• IE interfaces
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• v.12 no.2
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• pp.222-236
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• 1999

This paper presents a design and implementation of an intelligent-integrated production-logistics systems. The situation considered here is that there are multiple manufacturing plants and multiple distribution centers. Effective distribution resource and production planning are required to reduce inventory cost and to avoid inventory shortage. We propose an intelligent forecasting scheme of each distribution centers, adaptive inventory replenishment planning, distribution resource planning, and integrated production planning system. In forecasting a huge number of on-line model identification is performed using neural network approximation capability. An efficient adaptive replenishment planning and distribution resource planning are also presented in connection with forecasting scheme. An appropriate production is also requested based on production lead-time and the results of distribution planning. Experimental simulations are presented to verify the proposed approach using real data.

• IE interfaces
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• v.16 no.3
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• pp.280-290
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• 2003

Many firms are trying to optimize their production and distribution functions separately, but possible savings by this approach may be limited. Nowadays, it is more important to analyze these two functions simultaneously by trading off the costs associated with the whole. In this paper, I treat a production and distribution planning problem for single-period inventory products comprised of a single production facility and multiple customers, with the aim of optimally coordinating important and interrelated decisions of production sequencing and vehicle routing. Then, I propose a hybrid genetic algorithm incorporating several local optimization techniques, HGAP, for integrated production-distribution planning. Computational results on test problems show that HGAP is effective and generates substantial cost savings over Hurter and Buer's decoupled planning approach in which vehicle routing is first developed and a production sequence is consequently derived. Especially, HGAP performs better on the problems where customers are dispersed with multi-item demand than on the problems where customers are divided into several zones based on single-item demand.

• 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 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 Korean Institute of Industrial Engineers
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• v.31 no.2
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• pp.120-126
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• 2005

Until now, the traditional production models and logistics have developed a broader strategic approach called supply chain. However, there are some obstacles to apply industry practice because of unrealistic assumptions. The most serious of them is that they assume the lead times are integer multiples of the planning time grid. This assumption makes it difficult to express the processing and transportation lags correctly. Thus, in this paper, we propose a new methodology for the integrated production/distribution model having non-integer time lags using the concept of dynamic production function. In case that the time lags are integer or non-integer, the dynamic production function reflects well the situation under given environments. Experiments show that the proposed model can express the real system more accurately than the prior model can.

• 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 Korean Institute of Industrial Engineers
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• v.26 no.1
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• pp.17-26
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• 2000

This paper presents an integrated order scheduling method with the improved DRP concept for multi-echelon distribution system that has the constraint of limited production capacity of producers. The proposed method reflects the dynamic characteristics of inventory level changes in the regional and central distribution center. The simulation is done with two models : the traditional DRP method and the proposed method presented in this paper. From the results, the latter is more efficient than the former in cost, customer's service level as well as balanced production load on each producer.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.165-173
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• 2000

Analytic models have been developed to solve integrated production-distribution problems in supply chain management (SCM). As one of major constraints in analytic models, capacity, which is the total operation time in this paper has mostly been known or disregarded assuming infinite capacity. Also, as major factors, machine processing time to fabricate or assemble a part or product at a certain machine center in production system and vehicle processing time to deliver a product to a customer by a certain vehicle in distribution system have been fixed and regarded as a static factor, But in the real systems significant differences exit between capacity and the required time to achieve the production-distribution plan and between processing time and consumed time to process a part or product. In this paper, capacity and processing times in the analytic model are considered as dynamic factors and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. Through experiments, we obtain the more realistic solutions reflecting stochastic natures by performing the iterative analytic-simulation procedure.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.45-52
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• 2020

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.14-20
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• 2003

Supply chain is the link that moves products between suppliers, manufactures, wholesalers, distribution, retailers and ended consumers. Supply chain management(SCM) is a way to supervise the flow of products, materials and information as they move along the supply chain. In the recent years, Most of the companies are in a hurry the introduction of SCM to obtain international competitiveness. The goal of SCM is to optimize the supply chain, which can not only reduce inventories, but may also create a higher profit margin for finished goods by giving customers exactly what they want. There are four major decision areas (location, production, inventory, transportation) in supply chain management, and there are both strategic and operational elements in each of these decision areas. This paper is concerned with the integrated production planning problem including not only the production cost but also the transportation cost in supply chains, and an efficient algorithm using genetic algorithm and quickest path method is presented to solve the problem.