retailers. The cycle times of the vendor obtained with the costs of the vendor is compared with those obtained with the costs of the vendor and the retailers. Various numerical examples are tested if the cycle times of the vendor for both methods are the same.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.537-544
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• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1859-1864
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• 2004

An effective methodology is reported for the optimal design of multisite batch production/transportation and storage networks under uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, internally consumed, transported to or from other plant sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between plant sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of large-scale supply chain system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.57-65
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• 2009

In continuous review inventory model, (${\varrho}$, ${\gamma}$) system, order quantity(${\varrho}$) and reorder point(${\gamma}$) should be determined to calculate inventory-related cost that consists of setup, holding, and penalty costs. The procedure to obtain the exact value of ${\varrho}$ and ${\gamma}$ is complex. In this paper, a regression analysis is proposed to get the approximate inventory-related cost without the determination of ${\varrho}$ and ${\gamma}$ in the case that the standard deviation(${\sigma}$) of the lead time demand is small or that the mean(${\mu}$) of the lead time demand is proportional to ${\sigma}$. To save inventory-related cost, central warehouses with (${\varrho}$, ${\gamma}$) system can be built. Central warehouse can provide some stores with products with the consideration of the tradeoff between inventory-related cost and transportation cost. The number and the location of central warehouses to cover all the stores are determined by a regression-based approach. The performance of the proposed approach is tested by using some computational experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.35
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• pp.89-94
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• 1995

Previous MRP models were developed without considering learning effects on the determination of the production lot size environment. The only consideration was the setup and inventory holding costs. In this paper the proposed MRP model were developed incorporated the effects of learning. The result of this model show that learning significantly influence the production lot size and total variable cost. The numerical examples have been used to illustrated the impact of proposed model.