• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.64-73
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• 2019

The manufacturing companies under Make-To-Order (MTO) production environment face highly variable requirements of the customers. It makes them difficult to establish preemptive production strategy through inventory management and demand forecasting. Therefore, the ability to establish an optimal production schedule that incorporates the various requirements of the customers is emphasized as the key success factor. In this study, we suggest a process of designing the simulation model for establishing production schedule and apply this model to the case of a flat glass processing company. The flat glass manufacturing industry is under MTO production environment. Academic research of flat glass industry is focused on minimizing the waste in the cutting process. In addition, in the practical view, the flat glass manufacturing companies tend to establish the production schedule based on the intuition of production manager and it results in failure of meeting the due date. Based on these findings, the case study aims to present the process of drawing up a production schedule through simulation modeling. The actual data of Korean flat glass processing company were used to make a monthly production schedule. To do this, five scenarios based on dispatching rules are considered and each scenario is evaluated by three key performance indicators for delivery compliance. We used B2MML (Business To Manufacturing Markup Language) schema for integrating manufacturing systems and simulations are carried out by using SIMIO simulation software. The results provide the basis for determining a suitable production schedule from the production manager's perspective.

• Journal of the Korean Operations Research and Management Science Society
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• v.9 no.2
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• pp.15-22
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• 1984

A deterministic multiproduct, facility-in series multiperiod production planning model is analyzed, where each period demand for the product of a facility appear in a fixed proportion of that for the product of the immediately following facility. The model considers concave production and inventory costs, which can depend upon the production in different facilities. No backlogging is allowed. It is shown that the model is represented via a single source network, which facilitates development of efficient dynamic programming algorithms for computing the optimal production schedule.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.1
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• pp.1-18
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• 2012

The present study provides some extensions over a recent work in Won (2011) which investigates properties of the static newsvendor problem under a schedule involving progressive multiple discounts under the assumption that demand is given exogenously. Khouja (1995, 1996) formulated the extended versions over the classical newsvendor model with various discount policies including all-units discount and/or multiple discounts and found that the extended newsvendor models with discount schedules yield higher optimal expected profits than the classical newsvendor model with no-discounts. In this study, we establish a robust conjecture as a stronger statement than Khouja's findings with regard to the general relationship among the expected profits of newsvendor models in the sense that the conjecture holds for every order quantity as well as the optimal order quantity. The conjecture encourages the newsvendor facing quantity discounts to safely implement her own discounts policy to customer or accept quantity discounts offered by the supplier even if the optimal order quantity cannot be ordered due to additional restrictions such as budget or warehouse capacity constraints because the newsvendor models with quantity discounts always yield higher expected profit than the classic newsvendor model without quantity discounts regardless of the order quantity. Results from wide experiments with various probability distributions of demand strongly support our conjecture.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.414-424
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• 2015

In this research, we consider a smart grid network of electricity with multiple consumers connected to a monopolistic provider. Each consumer can be informed the real time price changes through the smart meter and updates his consumption schedule to minimize the energy consumption expenditures by which the required power demand should be satisfied under the given real time pricing scheme. This real-time decision making problem has been recently studied through game-theoretic approach. The present paper contributes to the existing literature by incorporating storage appliance into the set of available household appliances which has somewhat distinctive functions compared to other types of appliances and would be regarded to play a significant role in energy consumption scheduling for the future smart grid. We propose a game-theoretic algorithm which could draw the optimal energy consumption scheduling for each household appliances including storage. Results on simulation data showed that the storage contributed to increase the efficiency of energy consumption pattern in the viewpoint of not only individual consumer but also whole system.

• 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.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11B
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• pp.709-717
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• 2007

Pricing a network service aims for congestion control of the network as well as economic efficiency. A monopolistic supplier providing users with a network service on a shared link needs a pricing schedule that maximizes revenue under the link's bandwidth constraint and guarantees the bandwidth purchased by the users. In that case, nonlinear pricing is an efficient scheme which meets both requirements. This study reviews how nonlinear pricing can be applied to the network service under the constraint and shows that the nonlinear pricing may result in a fixed unit price of bandwidth as linear pricing when demand characteristics of the users follow a power law. Also, the way how the provider with incomplete information on the demand distribution seeks for the optimal pricing from the degree of the network congestion is introduced and the relationship between the development direction of the Internet and internet pricing is considered based on the results of the study.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.676-678
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• 2002

In this work, we present the novel scheduling algorithm of the multimedia data retrieval for the mobile disk drive. Our algorithm is focused on minimizing the power consumption involved in data retrieval from the local disk drive. The prime commodity in mobile devices is the electricity. Strict restriction on power consumption requirement of the mobile device put unique demand in designing of its hardware and software components. State of the art disk based storage subsystem becomes small enough to be embedded in handhold devices. It delivers abundant storage capacity and portability. However, it is never be trivial to integrate small hard disk or optical disk drive in handhold devices due to its excessive power consumption. Our algorithm ARM in this article generates the optimal schedule of retrieving data blocks from the mobile disk drive while guaranteeing continuous playback of multimedia data.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.19-21
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• 2000

This paper presents a game theory application for analyzing power transactions and market design in a deregulated energy marketplace such as PoolCo. The conventional least-cost approaches for the generation resource schedule can not exactly handle recent real-world situations. A systematic tool using game theory for the market participants is presented such that it determines the net profits through the optimal bidding strategies including the strategies for the bidding prices and bidding generations. We treat this power transaction game as incomplete information one, which means each market participants does not know other's cost function. And the demand elasticity of the energy price is considered for the realistic modeling of the deregulated marketplace.

• Proceedings of the Korean DIstribution Association Conference
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• 2000.10a
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• pp.91-105
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• 2000

Results of a study of the coordination effect in stocking and promotional markdown policies for a supply chain consisting of a retailer and a discount outlet (DCO) are reported here. We assume that the products are sold in two consecutive periods: Normal Sales Period (NSP) and subsequent Promotional Sales Period (PSP). When managers in the two periods coordinate, they share information on the demand forecast and jointly decide the stocking quantity, markdown time schedule, and markdown price to maximize mutual profit. In the absence of coordination, decisions are decentralized to optimize the individual party's objective function. Optimal coordination policy for the retailer/DCO problem setting is analyzed, and the coordination policy is compared with the uncoordinated policy to explore factors that make coordination an effective approach.