• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.139-148
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• 2009

In a real-life supply chain environment, demand forecasting is usually represented by probabilistic distributions due to the uncertainty inherent in customer demands. However, the customer demand used for an actual supply chain planning is a single deterministic value for each of periods. In this paper we study the choice of single demand value among of the given customer demand distribution for a period to be used in the supply chain planning. This paper considers distributed multi-echelon supply chain and the objective function of this paper is to minimize the total costs, that is the sum of holding and backorder costs over the distribution network under the service level constraint, by using demand selection scheme. Some useful findings are derived from various simulation-based experiments.

• The Journal of Society for e-Business Studies
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• v.7 no.2
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• pp.143-156
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• 2002

The objective of supply chain planning is to satisfy the requirements for minimizing inventory costs, transportation costs, and lead times throughout the supply, production and distribution stage dispersed geographically. Therefore, the supply chain planning system should have functionalities to resolve complex optimization problems that have characteristics of multi-stage and multi-product. Ant the system should also support collaborative decision making among distributed business partners. In this study, we proposed a distributed architecture for the supply chain planning system. To do this, we analyzed functional requirements by using IDEF-0(ICAM Definition-0) methodology, defined required components, and designed each component by using object-oriented methodology. We implemented a prototype system based on CORBA (Common Object Request Broker Architecture) to show that the proposed distributed architecture based on component technology is feasible and can solve supply chain planning problem collaboratively.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.139-150
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• 2006

The aim of this study is to establish an efficient distribution planning for a capacitated multi-stage supply chain. We assume that the demand information during planning horizon is given a deterministic form using a certain forecasting method. Under such a condition, we present a cost effective heuristic method for minimizing chain-wide supply chain inventory cost that is the sum of holding and backorder costs by using look-ahead technique. We cope with the capacity restriction constraints through look-ahead technique that considers not only the current demand information but also future demand information. To evaluate performance of the proposed heuristic method, we compared it with the extant research that utilizes echelon stock concept, under various supply chain settings.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.205-211
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• 2009

In reality, distribution planning for a supply chain is established using a certain probabilistic distribution estimated by forecasting. However, in general, the demands used for an actual distribution planning are of deterministic value, a single value for each of periods. Because of this reason the final result of a planning has to be a single value for each period. Unfortunately, it is very difficult to estimate a single value due to the inherent uncertainty in the probabilistic distribution of customer demand. The issue addressed in this paper is the selection of single demand value among of the distributed demand estimations for a period to be used in the distribution planning. This paper proposes an efficient demand selection scheme for minimizing total inventory costs while satisfying target service level under the various experimental conditions.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.04a
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• pp.57-66
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• 2000

Production-distribution planning is the most important part in supply chain management (SCM). To solve this planning problem, either analytic or simulation approach has been developed. However these two approaches have their own demerits in problem solving. In this paper, we propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation method to solve production-distribution problems in supply chain. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and adjusted by the proposed specific process according to the results from independently developed simulation model which includes general production-distribution characteristics.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.1-15
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• 2003

This paper presents a new evolutionary algorithm to solve complex multi-level integration problems, which is called multi-level symbiotic evolutionary algorithm (MEA). The MEA uses an efficient feedback mechanism to flow evolution information between and within levels, to enhance parallel search capability, and to improve convergence speed and population diversity. To show the MEA's applicability, It is applied to the integrated planning of production and distribution in supply chain management. The encoding and decoding methods are devised for the integrated problem. A set of experiments has been carried out, and the results are reported. The superiority of the algorithm's performance is demonstrated through experiments.

• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.147-160
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• 2013

Today, the management environments of intelligence firm are changing the way of production planning and logistics management, and are changing the process of supply chain management system. This paper shows the development of information system software for intelligence enterprises is used in supply chain management for robot engineering industry. Specifically, supply chain management system in this paper has been developed to analyze the impact of multi plant and multi distribution environment, showing the process analysis and system development of hierarchical assembly manufacturing industry. In this paper we consider a production planning and distribution management system of intelligence firm in the supply chain. We focus on a capacitated production resource and distribution volume allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using a genetic algorithm to solve it efficiently. This method makes it possible for the population to reach the feasible approximate solution easily. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solution converge to the feasible approximate solution quickly.

• IE interfaces
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• v.18 no.1
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• pp.63-72
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• 2005

In this paper, we present a distribution planning method for a supply chain. Like a typical distribution network of manufacturing firms, we have the form of arborescence. To consider more realistic situation, we investigated that an outside supplier has limited capacity. The customer demands are given in deterministic form in finite number of discrete time periods. In this environment, we attempt to minimize the total costs, which is the sum of inventory holding and backorder costs over the distribution network during the planning horizon. To make the best of the restricted capacity, we propose the look-ahead feature. For looking ahead, we convert this problem into a single machine scheduling problem and utilize tabu search approach to solve it. Numerous simulation tests have shown that the proposed algorithm performs quite well.

• 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.