• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.313-320
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• 2014

We consider the problem to find economic inventory quantity of a single commodity under stochastic demands and order cancellation. In contrast to the traditional economic production quantity (EPQ) model, we assume that once the amount of inventory reaches to a predetermined level of quantity then the production is not halted but its production speed decreases until the inventory level drops to zero. We establish two probabilistic models representing the behaviors of both the high-production period and low-production period, respectively, and derive the relationship between the level of inventory and costs of production, cancellation, and holding, from which the quantity of economic inventory is obtained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.93-103
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• 1995

This paper des with an economic production quantity model with partial backorders for the situation in which production lead time is deterministic and demand during lead time follows a continuous distribution. In the model, an objective function is formulated In minimize an average annual inventory cost. And then the procedure of iterative solution method for the model is developed to find both production reorder point and production quantity. Finally, sensitivity analysis for various partial backorder ratios and standard deviations of demand during production lead time are presented.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.93-104
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• 1994

One of the typical assumptions of the studies to determine economic production quantity is that yield rate of a given manufacturing process is 100% or constant after setup. However, in the real world, there are many manufacturing processes of which yield rates are quite low just after setup and then increasing with time until they reach the target rates which are set strategically. This period is usually called "stabilization period". During the stabilization period, defectives are produced, which incur cost (defective cost). In this study, an optimal production quantity for this situation is presented. Also, it is shown that defective cost acts like setup cost and therefore, increases the economic production quantity.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.251-260
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• 2003

Generally, in deriving the solution of economic production quantity (EPQ) inventory model, we consider the demand rate and deterioration rate as constant quantity. But in case of real life problems, the demand rate and deterioration rate are not actually constant but slightly disturbed from their original crisp value. The motivation of this paper is to consider a more realistic EPQ inventory model with finite production rate, fuzzy demand rate and fuzzy deterioration rate. The effect of the loss in production quantity due to faulty/old machine have also been taken into consideration. The methodology to obtain the optimum value of the fuzzy total cost is derived and a numerical example is used to illustrate the computation procedure. A sensitivity analysis is also carried out to get the sensitiveness of the tolarance of different input parameters.

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.1-10
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• 1992

In this paper, we study a joint lot sizing and inspection policy under an EPQ(Economic Production Quantity) model where a random proportion of units are defectives. Those units can be discovered only through costly inspection. The problem is thus bivariate : both lot size and fraction to inspect are to be chosen. We first analyze a model in which the only penalty for uninspected defectives is financial, and then consider a model where defectives units cannot be used and thus must be replaced by non-defective ones. As a result it can be proved that this inspection policy costs economically and is to be decided effectively for the Economic Production Quantity constraining the fraction to inspect.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.81-91
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• 1994

This paper is to build an economic production quantity model for situations, in which, during the stockout period, a fraction .betha.(backorder ratio) of the demand is backordered and remaining fraction (1-.betha.) is lost. This paper develops an objective function representing the average annual cost of a production system by defining a time-weighted backorder cost and a lost sales penalty cost per unit lost under the assumptions of deterministic demand rate and deterministic production rate, and provides an algorithm for its optimal solution. At the extreme .betha.= 1, the presented model reduces to the Fabrycky's model with complete backorders.

• Journal of Korean Society for Quality Management
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• v.18 no.1
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• pp.1-8
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• 1990

In this paper we present a procedure to determine the quantity of incoming materials when the nonconforming materials appear in the production process. We determined the total loss of materials $D_2$ due to fraction defectives in process and added this $D_2$ to the quantity of incoming materials $Q_0$ considered ingoing-outgoing quality level. The quantity of materials $Q_1$ as a result of this procedure should be an economic purchasing quantity and is a rectifying quantity of the EOQ determined in classical inventory model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.70-78
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• 2006

제조업에서는 보다 합리적으로 생산량을 결정함으로써 고객에 대한 납기 준수는 물론, 기업 내부의 비용을 감소시키기 위한 노력을 끊임없이 하고 있다. 합리적인 생산량의 결정은 기업 내적으로는 낭비를 제거하고, 생산 흐름의 안정성을 유지하여 주며, 기업 외적으로는 공급사슬 전체의 자재흐름을 원활히 해주고 고객의 기호 변화에 빠르게 대처할 수 있도록 한다. 이에 본 논문은 보다 높은 고객 만족도와 비용의 절감을 위해서 재고 유지비용과 생산준비비용만을 고려하는 기존의 생산량 결정 모형에 고객의 대기 비용을 추가한 다품목 경제적 생산량 모델을 제시하였다.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.87-96
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• 2019

Purpose: The purpose of this paper is to determine an optimal production time for economic production quantity model with preventive maintenance and random defective rate as the function of a machinery deteriorates. Methods: If a machinery shifts from "in-control" state to "out-of-control" state, a proportion of defective items being produced increases. It is assumed that time to state shift is a random variable and follows an arbitrary distribution. The elapsed time until process shift decreases stochastically as a production cycle repeats and quasi-renewal process is used to implement for production facilities to deteriorate. Results: When the exponential parameter for exponential distribution increases, the optimal production time increases. When Weibull distribution is considered, the optimal production time is closely affected by the shape parameter of Weibull distribution. Conclusion: A mathematical model is suggested to find optimal production time and optimal number of production cycles and numerical examples are implemented to validate the patterns for changes of optimal times under different parameters assumptions. The real application is implemented using the proposed approach.

• Economic and Environmental Geology
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• v.13 no.4
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• pp.229-239
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• 1980

A various kinds of harmful gases in the gallery and the increasing quantity of in-flow water are the important factors causing mainly the decline in production. In this thesis, the increase and decrease of in-flow water which effects the out put have been investigated and analysed in the statistical method. Through the results obtained together with the stastistical data some typical interreation formula between the quantity of in-flow water and production have been induced and the productive percentage in season was examined with special reference to Changsung Coal Mine. The formulas are as fallows: (1) Underground in-flow water to production; $Y=-5.74x^2+108.9x+6,346.6$ where, Y: production(tons/day): x:in-flow water($m^3/min$), (2) Rain and Snow fall to production; P=6.555-1.591 R+1.282S where, P;production(tons/day); R:rain fall(mm); S : snowfall(cm), (3) Productivity ratio in season compared with the average annual production; 1st quarter of year:100.1%, 2nd quarter of year: 100.3%, 3rd quarter of year: 97.2%, 4th quarter of year: 102.4%.