• IE interfaces
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• v.13 no.3
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• pp.378-387
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• 2000

As the globalization of manufacturing companies continues, the scope of dependence between these companies and distributors, and other suppliers are growing very rapidly since no one company manufactures or distributes the whole product by themselves. And, the need to increase the efficiency of the whole supply chain is increasing. This paper deals with a multi-plant lot-sizing problem(MPLSP) which happens in a decentralized manufacturing system of a supply chain. In this study, we assume that the whole supply chain is driven by a single source of independent demand and many levels of dependent demands among manufacturing systems in the supply chain. We consider setup cost, transportation cost and time, and inventory holding cost as a decision factor in the MPLSP. The MPLSP is decomposed into two sub-problems: a planning problem of the whole supply chain and a lot-sizing problem of each manufacturing system. The supply chain planning problem becomes a pure linear programming problem and a Generalized Goal Decomposition method is used to solve the problem. Its result is used as a goal of the lot-sizing problem. The lot-sizing problem is solved using the CPLEX package, and then the coefficients of the planning problem are updated reflecting the lot-sizing solution. This procedure is repeated until termination criteria are met. The whole solution process is similar to Lagrangian relaxation method in the sense that the solutions are approaching the optimum in a recursive manner. Through experiments, the proposed closed-loop hierarchical planning and traditional hierarchical planning are compared to optimal solution, and it is shown that the proposed method is a very viable alternative for solving production planning problems of decentralized manufacturing systems and in other areas.

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.156-172
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• 2016

Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multi-product supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship.

• Journal of Korea Technology Innovation Society
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• v.13 no.3
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• pp.459-470
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• 2010

The aim of this study were to investigate the process of national R&D project planning by using the industry quantitative analysis(especially supply-chain enterprise ananlysis). In the selection process of funding project, the role and significance of the industry analysis can be identified as the effective guideline of using this methodology. Across the study, the following is noted : the factors of R&D effectiveness and R&D necessity are found reliable and valid. On the other hand, the emptiness of supply chain and the positive effect of foreign trade balance did not influence the selection of project. The information gained from this study will help us to define better expectations and industry analysis employing to the National R&D Project Planning.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.371-374
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• 2004

The ultimate purpose of Supply Chain Management (SCM) is maximizing the profits of the overall Supply Chain (SC) through increasing customer satisfaction and decreasing operating cost. It can be successfully accomplished only when SC system balances demands with supply activities coordinated by aggregate planning, mid-term level of Supply Chain Planning(SCP). However, the existing measures to mainly estimate the specific function of SCM are not enough to evaluate the state of SC with respect to the balance between supply and demand in operating. To solve this problem, we develop a new SC performance measure, Balancing Point, using momentum concept. a fundamental knowledge of physics. Momentum concept can explain the relation among objects so that it can consider the balance between supply and demand in SC operating. The developed measure can not only consider the current state of the SC system but also take planned but not executed supply activities and upcoming demands into account. Therefore, using Balancing point, we can be aware of the unbalanced state of SC in advance.

• IE interfaces
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• v.17 no.4
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• pp.472-481
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• 2004

In this paper, we propose a linear programming model of supply planning process for the supply chain collaboration strategy of a company. The amount of its supplying quantity relies on outsourcing suppliers heavily. Conversely, the revenues of those suppliers are highly dependent on the supplying quota from the supply network planning of the company. In order to keep the supply stable through collaboration, the company builds such a policy to guarantee the fairness on revenue between the supplies. For this, the supply network plan should keep the capacity utilization ratio even for all the suppliers. But the production capacities are different and the distribution of molds is disproportional through suppliers, so the supply network plan is not easily established with simple arithmetic processes. Therefore, we developed the linear programming model with those target function and constraints minimizing the costs for holding inventory and penalty of delayed delivery, simultaneously guaranteeing the even capacity utilization through suppliers. The proposed model has been applied to real case and the evaluation for the planning result from the model would be followed in order to make sure that our model guarantee on extracting the supply network plan subordinated to the policy. Also we mention about further studies for improvement of the model.

• 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.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.213-220
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• 2009

A revenue sharing contract is one of the mechanisms that coordinate decision makers in a decentralized supply chain toward the consensual goal. The transfer prices between different echelons in the supply chain influence the total supply chain profits. The study aims to explore various transfer pricing heuristics on the supply chain coordination in terms of the supply chain profits and their interactions with the revenue sharing rate. A model is proposed for formulating the collaborative production and distribution planning in a decentralized supply chain with the revenue sharing mechanism. Experiment results indicate that the transfer price and the revenue sharing rate affect significantly the coordination. Among the studied pricing heuristics, the variable-cost pricing method led to the best SC profits. Raising the revenue sharing rate reduced the SC profits no matter what heuristics were employed. Furthermore, the experiments provide us clues for finding the optimal transfer price for the supply chain.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.227-241
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• 2010

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.47-61
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• 2007

In SCM (supply chain management), companies are pursuing a new approach through which overall functions within the supply chain, ranging from material purchase to production, distribution, and sales are designed, planned, and managed in an integrated way. The core functions among them are production planning and distribution planning. As these problems are mutually related, they should be dealt with simultaneously in an integrated manner. SCM is large-scale and multi-stage problems. Also, its various kinds of internal or external factors can, at any time, dynamically bring a change to the existing plan or situation. Recently, many enterprises are moving toward an open architecture for integrating their activities with their suppliers, customers and other partners within the supply chain. Agent-based technology provides an effective approach in such environments. Multi-agent systems have been proven suitable to represent domains such as supply chain networks which involve interactions among manufacturing organization, their customers, suppliers, etc. with different individual goals and propriety information. In this paper, we propose a multi-agent system based on the genetic algorithm that make it possible to integrate the production and distribution planning on a real-time basis in SCM. The proposed genetic algorithm produced near optimal solution and we checked that there is a great difference in the results between integrated planning and non-integrated planning.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.143-156
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• 2005

Facility location planning (FLP) problem is the strategic level planning in supply chain. The FLP is significantly affected by the operation time of each facility. In most of the FLP researches, operation time of facility has been treated as a fixed value. However, the operation time is not a static factor in real situations and the fixed operation time may lead unrealistic FLP. In this paper, a mixed integer programming (MIP) model is proposed for solving the 3-stage FLP problem and operation times are adjusted by the results from the simulation model and an iterative approach combining the analytic model and simulation model is proposed to obtain more realistic operation plans for FLP problems.