• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.135-143
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• 2012

This paper suggests the optimal blood distribution center algorithm that satisfies the minimum total transportation cost and within the allowable distribution time $T^*$. Zhang and Yang proposes shifting the location of each point that has less than the average distance of two maximum distance points from each point. But they cannot decide the correct facility location because they miscompute the shortest distance. This algorithm computes the shortest distance $l_{ij}$ from one area to another areas. Then we select the $v_i$ area to thecandidate distribution center location such that $_{max}l_{ij}{\leq}L^*$ and the $v_i$ such that $l_{ij}-L^*$ area that locates in ($v_i,v_k$) and ($v_j,v_l$) from $P_{ij}=v_i,v_k,{\cdots},v_l,v_j$ path and satisfies the $_{max}l_{ij}{\leq}L^*$ condition. Finally, we decide the candidate distribution area that has minimum transportation cost to optimal distribution area.

• Journal of the Korean Institute of Navigation
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• v.23 no.4
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• pp.63-76
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• 1999

This paper aims at developing an integrated fleet management support system for industrial carriers who usually control the vessels of their own or on a time charter to minimize the cost of shipping their cargoes. The work is mainly concerned with the operational management problem of the fleet owned by a major oil company, a typical industrial carrier. The optimal fleet management problem for the major oil company can be divided into two phase problem. The front end corresponds to the production operation problem of the transportation of crude oil, the refinery operation, and the distribution of product oil to comply with the demand of the market. The back end is to tackle the fleet scheduling problem to meet the seaborne transportation demand derived from the front end. Relevant optimization models for each phase are proposed and described briefly. Then a user-friendly integrated fleet management support system is built based on the proposed optimization models for both ends under Windows environment. A case study reflecting the practices of fleet management problem for the major oil company is carried out by using the system.

• 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.

• IE interfaces
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• v.15 no.4
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• pp.474-482
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• 2002

In this paper, we propose a parametric optimization approach to simultaneously determining trip distribution, mode choice, and user-equilibrium assignment. In our model, mode choice decisions are based on a binomial logit model and passenger and cargo demands are divided into appropriate mode according to the user equilibrium minimum travel time. Underlying network consists of road and rail networks combined and mode choice available is auto, bus, truck, passenger rail, and cargo rail. We provide an equivalent convex optimization problem formulation and efficient algorithm for solving this problem. The proposed algorithm was applied to a large scale network examples derived from the National Intermodal Transportation Plan (2000-2019).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.327-331
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• 2006

This paper considers an integrated inventory-distribution system with a fleet of heterogeneous vehicles employed where a single warehouse distributes a single type of products to many spatially distributed retailers to satisfy their dynamic demands and the product is provided to the warehouse via procurement ordering from any manufacturing plant or market The Problem is formulated as an Mixed Integer Programming with the objective function of minimizing the sum of inventory holding cost (at the warehouse and retailers), and transportation cost and procurement ordering cost at the warehouse, subject to inventory-balancing constraints, ordering constraints, vehicle capacity constraints and transportation time constraints. The problem is Proven to be NP-hard. Accordingly, a Lagrangean heuristic procedure is derived and tested for its effectiveness through computational experiments with some numerical instances.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.463-468
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• 2002

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. We have developed an optimization model to tackle the above problems under the restricted conditions such as transportation time and a zero inventory. The model can be used to deride an appropriate factory and assign an optimal output the factory yields. This paper deals with the main idea of the proposed methodology in depth.

• Journal of applied mathematics & informatics
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• v.42 no.4
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• pp.831-854
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• 2024

The transportation problem (TP) is one of the earliest and the most significant implementations of linear programming problem (LPP). It is a specific type of LPP that mostly works with logistics and it is connected to day-to-day activities in our everyday lives. Nowadays decision makers (DM's) aim to reduce the transporting expenses and simultaneously aim to reduce the transporting time of the distribution system so the bi-objective transportation problem (BOTP) is established in the research. In real life, the transportation parameters are naturally uncertain due to insufficient data, poor judgement and circumstances in the environment, etc. In view of this, neutrosophic bi-objective transportation problem (NBOTP) is introduced in this paper. By introducing single-valued trapezoidal neutrosophic numbers (SVTrNNs) to the co-efficient of the objective function, supply and demand constraints, the problem is formulated. The DM's aim is to determine the optimal compromise solution for NBOTP. The extended weighted possibility mean for single-valued trapezoidal neutrosophic numbers based on [40] is proposed to transform the single-valued trapezoidal neutrosophic BOTP (SVTrNBOTP) into its deterministic BOTP. The transformed deterministic BOTP is then solved using the dripping method [10]. Numerical examples are provided to illustrate the applicability, effectiveness and usefulness of the solution approach. A sensitivity analysis (SA) determines the sensitivity ranges for the objective functions of deterministic BOTP. Finally, the obtained optimal compromise solution from the proposed approach provides a better result as compared to the existing approaches and conclusions are discussed for future research.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.580-582
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• 2005

This paper presents a new method which applies a genetic algorithm(GA) for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. The distribution system loss minimization re-configuration problem is in essence a 0-1 planning problem which means that for typical system scales the number of combinations requiring searches becomes extremely large. In order to deal with this problem, a new a roach which applies a GA was presented. Briefly, GA are a type of random number search method, however, they incorporate a multi-point search feature. Further, every point is not is not separately and respectively renewed, therefore, if parallel processing is applied, we can expect a fast solution algorithm to result.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.33-51
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• 1998

The industrial operation is one of the three basic modes of shipping operation with liner and Tramp operations. Industrial operators usually control vessels of their own or on a time charter to minimize the cost of shipping their cargoes. Such operations abound in shipping of bulk commodities, such as oil, chemicals and ores. This work is concerned with an operational optimization analysis of the fleet owned by a major oil company. a typical industrial operator. The operational optimization problem of the fleet of a major oil company is divided Into two phase problem. The front end corresponds to the optimization problem of the transportation of crude oil. product mix. and the distribution of product oil to comply with the demand of the market. The back end tackles the scheduling optimization problem of the fleet to meet the seaborne transportation demand derived from the front end. A case study reflecting the practices of an international major oil company is demonstrated to make clear the underlying ideas.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.29-40
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• 1999

The distribution center location and routing problem involves interdependent decisions among facility, transportation, and inventory decisions. The design of distribution system affects the customers' purchase decision by sets the level of customer service to be offered. Thus the lower product availability may cause a loss of demand as falls off the customers' purchase intention, and this is related to the firm's profit reduction. This study considers the product availability of the distribution centers as the measure of the demand level change of the demand points, and represents relation between customer service and demand level with linear demand function. And this study represents the distribution center location and routing to demand point in order to maximize the total profit that considers the products' sales revenue by customer service, the production cost and the distribution system related costs.