• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.201-204
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• 1997

This study is focussed on optimization problems which require allocating the restricted inventory to demand points and assignment of vehicles to routes in order to deliver goods for demand sites with optimal decision. This study investigated an integrated model using three step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations. we developed several sub-models such as; first, an inventory-allocation model, second a vehicle-routing model based on clustering and a heuristic algorithms, and last a vehicle routing scheduling model, a TSP-solver, based on genetic algorithm. Also, for each sub-models we have developed computer programs and by a sample run it was known that the proposed model to be a very acceptable model for the inventory-allocation and vehicle routing problems.

• Korean Management Science Review
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• v.26 no.1
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• pp.171-182
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• 2009

We consider the inventory replenishment problem and the vehicle routing problem simultaneously in the vending machine operation. This problem is known as the inventory routing problem. We design a memory cell in the clonal selection algorithm. The memory cell store the best solution of previous solved problem and use an initial solution for next problem. In general, the other clonal selection algorithm used memory cell for reserving the best solution in current problem. Experiments are performed for testing efficiency of the memory cell in demand uncertainty. Experiment result shows that the solution quality of our algorithm is similar to general clonal selection algorithm and the calculations time is reduced by 20% when the demand uncertainty is less than 30%.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.179-186
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• 2006

There was a lot of research to integration of the transshipment and inventory problem in supply chain. Such a integration of inventory and transshipment problem called IRP (Inventory Routing Problem). We consider a distribution problem in which a set of products has to be shipped from a supplier to several retailers in a given planning horizon. Transshipment from the supplier to the retailer is performed by vehicles of limited capacity. Each retailer determines replenishment leadtime and order quantity with buffer management. A supplier determines optimal vehicle routing in supply chain. We suggest a heuristic algorithm which be used TOC buffer management in a replenishment problem and a tabu search algorithm in VRP (Vehicle Routing Problem).

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.1-9
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• 2011

This paper presents a new method that can solve the integrated problem of combined location routing and inventory problem (CLRIP) efficiently. The CLRIP is used to establish facilities from several candidate depots, to find the optimal set of vehicle routes, and to determine the inventory policy in order to minimize the total system cost. We propose a mathematical model for the CLRIP with budget constrained. Because this model is a nonpolynomial (NP) problem, we propose a endosymbiotic evolutionary algorithm (EEA) which is a kind of symbiotic evolutionary algorithm (SEA). The heuristic method is used to obtaining the initial solutions for the EEA. The experimental results show that EEA perform very well compared to the existing heuristic methods with considering inventory control decisions.

• Korean Management Science Review
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• v.29 no.3
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• pp.107-120
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• 2012

In this paper we propose an effective genetic algorithm for solving the integrated inventory and routing problem of supply chain composed of multi-warehouses and multi-retailers. Unlike extant studies dealing with integrated inventory and routing problem of supply chain, our model incorporates more realistic aspect such as positive inventory at the multi-warehouses under the assumption of inventory policy of power of two-replenishment-cycle. The objective is to determine replenishment intervals for the retailers and warehouses as well as the vehicles routes so that the total cost of delivery and inventory cost is minimized. A notable feature of our algorithm is that the procedure for evaluating the fitness of objective function has the computational complexity closing to linear function. Computational results show effectiveness of our algorithm.

• IE interfaces
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• v.11 no.1
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• pp.85-95
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• 1998

This study is focussed on an optimal vehicle routing model for multi-supply centers in two-echelon logistic system. The aim of this study is to deliver goods for demand sites with optimal decision. This study investigated an integrated model using step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations such as the capability of supply centers, vehicle capacity and transportation parameters. Three sub-models are developed: 1) sector-clustering model, 2) a vehicle-routing model based on clustering and a heuristic algorithm, and 3) a vehicle route scheduling model using TSP-solver based on genetic and branch-and-bound algorithm. Also, we have developed computer programs for each sub-models and user interface with visualization for major inputs and outputs. The application and superior performance of the proposed model are demonstrated by several sample runs for the inventory-allocation and vehicle routing problems.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.217-225
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• 2008

The inventory routing problem (IRP) is an important area of Supply Chain Management. The objective function of IRP is the sum of transportation cost and inventory cost. We propose an Artificial Immune System(AIS) to solve the IRP. AIS is one of natural computing algorithm. An hyper mutation and an vaccine operator are introduced in our research. Computation results show that the hyper mutation is useful to improve the solution quality and the vaccine is useful to reduce the calculation time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.236-246
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• 2008

We consider the Inventory Routing problem(IRP) for the vending machine operating system. An artificial immune system(AIS) is introduced to solve the IRP. The IPR is an rolling wave planning. The previous solution of IRP is one of good initial solution of current IRP. We introduce an Artificial Immune system with memory cell (AISM) which store previous solution in memory cell and use an initial solution for current problem. Experiment results shows that AISM reduced calculations time in relatively less demand uncertainty.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.522-525
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• 2000

본 연구에서는 고객의 재고 수준을 고려하면서 동시에 연간 수송비용을 최소로 하는 차량 경로결정문제 IRP(Inventory Routing Problem)에 새로운 위성창고 (Satellite facility)개념을 도입한 수리모델과 알고리즘을 개발하였다. 위성창고는 수송 가능 용량을 초과하는 수요가 발생했을 때 수송 도중에 물량을 보충하여 재고 보충 일정 시간 내에 운반을 할 수 있도록 하는 장치이며 여기서는 용량의 한계를 고려한 위성창고 개념을 도입하였다. 또한 재고 보충 일정 관리 문제와 차량 경로 결정 문제를 분리하여 순차적으로 발견적 기법을 이용하는 기존의 방법을 통합 최적화 알고리즘으로 구현하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.142-152
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• 2009

We developed two heuristic methods to solve the problem considering a fleet of ships delivering chemical products from terminals to terminals. We need to decide how much of each product to carry, on which ship, subject to the conditions that all terminals must have sufficient products to meet demand, and the stock levels of the products cannot exceed the inventory capacity of that terminal. Mathematical formulation and the optimal objective value for the small size problems are compared with two greedy heuristic methods developed in terms of solution qualities and computing time. Numerical experiments on test problems indicate that the heuristics are effective at finding good solutions quickly.