• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.747-757
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• 2005

A physical variable is customarily thought of as a function. Another way of describing a physical variable is to specify it as a functional, whose special type is called a distribution. It turns out that the distribution concept provide a better mechanism for analyzing certain physical phenomena than does the function concept. By using wavelets with high regularity we give a resolution of functions with slow growth.

• Korean Management Science Review
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• v.10 no.2
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• pp.79-91
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• 1993

Strategic decisions related to the design of a physical distribution system can be classified into three basic components : facility location, transportation, inventory decisions. In this research the interdependence of those decisions are expressed in a mathematical model such that the total relevant cost of the system is minimized. We suggested a heuristic technique for solving the model. In broad terms, our solution technique combines a heuristic method for determining which candidate DCs to open and an exact method for minimizing costs given a set of open DCs. And we also developed a decision supporting package for the design of a physical distribution system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.68
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• pp.1-7
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• 2001

A probabilistic model for fatigue life of a structural component is derived when the component is in a variable-amplitude loading environment. The physical mechanism which governs fatigue failure is used to model the fatigue life. Especially, the judgement of rotational symmetry in the-stress-intensity-factors results in the probability distribution for fatigue life. The probability distribution is related to the familiar truncated Gaussian distribution, which has a single parameter with a direct physical meaning.

• Journal of Distribution Science
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• v.7 no.4
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• pp.47-56
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• 2009

The purpose of this study is to present an alternative improving the efficient and reasonable of the physical distribution system management is influenced by many factors. Therefore, the study depends on the documentary method and survey method to achieve the purpose of this study. The major components of a physical distribution system are refers to as elements, include warehouse·storage system, transportation system, inventory system, physical distribution information system. The factors used in this study are ① factor of product(quality·A/S·added value of product·adaption of product·technical competitive power to other enterprises), ② factor of market(market channel·kinds of customer·physical distribution share), ③ factor of warehouse·storage(warehouse design·size·direction·storage ability·warehouse quality), ④ factor of transportation(promptness·reliability·responsibility·kinds of transportation·cooperation united transportation system·national transportation network), ⑤ factor of packaging (packaging design·material·educating program·pollution degree measure program), ⑥ factor of inventory(ordinary inventory criterion·consistence for inventories record), ⑦ factor of unloaded(unloaded machine·having machine ratio), ⑧ factor of information system (physical distribution quantity analysis·usable computer part), ⑨ factor of physical distribution cost(sales ratio to product) ⑩ factor of physical distribution system(physical distribution center etc). The implication of this study can be summarized as follows: ① In firms that have not adopted a systems integrative approach, physical distribution is a fragmented and often uncoordinated set of activities spread throughout various functions with function having its own set of priorities and measurements. ② The physical distribution is recognized as more an important strategic factor than a simple cost reduction factor, ③ It can be used a strategic competition tool to enterprise.

• Journal of Korean Institute of Industrial Engineers
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• v.3 no.2
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• pp.41-55
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• 1977

The physical distribution system is composed of all the activities associated with getting products from the factory to the consumer through the firm's channels of distribution. An increasing amount of attention has been recently devoted to the component problems associated with distribution system analysis. This paper analyzes effect of system parameters which are safety factor of depot's stock, limitation of direct supply from shop stock to customer and ordering quantity on the physical distribution cost and the evaluated items by the Simulation. The distribution system simulator views system as being composed of three structural entities, presents a case study of a Chemical manufactures and carried on one year computer running.

• Proceedings of the Safety Management and Science Conference
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• 2007.04a
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• pp.449-456
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• 2007

This paper presents system of physical distribution module, and classification of logistics equipments. This study also introduces equipment standards of logistics facilities, and environmental rules for distribution center. Moreover, this paper shows safety standard and marking for logistics equipments and tools.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.77-86
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• 2010

The study developed physical distribution channel choice models reflecting decision making of the firms and studied how choice decision factors influence selection of distribution channel. The distribution channel survey data in Korea was used to do empirical study. As a choice set, distribution channels were classified into two main choice channels: direct and indirect channels. In addition, indirect channels were classified into other three channels according to the type of intermediate point: distribution center, wholesale store, and agency. This study developed choice models by applying both binary and multinomial logit model with various set of factors. The results showed that the developed logit models seemingly reflect distribution channel choice behaviors. The hypothesis tests on how each factor influences choice of distribution channel were performed and discussed as well.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.23
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• pp.1-6
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• 1991

The physical distribution service(PDS) is essential to evaluate the business logistics system. The PDS combines the inventory service with the lead time to deliver. This paper is presented to model Mixed Zero-One integer programming which is to determine distribution center location and to allocation products, considering delivery lead time, from given candidate locations to given customer markets. A numerical example is given to demonstrate the applicability of Mixed Zero-One integer programming for Distribution-Location problem.

• Journal of the Korean Geographical Society
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• v.50 no.6
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• pp.621-639
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• 2015

The aim of this study is to consider the physical distribution system of assembly supplied from $1^{st}$ supplier and module corporation to automobile assembly plant through analysis of existing literatures and interview survey. The major findings of this study are as follows. Parts supply of automobile are to bind together with many regional parts supplier than using multi frequency and small delivery of JIT. Regional physical distribution center of joint parts and integrated physical distribution information center is to be installed in neighboring region of automobile assembly plant in order to reduce the transport cost by designating and activating multiple physical distribution and transport companies in each automobile assembly plant and turning into effective high volume shipping system.

• Communications for Statistical Applications and Methods
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• v.23 no.5
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• pp.363-383
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• 2016

The Weibull family of lifetime distributions play a fundamental role in reliability engineering and life testing problems. This paper investigates the potential usefulness of transmuted new generalized Weibull (TNGW) distribution for modeling lifetime data. This distribution is an important competitive model that contains twenty-three lifetime distributions as special cases. We can obtain the TNGW distribution using the quadratic rank transmutation map (QRTM) technique. We derive the analytical shapes of the density and hazard functions for graphical illustrations. In addition, we explore some mathematical properties of the TNGW model including expressions for the quantile function, moments, entropies, mean deviation, Bonferroni and Lorenz curves and the moments of order statistics. The method of maximum likelihood is used to estimate the model parameters. Finally the applicability of the TNGW model is presented using nicotine in cigarettes data for illustration.