• Title/Summary/Keyword: Transportation-Inventory

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Optimal Inventory-Transportation Policy for the Simplified Physical Distribution System (단순화된 물적유통체계에서의 최적재고-운송정책)

  • 차동원;류춘번
    • Journal of the Korean Operations Research and Management Science Society
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    • v.3 no.1
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    • pp.69-73
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    • 1978
  • This paper deals with the problem of determining the optimal inventory-transportation policy of the idealistically simple inventory-transportation system with the following assumptions: (1) The system consists of a single central warehouse and a single local warehouse, (2) The planning horizon is finite, (3) Demand rate is fixed costant, and so forth. Developed is the algorithm by which to identify the optimal inventory policy which minimizes the total cost incurred to the system over the given finite planning horizon. A sample numerical example is presented along with a discussion of the possible applications of the approach used n the algorithm.

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A Model of Dynamic Transportation Planning of the Distribution System Using Genetic Algorithm (유전 알고리듬을 이용한 물류시스템의 동적 수송계획 모형)

  • Chang Suk-Hwa
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.27 no.2
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    • pp.102-113
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    • 2004
  • This paper addresses the transportation planning that is based on genetic algorithm for determining transportation time and transportation amount of minimizing cost of distribution system. The vehicle routing of minimizing the transportation distance of vehicle is determined. A distribution system is consisted of a distribution center and many retailers. The model is assumed that the time horizon is discrete and finite, and the demand of retailers is dynamic and deterministic. Products are transported from distribution center to retailers according to transportation planning. Cost factors are the transportation cost and the inventory cost, which transportation cost is proportional to transportation distance of vehicle when products are transported from distribution center to retailers, and inventory cost is proportional to inventory amounts of retailers. Transportation time to retailers is represented as a genetic string. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. A mathematical model is developed. Genetic algorithm procedure is suggested, and a illustrative example is shown to explain the procedure.

Coordinated Transportation and Inventory Decision using Shipment Consolidation (선적 통합을 이용한 수송과 재고의 통합 결정에 관한 연구 분석)

  • Hong, Gi-Seong
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.11a
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    • pp.661-664
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    • 2006
  • Under a VMI (Vendor Managed Inventory) system, the vendor holds a certain level of control over not only inbound replenishment decisions on stocking but also outbound re-supply decisions. In this situation, vendor faces a better opportunity to synchronize the inventory and transportation decisions. However, shipment consolidation can reduce transportation expenses, but delivery time about the customer comes to be long and a customer service is fallen. Thus, a stock and transportation decision must consider this correlation. This study look into the relevant literature and suggest about further research direction.

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Determination of the Transportation Cycle Time and the Vehicle Size in a Distribution System (물류시스템에서 수송주기와 차량크기의 결정)

  • Chang Suk-Hwa
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.27 no.4
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    • pp.23-32
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    • 2004
  • This paper addresses a model for the transportation planning that determines the transportation cycle time and the vehicle size to minimize the cost in a distribution system. The vehicle routing to minimize the transportation distance of the vehicles is also determined. A distribution system is consisted of a distribution center and many retailers. Products are transported from distribution center to retailers according to transportation planning. A model is assumed that the time horizon is continuous and infinite, and the demand of retailers is constant and deterministic. Cost factors are the transportation cost and the inventory cost, which the transportation cost is proportional to the transportation distance of vehicle when products are transported from distribution center to retailers, and the inventory cost is proportional to inventory amounts of retailers. A transportation cycle time and a vehicle size are selected among respective finite alternatives. The problem is analyzed, and a illustrative example is shown.

A Simulation Study for the Inventory Pooling Effect (재고풀링효과의 시뮬레이션 연구)

  • Jung, Jaeheon
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.35 no.4
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    • pp.211-218
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    • 2012
  • We analyzed the effect of inventory pooling on the system where multiple depot was used to replenish retailers and where inventories are kept only on the depots. Inventory pooling consists of inventory integration and inventory exchange. We used simulation for checking the cost saving effect of reducing the number of depot (Inventory Integration) for the case when inventories kept on every depots are commonly used for all retailers when certain depot have stock out for their retailer assigned to them (Inventory Exchange) with the constraint of service level. Simulation on wide range of parameter settings results show that cost saving effect from inventory integration diminishes when transportation cost between depot and retailers or stock out cost, or retailer number increases. The effect becomes stronger when the demands on retailers have bigger variance or average. Also the results show that the cost saving effect from inventory exchange becomes stronger on the same situation when inventory integration effect becomes stronger.

A Distance-Based Simulated Annealing Algorithm for the determination of the Number and the Location of Centralized Warehouses (중앙창고의 수와 위치 결정을 위한 거리 기반 Simulated Annealing 앨고리듬)

  • Lee, Dong-Ju;Kim, Jin-Ho
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.30 no.3
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    • pp.44-53
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    • 2007
  • Forming central warehouses for a number of stores can save costs in the continuous review inventory model due to economy of scale and information sharing. In this paper, transportation costs are included in this inventory model. Hence, the tradeoff between inventory-related costs and transportation costs is required. The main concern of this paper is to determine the number and location of central warehouses. Transportation costs are dependent on the distance from several central warehouses to each store. Hence, we develop an efficient simulated annealing algorithm using distance-based local search heuristic and merging heuristic to determine the location and the number of central warehouses. The objective of this paper is to minimize total costs such as holding, setup, penalty, and transportation costs. The performance of the proposed approach is tested by using some computational experiments.

A Dynamic Transportation-Inventory Model with Quantity Discounts and Disposals (가격할인과 처분이 가능한 동적 수송-재고 모형)

  • Shon, Kweon-Ik
    • Journal of Korean Institute of Industrial Engineers
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    • v.16 no.1
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    • pp.27-36
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    • 1990
  • This study deals with the dynamic transportation-inventory model for a single product from which the optimal procurement quantities and the transportation modes are determined simultaneously over a finite planning horizon. Moreover, it covers the situation where quantity discounts are applied to the transportation cost as well as the purchase cost and disposals of the excess are possible at the end of each period. For a relevant mathematical model formulated, the theorems and properties of an optimal solution are discussed to present the efficient algorithm. A numerical example is solved to illustrate the algorithm developed.

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Allocating Storage Spaces for Temporary Inventories Considering Handling, Transportation, and Storage Capacities (취급, 수송 및 저장능력을 고려한 임시 재고의 저장 공간 할당)

  • Won Seung-Hwan;Kim Kap-Hwan
    • Journal of the Korean Operations Research and Management Science Society
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    • v.31 no.3
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    • pp.11-25
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    • 2006
  • Space may be a scarce resource in manufacturing shops, warehouses, freight terminals, and container terminals. This Paper discusses how to locate temporary storage Inventories In limited storage areas. A typical inventory is delivered from the location of the preceding process to the storage area and stored In the storage area during the certain period of time. And it may be relocated from the storage position to another. Finally. it is delivered from the final storage area to the location of the next process. Because this logistic process for an inventory requires handling activities, transportation activities, and storage spaces, the limitation in capacities of handling equipment, transportation equipment, and storage space must be considered when allocating spaces to the inventory. This problem Is modeled as a multicommodity minimal cost flow problem. And a heuristic algorithm for the Problem is proposed. Numerical experiments are presented to validate the mathematical model and the heuristic algorithm.

A Simulation Study for the Inventory Policy in the 2 Echelon System (시뮬레이션을 통한 2단계 재고시스템에서의 재고정책 비교연구)

  • Jung, Jaeheon
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.38 no.3
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    • pp.136-148
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    • 2015
  • We analyzed the effect of three different types of inventory systems for saving the total cost using simulation on the system where multiple depots and many retailers disperse on the limited area. Three types of inventory systems are single echelon system with inventory exchange and two-echelon system and the variant two-echelon system. Variant two echelon system is the two-echelon system where the inventory transshipmentsare allowed on every two stage inventory echelons. Inventories kept on every retailer are commonly used for all retailers when certain retailer has stock-out. And when all retailers are stock-out, inventories kept on every depot are commonly used for the retailers whose assigned depots are stock-out. These all three systems are simulated with the constraint of service level on wide range of parameter settings. Simulation results show that cost saving effect appear clear for single echelon system and two-echelon system when shortage cost portion and transportation cost portion becomes large respectively irrespective of depot number. Variant two echelon system seems to be superior to two other systems when transportationcost portion becomes very small. But this superiority is not proved in terms of statistics. So we may conclude that the variant two echelon system may be useless with the higher administrative efforts due to frequent inventory exchange. Also we note that the traditional two echelon system becomes inferior to two other systems in terms of statistics when service level becomes high or when demand variance becomes very large. And inventory integration effect that cost becomes saved when depot number decrease, diminishes when transportation cost or stock-out cost increases irrespective of inventory systems.

The Transportation Size and the Location of Distribution Centers in a Distribution System (물류시스템에서 수송크기와 물류센터의 위치)

  • Chang, Suk-Hwa
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.30 no.3
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    • pp.12-19
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    • 2007
  • This paper is to determine the transportation size and the location of distribution centers to minimize logistics cost in a distribution system where products are transported from the distribution centers to the retailers. Logistics cost consists of the fixed cost of distribution centers, the transportation cost from the distribution centers to the retailers and the inventory holding cost in the retailers. The logistics cost is affected by the transportation size and the location of distribution centers. The transportation size affects transportation cost and inventory holding cost. The location of distribution centers affects the transportation cost. A mathematical model is formulated and the algorithm is developed. A numerical example is shown to explain the problem.