• Journal of the Korea Society for Simulation
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• v.12 no.1
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• pp.35-48
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• 2003

This paper suggests the long-term strategy of the production distribution planning considering the capacity of factory production and the uncertain demand in a supply chain. This paper determines the near optimal capacity of factory production by using the advantages of mathematical and simulation models. Also, the relationship between the capacity from the suggested model and the strategy of production and distribution in a supply chain is studied. Arena is used for modeling and analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.135-149
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• 1994

Demand forecasting is to estimate the demand of customers for products and services. Since the future is uncertain in nature, it is too difficult for us to predict exactly what will happen. Therefore, when the forecasting is performed upon the uncertain future, it is realistic to estimate the value of demand as an interval or a fuzzy number instead of a crisp number. In this paper, we propose a demand forecasting method using the standard back-propagation algorithm and then we extend the method to the case of interval inputs. Next, we demonstrate that the proposed method using the interval neural networks can represent the fuzziness of forecasting values as intervals. Last, we propose a demand forecasting method using the transformed input variables that can be obtained by taking account of the degree of influence between an input and an output.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.239-244
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• 1998

The environment of the access network service market is characterized by uncertain demand and various competing alternative technologies. In Korea, despite the introduction of competition, dominant Public Network Operator(PNO) still leads the market. Therefore, the decision of PNO has a great impact on the access network evolution. In this paper, we propose an model which aims to reduce risks and both investment and operating costs, to cope with the uncertain demand and technology evolution. We expect this model to provide a tool analyze risks and evaluate various strategies on the network evolution.

• The Journal of Society for e-Business Studies
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• v.6 no.3
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• pp.197-207
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• 2001

The industrial structure comes to be complicated and for the production of the enterprise the rational and scientific forecast is necessary. The demand forecast has been widely used to linear regression, and up to now the linear regression was sharp the relationskp between then dependent variable and the independent variables. But, The real society demands accurate demand forecast from uncertain environment and subjective concept. This paper proposes the demand quantity forecast method to using of the fuzzy linear regression in uncertain and vague environment. Also, the optimum decision making of the demand quantity forecast uses integral calculus of the Sugeno to reflecting with the expert's (inventory manager) opinion.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.559-569
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• 1998

Cellular manufacturing system (CMS) has been recognized as an alternative to improve manufacturing productivity in conventional batch-type manufacturing systems through reducing set-up times, work-in-process inventories and throughput times by means of group technology. Most of the studies on the design of CMS assumed that each part has a unique process plan, and that its demand is known as a deterministic value despite of the probabilistic nature of the real world problems. This study suggests an approach for designing CMS, considering both alternative process plans and uncertain demand. A mathematical model is presented to show how to minimize the expected amortized and operating costs satisfying these two relaxations. Four heuristic algorithms are developed based on tabu search which is well suited for getting an optimal or near-optimal solution. Example problems are carried out to illustrate the heuristic algorithms and each of them is compared with the deterministic counterpart.

• The Journal of Industrial Distribution & Business
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• v.15 no.8
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• pp.1-12
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• 2024

Purpose: Marketing networks are essential for firms to gain new information and resources, yet their effect on innovation performance under uncertainty remains unclear. This study aims to elucidate the effects of technological and demand variability on the innovation performance of first-tier suppliers, considering different levels of structural holes. It particularly explores how structural holes moderate the relationship between uncertain factors and innovation performance. Research design, data and methodology: To assess the hypotheses, a survey was conducted with the first-tier suppliers. The survey targeted internal networks and the relationships between manufacturers, suppliers, and subsuppliers. Structural equation modeling was employed to validate the hypotheses using measures from previous research. Results: The findings indicate that the impact of technological uncertainty and demand variability on innovation performance varies based on the extent of structural holes in the network. Conclusions: This study provides both theoretical and practical insights for distribution channels, highlighting the competitive advantage of interfirm networks in uncertain conditions. However, the focus on the engineering industry may limit the generalizability of the findings. Future research should explore a broader range of industries to improve result applicability.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.30-35
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• 2013

Motivated by the emergency scheduling in a transportation network, this paper considers a transportation problem, in which, the truck times and transportation costs are assumed as uncertain variables. To meet the demand in the practical applications, two optimization objectives are considered, one is the total costs and another is the completion times. And then, a multi-objective optimization model is developed according to the situation in applications. Because there are commensurability and conflicting between the two objectives commonly, a solution does not necessarily exist that is best with respective to the two objectives. Therefore, the problem is reduced to a single objective model, which is an uncertain programming with a chance-constrain. After some analysis, its equivalent deterministic form is obtained, which is a nonlinear programming. Based on a stepwise optimization strategy, a solution method is developed to solve the problem. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• Korean Management Science Review
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• v.15 no.1
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• pp.33-47
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• 1998

The facility location problem considered here is to determine facility location sites under future's uncertain demand. The objective of this paper is to propose a solution method and algorithm for a two-stage stochastic facility location problem. utilizing the Benders decomposition method. As a two-stage stochastic facility location problem is a large-scale and complex to solve, it is usually attempted to use a mean value problem rather than using a stochastic problem. Thus, the other objective is to study the relative error of objective function values between a stochastic problem and a mean value problem. The simulation result shows that the relative error of objective function values between two problems is relatively small, when a feasibility constraint is added to a facility location model.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.337-340
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• 2000

In this paper, the effects of the uncertain hourly load demand are stochastically analyzed especially by the consideration of the average over generation of the Unit Commitment(UC) results. In order to minimize the effects of the actual load profile change, a new UC algorithm is proposed. The proposed algorithm calculates the UC results with the lower load level than the one generated by the conventional load forecast. In case of the worse load forecast, the deviation of the conventional UC solution can be overcome with the lower load level and the more hourly reserve requirements. The proposed method is tested with sample systems, which shows that the proposed method can be used as the basic guideline for selecting the potimal load forecast applying to UC problem.