Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

F-阶梯知识与F一阶梯挖掘一发现

利用单向导粗集((one direction singular rough sets)与它的动态特性,给出F-阶梯知识、F-阶梯度的概念。利 用这些概念,提出F-阶梯知识分辩定理、最小F-阶梯知识挖据发现定理、最大F-阶梯知识挖掘一发现定理、知识发现 依赖一筛选定理与F-阶梯知识内潜藏原理,给出F-阶梯知识挖掘一发现准则及应用。这些结果是单向导粗集的新特性 与单向导粗集的动态特性的新应用。     

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运用区间值模糊集的思想对IV-(∈,∈Ⅴq)-模糊格蕴涵子代数概念作进一步研究。给出了IV-(∈,∈Ⅴq)-模糊格蕴涵子代数的若干新的性质与刻画;获得了IV-(∈,∈Ⅴq)-模糊格蕴涵子代数的象与原象成为IV-(∈,∈Ⅴq)-模糊格蕴涵子代数的条件;讨论了IV-(∈,∈Ⅴq)-模糊格蕴涵子代数的直积特征。     

3-甲基-7-二正丙氨基-1,4-氧氮杂萘-2-酮的合成

以间氨基酚为原料,经N 烷基化、亚硝化,制得2 亚硝基 5 二正丙氨基苯酚,其收率为91 6%。该化合物经还原、成环反应后,合成出3 甲基 7 二正丙氨基 1,4 氧氮杂萘 2 酮,其收率为80 3%。实验确定1 溴丙烷、亚硝酸钠及丙酮酸乙酯的最佳用量分别为70mL、11 5g和2mL(以15g间氨基酚为基准)。     

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电炉还原熔炼氧化钴矿的生产实践

介绍了刚果(金)钴铜冶炼厂电炉还原熔炼处理钴铜氧化矿工艺的设计特点及生产实践。     

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在实验研究的基础上，对现有葡醛内酯生产工艺中存在的的问题提出了相应的对策．为了提高产率，减轻污染以降低生产成本，进行了分阶段和全流程工艺实验，确认淀粉氧化工序中硝酸的浓度以70％适宜，氧化温度应控制在50℃或稍低，氧化淀粉水解前用硼氢化钠或氢气对氧化淀粉进行还原是提高葡醛内酯产率的有效手段．通过控制用酸量、改进加料方式和改善吸收可以有效地减轻环境污染．     

Rough Set中基于聚类的连续属性离散化方法

分析了一些RoughSet中连续属性离散化的方法，指出了其中的某些不足，并给出了一个基于聚类的连续属性离散化的方法，对当前的论域中的例子根据相似性进行聚类，对每个聚类在各属性轴上的投影的边界设离 散断点。该方法考虑了各属性之间的相关性，能得到比较合理的离散结果。     

11°BX全麦芽啤酒的工艺研究

介绍了一种全麦芽啤酒生产的新工艺。通过大量的试验与探索，确定了合理的生产工艺条件，解决了全麦芽啤酒酿造时色度深、酸度高、口味粗糙等难题，从而研制出了一种具有淡爽型啤酒特征的11°BX全麦芽啤酒。     

LF半准开集的一种新定义

给出了一种新的ＬＦ半准开集定义，采用新的ＬＦ半准开集定义，较详细地研究了ＬＦ半准开集及半准连续函数的一些重要性质。