• Management Science and Financial Engineering
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• v.3 no.1
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• pp.57-74
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• 1997

An EOQ-like inventory model for a manufacturing process is studied. The system is assumed to deteriorate during the production process. The results are either the production of a number of defective items, or the breakdown of the production machine. The optimal production lot size is derived. The model is extended to the case in which the probabilities of making defective items and machine breakdowns are a function of both the quantity (amount) and quality (performance) of the consumed setup cost (including the preventive maintenance cost). We further assume that the setup performance can be improved by investing in the performance improvement program. Hence, the same or a better setup outcome can be achieved with a lower setup cost. We then investigate the optimal setup cost and investment policy simultaneously, thereby achieving a better process quality and setup cost reduction concurrently.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.75-86
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• 1989

Joint stocking and preventive age replacement policy is considered for non-repairable items assuming instantaneous replenishment. A recursive relationship among the optimal preventive replacement ages is obtained, which shows that the preventive replacement ages in a replenishment cycle form an increasing sequence due to the inventory carrying cost. Using this relationship, a procedure is given for determining how many units to purchase on each order and when to replace each unit after it has begun operating so as to minimize the total cost per unit time over an infinite time span. The problem can be simplified if equal preventive replacement ages are assumed, and the solution is very close to that of the original unconstrained problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.100-110
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• 1990

An optimal raw mix model in stainless steel making is developed. The key raw materials in stainless steel making are stainless steel scrap, steel scrap, and alloy materials like Fe-Ni, Fe-Cr. Among those raw materials, the alloy metals are very expensive as well as rapidly price-changing items. Consequently, it is very important to develop an minimal cost raw mix scheme while the produced stainless steel satisfies the required specification in it's composition. The linear programming model is employed to determine the minimal cost raw mix scheme. Compared with the method being used, the developed linear programming model gives much faster and better solution (lower cost raw mix plan). Together with the linear programming model, the database is also developed, which includes the following: 1) data for raw materials, such as compositions, costs, densities, available inventory levels, and so on, 2) the required specifications process. The developed optimal raw mix model will be implemented in VAX computer.

• Journal of Korean Society for Quality Management
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• v.18 no.1
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• pp.65-76
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• 1990

A lot-sizing model for multi-stage MRP systems is proposed, in which known demands must be satisfied. In this model, an approach with considerations of initial inventory and limited production capacity is involved. Most of the studies on the lot-sizing techniques for multi-stage material requirement planning systems have been focused upon two basic approaches. One approach is to develope an algorithm yielding an optimal solution. Due to the computational complexity and sensitivity of the optimal solution to the problem of lot sizing, heuristic approaches are often employed. In this paper, the heuristic approach is used by sequential application of a single-stage algorithm with a set of modified cost by the concept of multi-echelon costs. The proposed method is compared with an lot-sizing method(Florian-Klein Model) to prove its effectiveness by numerical examples.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.686-692
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• 2002

Disassembly scheduling is the problem of determining the ordering and disassembly schedules of used products while satisfying the demand of their parts of components over a certain planning horizon. The objective is to minimize the sum of purchase, setup, disassembly operation and inventory holding costs. This paper considers products with assembly structure, i.e. products without parts commonality, and suggests a heuristic in which an initial solution is obtained in the form of the minimal latest disassembly schedule, and then improved considering trade-offs among different cost factors. To show the performance of the heuristic suggested in this paper, computational experiments were done on the modified existing examples and the results show that the heuristic can give optimal of very near optimal solutions within very short computation times.

• Management Science and Financial Engineering
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• v.21 no.1
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• pp.25-29
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• 2015

Recently much research efforts have focused on how to manage carbon emissions in logistics operations. This paper formulates a model to determine an optimal shipment size with aims to minimize the total cost consisting not only of inventory and transportation costs but also cost for carbon emissions. Unlike the literature assuming carbon emission factors as a given condition, we consider the emission factors as decision variables. It is allowed to make an investment in improving carbon emission factors. The optimal investment decision is shown to be of a threshold type with respect to unit investment costs. Moreover, the findings in this work provide insights on the various elements of the investment decision and their impacts.

• Journal of Korean Society for Quality Management
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• v.37 no.1
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• pp.69-79
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• 2009

Supply Chain Management is the process of planning, implementing and controlling the operations of the supply chain to satisfy customer requirements as efficiently as possible. It spans all movements and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of-consumption. Korean Standards Association claims the Supply Chain Quality Management(SCQM) as a win-win model of organizations among the supply chains for the best product/service quality to final customers. The SCQM is focused on quality of product/service and will do much for improving manufacturing performance between customer and suppliers. Consulting-teams make every effort to design suitable solution for constructing solutions and improving the performance. This study is to analyze the performance of SCQM consulting, from July of 2007 to June of 2008, and would provide some guidelines to design the optimal consulting models and develop guidebooks.

• Management Science and Financial Engineering
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• v.15 no.1
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• pp.1-31
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• 2009

This paper considers the value of warehousing contract under probabilistic demands. We consider a supply chain consisting of a supplier, a retailer and its third-party warehousing partner who provides the warehousing service to the retailer through an outsourcing contract. A typical contract is specified by initial space commitment and modification schedule. The retailer decides the order quantity for the supplier and space commitment for the outsourcing contract. Since there is close relationship between order quantity and space commitment to minimize the total cost including ordering cost, inventory carrying cost, shortage cost, and warehousing cost, we develop an analytical model under probabilistic demands, where the retailer can determine the optimal order size and space commitment level jointly. We found the closed-form optimum for a single-period case and the optimal conditions for a two-period case. To evaluate the value of contract flexibility for the two-period case, we compared the total cost under two policies; one with modification, under which the base commitment can be changed at the start of each period and the other without modification. From results of our numerical analysis, we showed that the modification policy is more cost-effective as the variability of demand increases.

• Journal of Information Technology Applications and Management
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• v.30 no.3
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• pp.1-13
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• 2023

Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.106-118
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• 2007

This paper present a new optimization model to generate aggregate production planning by considering electric cost. The new Time Of Switching (TOS) electric type is introduced by switching over Time Of Day (TOD) and Time Of Use (TOU) electric types to minimize the electric cost. The fuzzy demand and Dynamic inventory tracking with multiple plant capacity are modeled to cover the uncertain demand of customer. The constraint for minimum hour limitation of plant running per one start up event is introduced to minimize plants idle time. Furthermore; the Optimal Weight Moving Average Factor for customer demand forecasting is introduced by monthly factors to reduce forecasting error. Application is illustrated for multiple cement mill plants. The mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A simulation running on part of the system in a test for six months shows the optimal solution could save 60% of the actual cost.