• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.2
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• pp.15-23
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• 1985

A multi-product multi=facility production planning model is in which known demands must be satisfied. The model considers concave production costs and piecewise concave inventory costs in the introduction of production capacity constraints. Backlogging of unsatisfied demand is permitted. The structure of optimal production schedules is characterized and then used to solve an illustrative numerical problem.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.39-45
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• 2020

In the production process, the buffer acts as a buffer to alleviate some of the problems such as delays in delivery and process control failures in unexpected situations. Determining the optimal buffer size can contribute to system performance, such as increased output and resource utilization. However, there are difficulties in allocating the optimal buffer due to the complexity of the process or the increase in the number of variables. Therefore, the purpose of this research is proposing an optimal buffer allocation that maximizes throughput. First step is to design the production process to carry out the research. The second step is to maximize the throughput through the harmony search algorithm and to find the buffer capacity that minimizes the lead time. To verify the efficiency, comparing the ratio of the total increase in throughput to the total increase in buffer capacity.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.157-165
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• 1998

This paper considers the single-product production and transportation problem with discrete time, dynamic demand and finite time horizon, an extension of classical dynamic lot-sizing model. In the model, multiple freight container types are allowed as the transportation mode and each order (product) placed in a period is shipped immediately by containers in the period. Moreover, each container has type-dependent carrying capacity restriction and at most one container type is allowed in each shipping period. The unit freight cost for each container type depends on the size of its carrying capacity. The total freight cost is proportional to the number of each container type employed. Such a freight cost is considered as another set-up cost. Also, it is assumed in the model that production and inventory cost functions are dynamically concave and backlogging is not allowed. The objective of this study is to determine the optimal production policy and the optimal transportation policy simultaneously that minimizes the total system cost (including production cost, inventory holding cost, and freight cost) to satisfy dynamic demands over a finite time horizon. In the analysis, the optimal solution properties are characterized, based on which a dynamic programming algorithm is derived. The solution algorithm is then illustrated with a numerical example.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.273-284
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• 2007

This paper studies a periodic review inventory model with an e-MarketPlace transaction in reconfigurable manufacturing system(RMS). A decision maker can expand/reduce production capacity/quantities and/or replenish/dispose inventories from/to e-MarketPlace urgently to satisfy the stochastic demands. If inventories are replenished or disposed through e-MarketPlace, this leadtime is shorter than the production leadtime, but unit purchasing or selling cost is more expensive than that of expanding capacity or reducing production quantities respectively. Henceforth, trade-off on these alternatives is considered. In addition to this, in order to consider the economy of scale, our model includes the fixed cost for purchasing from e-MarketPlace and capacity expansion. We use dynamic programming and K convexity methods to characterize the nature of the optimal policy. Finally, We present the optimal inventory control policy which is composed by the combinations of a base stock and (s,S) type policy.

• Fisheries and Aquatic Sciences
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• v.12 no.2
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• pp.118-129
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• 2009

To develop a sustainable management model for oyster farming in Pukman Bay, Korea, we estimated the carrying capacity for oyster farming using food availability data. Optimal culture densities were calculated to be 124-133 individuals per unit flux area ($m^2$) and 310-330 individuals per string. The present annual production is approximately 1,038 tons/year, which is 87% of the estimated maximum yield of 1,193 tons/year. Therefore, considering annual fluctuations and a critical buffer to reduce ecological impacts, the current level is within optimal conditions. During periods of increased water temperature, energy demand was largely met by high primary production. The food supply significantly decreased as the harvest season approached, and 10 out of 21 oyster farms had a deficient food supply for at least 1 month. Therefore, these farms (39% of the farms within the bay) exceeded optimal densities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1579-1602
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• 2020

In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system's multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.39 no.3
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• pp.77-83
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• 2007

This study was carried out to examine the optimum culture condition for the production of cellulose from Saprolegnia ferax and its physical characteristics. Conclusions obtained from the results of this study were as follows: In producing the cellulose from S. ferex, optimal pH and temperature were 7.0 and $26{\sim}30^{\circ}C$ with a maximum of $26^{\circ}C$, respectively. And, optimal culture period was 11days. WHC and OHC of biocellulose were 3.2(25.04 g/g) times and 3.5(25.75 g/g) times higher than those of commercial ${\alpha}-cellulose$(7.57, 7.25 g/g) respectively. The viscosity of biocellulose is lower than that of commercial ${\alpha}-cellulose$. And the effect of rpm on the viscosity of biocellulose was more than on the that of ${\alpha}-cellulose$.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.750-755
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• 2014

Product shortage which causes backordering and/or lost sales cost is very popular in chemical industries, especially in commodity polymer business. This study deals with backordering cost in the supply chain optimization model under the framework of process-inventory network. Classical economic order quantity model with backordering cost suggested optimal time delay and lot size of the final product delivery. Backordering can be compensated by advancing production/transportation of it or purchasing substitute product from third party as well as product delivery delay in supply chain network. Optimal solutions considering all means to recover shortage are more complicated than the classical one. We found three different solutions depending on parametric range and variable bounds. Optimal capacity of production/transportation processes associated with the product in backordering can be different from that when the product is not in backordering. The product shipping cycle time computed in this study was smaller than that optimized by the classical EOQ model.

• Journal of Industrial Technology
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• v.29 no.A
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• pp.183-190
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• 2009

This study deals with the single-product production and transportation model with dynamic demand over finite time horizon, in which the optimal production(order) quantities, transportation modes and the number of each vehicles are determined simultaneously. The finite number of identical vehicles with capacity constraint is given to each mode. Production and transportation costs are assumed to be concave function for generality. For a relevant mathematical model formulated, the theorems and properties are discussed to present the efficient algorithm. A numerical example is solved to illustrate the algorithm developed.

• Journal of Korean Institute of Industrial Engineers
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• v.2 no.1
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• pp.93-100
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• 1976

Given a fixed production process and facility capacity, the ability to respond to market fluctuations in terms of changes in production, work force, and inventory is the major task of production management. The costs involved are primarily payroll (regular and overtime), inventory carrying, and hiring and firing. The magnitude of these costs is usually a significant portion of the operating costs of the firm and consequently a small percentage saving due to astute aggregate scheduling can mean substantial absolute saving. At least three demonstrably optimal techniques have been developed for solving this aggregate scheduling problem. These three optimal are apparently LDR, PPP, and SDR. By combining these three different approaches, another optimal solution was obtained by me. I call this CDR (Combined Decision Rule). This approach appears to be useful. This approach may be generalizable to aggregate scheduling involving a short term resources.