• 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 Korean Society for Quality Management
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• v.19 no.1
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• pp.95-102
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• 1991

This paper is concerned with the production-distribution problem with multiple conflicting objectives. In reality business firms should take account not only of the profit maximization but of various environmental criteria, namely customer services, in order to improve the business logistics. A production-distribution model of goal porgramming type considering the lead time and distribution cost by products is constructed, the solution algorithm is developed, which is based on the Ignizio's method. A numerical example is given to demonstrate the applicability of goal programming for production-distribution problem.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.443-453
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• 2002

Many firms are trying to optimize their production and distribution systems separately, but possible profit increase by this approach is limited. Nowadays, it is more important to analyze these two systems simultaneously for the integrated supply chain management. This paper is a computational study to investigate the effectiveness of integrating production and distribution scheduling. We are interested in a multi-plant, multi-retailer, multi-product and multi-period industrial problem where the objective in solving production and distribution scheduling problem is to maximize the total net profit. Computational results on test problems of various sizes using the heuristic we developed show a substantial advantage of the integrated scheduling approach over the decoupled scheduling process. Sensitivity analysis on the parameter values indicates that, under the right conditions, the effectiveness of integrating production and distribution functions can be extremely high.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.139-144
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• 2008

Recently, a multi-facility, multi-product and multi-period industrial production-distribution planning problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. We have developed an optimization model to tackle the above problems under the restricted conditions such as transportation time and a zero inventory. Computational experiments using commercial tool Ms-Excel Solver show that the real size problems we encountered can be solved in reasonable time. The model can be used to decide an appropriate production-distribution planning problem in SCM research area.

• Journal of the Korea Society for Simulation
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• v.12 no.1
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• pp.59-71
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constraints. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model using a commercial genetic algorithm based optimizer. The results for computational experiments show that the real size problems we encountered can be solved in reasonable time.

• 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.26 no.4
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• pp.133-141
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• 2001

Rapid development in computer and network technology these days has created in environment in which decisions for manufacturing companies can be made in a much broader perspective. Especially, better decisions on production and distribution planning(PDP) problems can be made laking advantage of real time information from all the parties concerned. However, since the PDP problem-a core part of the supply chain management- is known to be the so-called NP-hard problem, so heuristic methods are dominantly used to find out solutions in a reasonable time. As one of those heuristic techniques, many previous studios considered genetic a1gorithms. A standard genetic a1gorithm applies rules of reproduction, gene crossover, and mutation to the pseudo-organisms so the organisms can pass along beneficial and survival-enhancing trails to a new generation. When it comes to representing a chromosome on the problem, it is hard to guarantee an evolution of solutions through classic a1gorithm operations alone, for there exists a strong epitasis among genes. To resolve this problem, we propose a hybrid genetic a1gorithm based on Silver-Meal heuristic. Using IMS-TB(Intelligent Manufacturing System Test-bed) problem sets. the good performance of the proposed a1gorithm is demonstrated.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.267-284
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• 2008

Consider a supply chain where products are produced at a manufacturing system, shipped to a distribution center, and then supplied to customers. The distribution center controls inventory based on a base-stock policy, and whenever a unit of product is demanded by a customer, an order is released to the production system. Unsatisfied demand is backordered, and the inventory and backordered units are a function of the base-stock level. The manufacturing system is modeled as an M/M/s/c queueing system, and orders exceeding the limited buffer capacity are blocked and lost. The throughput of the manufacturing system and the steady state distribution of the outstanding orders are functions of number of servers and buffers of the manufacturing system. There is a profit obtained from throughput and costs due to servers and buffers of the manufacturing system, and also costs due to inventory positions of the distribution center, and we want to maximize the total production profit minus the total cost of the supply chain by simultaneously determining the optimal number of servers and buffers of the manufacturing system and the optimal base-stock level of the distribution center. We develope two algorithms, one analytical but without guarantee of the optimal solution and one optimal but without complete analytical proofs. The problem integrates strategic problem of the manufacturing system with tactical problem of the distribution center in a supply chain.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.253-256
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• 2001

Today's rapid development in the computer and network technology makes the environment which enables the companies to consider their decisions on the wide point of view and enables the software vendors to make the software packages to help these decisions. To make these software packages, many algorithms should be developed. The production and distribution planning problem belongs to those problems that industry manufacturers daily face in organizing their overall production plan. However, this combinatorial optimization problem can not be solved optimally in a reasonable time when large instances are considered. This legitimates the search for heuristic techniques. As one of these heuristic techniques, genetic algorithm has been considered in many researches. A standard genetic algorithm is a problem solving method that apply the rules of reproduction, gene crossover, and mutation to these pseudo-organisms so those organisms can Pass beneficial and survival-enhancing traits to new generation. This standard genetic algorithm should not be applied to this problem directly because when we represent the chromosome of this problem, there may exist high epitasis between genes. So in this paper, we proposed the hybrid genetic algorithm which turns out to better result than standard genetic algorithms