Supply chain is usually represented by a network (which is called supply chain network) that contains some nodes. In a supply chain network these nodes are suppliers, plants, distribution centers and customers which are some facilities connected by some arcs to each other. The arcs connect the nodes in the direction of their production flow, meaning that each arc shows a route between the facilities for transporting the products. A multi-stage supply chain network (MSCN) is defined as a sequence of multiple supply chain network stages. This paper addresses a typical supply chain network problem which is based on a two-stage single-product system under uncertain conditions such that both cost and constraint parameters are interval numbers. The combination of these uncertain parameters are considered in this typical problem for the first time. In this case, two different order relations (the order relations UC ≤ and HW ≤ ) for interval numbers are considered. Then, two solution procedures are developed in order relations for the interval two-stage supply chain network design problem. The efficiency of the proposed method is illustrated by a numerical example where it is proved that the relation HW ≤ shows better performance than the relation UC ≤ .

Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However, there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable prec

Disassembly of products at their end-of-life (EOL) is a prerequisite for recycling of remanufacturing, since most products should be disassembled before being recycled of remanufactured as secondary parts or materials In disassembly sequence planning of E

This paper is to propose two computation procedures of reliability measures for large interval data. First method is efficient to verify the relationship among four reliability measures such as F(t), R(t), f(t) and λ(t). Another method is effective to interpret the concept of various reliability measures. This study is also to reinterpret and recompute the errors of four reliability measures discovered in the reliability textbooks. Various numerical examples are presented to illustrate the application of two proposed procedures.

Among agronomists, there appears to be a confusion in selecting among standard deviation (SD), standard error (SE) and confidence interval (CI) in reporting their results as figures and graphs. If there is a confusion in selection among them, there should also be difficulties in interpreting results published in peer-reviewed journals. This review paper aims to help researchers better suited for reporting their results as well as interpreting others by revisiting the definition of SD, SE and CI and explaining in plain words the concepts behind the formula. A variation among observation obtained from an experiment can be explained by the use of SD, a descriptive statistic. If one wants to draw an attention to a variation observed among plant germplasm collected from different regions or countries, SD can be reported along with the mean so that readers can get an idea how much variation exists in the particular set of germplasm. When the purpose of reporting experiment results is about inferring true mean of the population, it is advised to use SE or CI, both inferential statistics. For example, a certain chemical compound is to be quantified from plant materials, estimated mean with SD does not tell the range where the true mean content of the chemical compound would lie. It merely indicates how variable the measured values were from replications. In this case, it would be better to report the mean with SE or CI. The author recommends the use of CI over SE since CI is a sort of adjusted SE. The adjustment comes from t value that considers not only the probability but also n size.

본 연구에서는 수질오염총량관리단위유역의 말단부에서 8일 간격으로 계측된 유량자료가 있을 때 이를 연속적인 일유량으로 확대할 수 있는 방법론을 제시하였다. 이 방법은 부분계측이 이루어지는 지점의 결측치를 인근 혹은 수문학적으로 유사한 지점에서 연속계측된 유량자료를 이용하여 보완하는 방식이다. 이를 위해 먼저 부분계측이 수행된 날짜와 같은 날의 유량을 연속계측자료로부터 추출한다. 그 다음 두 자료간에 상관도가 높다면 이를 잘 표현하는 확장식을 개발하고 이 식을 통해 결측치를 내삽 또는 외삽한다. 본 연구에서는 두 자료간 상관성을 잘 묘사하는 방법으로 분산유지법을 제안하였고, 이를 부분계측과 연속계측이 동시에 수행된 지점의 유량자료를 통해 그 정확성을 검증하였다. 검증된 분산유지법을 이용하여 한강수계 총량관리단위유역 중 15개 유역을 선택하여 각 유역의 말단부에 8일 간격으로 계측된 유량을 연속 일유량으로 확장시켰다. 확장된 자료를 기반으로 유황분석을 통해 저수량을 산정하였다.