Operational excellence in a green supply chain for environmental management: A case study

Nowadays, organizations have started to become more conscious about the environment in their supply chain operations. The greening process has guided supply chain practices into new ways of thinking according to green standards. The assessment of the performance of green supply chain management (GSCM) requires a holistic view for the whole supply chain. In this context, given that becoming green in the operational side of activities is essential, the performance assessment of operational activities also requires a holistic view to be taken. In this paper, an attempt has been made to improve the performance of GSCM by examining and evaluating the green operational excellence of a hot dip galvanizing company. The framework includes several green operational excellence key criteria, namely, quality management, efficiency management, green production/manufacturing, eco‐packaging, and green design. First, the weights of the criteria and the respective measurements were found by fuzzy analytic network process. Then, the overall operational performance score was found by a weighted scoring method. Finally, both managerial and theoretical implications were suggested according to the outcomes and findings of the case study.     

Moving beyond the tensions in open environmental innovation towards a holistic perspective

This paper analyses factors determining open environmental innovation (EI) and explores the possibility of embracing their tensions to achieve integrated environmental strategic goals. Building on recent literature linking open innovation and EI, the study assumes open EI strategic goals as a multidimensional construct based on the “synergistic and interwoven nature” of four identified enabling factors, that is, partners, knowledge, benefits, and motivations. A fuzzy‐set qualitative comparative analysis (fsQCA) based on Boolean algebra is conducted to analyse a set of 186 European companies operating in the telecommunication and information and communication technology (ICT) service industries. Making important theoretical, managerial, and policy contributions, this paper offers insights into the key factors enabling open EI and the tensional relationships within and between them. In doing so, it captures the non‐linear effects among these factors and the strategic goals related to an open EI strategy. It also suggests how the paradox theory might help in addressing the tensional dilemma of open EI strategy.     

基于两阶段法的多车型卷烟配送路径优化研究

在烟草商业企业中,物流是其重要的环节,是连接客户与烟草企业间的纽带,其卷烟配送线路设计的合理与否直接影响到卷烟的配送效率和配送成本,就如何合理的规划线路成为卷烟配送的关键问题。文章以Q烟草公司为例,运用先分组后路线的两阶段法对问题进行求解,先利用K-means聚类算法对区域进行划分,再考虑工作量均衡的条件下引入遗传算法对区域进一步调整,最后利用混合遗传算法对各配送区域进行线路优化,通过实例从线路数、车辆数、空载率、配送里程及总成本等指标上可以看出两阶段法的配送效果更优。     

一种加权聚类划分决策树算法

针对斜划分决策树算法普遍存在时间效率低、部分算法仅能应用于二分类问题，提出了一种基于加权距离的聚类决策树算法。通过Relief-F算法为预测属性计算权重，并将权重用于树结点中数据的聚类过程，使用分簇结果对结点进行多路划分，得到可直接用于多分类问题的决策树。理论分析和实验结果表明，该算法与经典轴平行决策树相比，拥有更好的泛化能力以及相近的算法时间复杂度，与大部分斜决策树相比，在付出更少计算代价的前提下，获得了近似的正确率以及模型简洁度。     

结合新型模糊支持向量机和证据理论的多传感器水质数据融合

在多传感器水质数据融合领域，证据理论是有效的数据融合方法之一，但基本概率分配一般不易确定，从而使数据融合能力难以有效发挥。支持向量机是统计学习理论之上的高级分类算法，具有普适性和全局优化等特点，但输出的基本概率分配有待进一步提高。提出了一种基于证据理论和新型模糊支持向量机相结合的数据融合方法，通过建立基于分类超平面距离的模糊隶属度，训练模糊支持向量机提高传统支持向量机的基本概率分配，并结合证据理论进行海河水质数据融合。通过证据理论分别结合支持向量机和模糊综合评价法与上述方法进行对比实验，经精度、平均绝对百分误差、均方根误差等指标验证，精度提高10.5%，表明所提方法是一种可靠的多传感器的水质融合方法，较其他方法具有更高的融合精度。     

基于显著区域提取的红外图像舰船目标检测

针对复杂海面背景下红外图像舰船目标由于灰度不均匀、海杂波干扰大等因素造成的自动检测虚警率高、准确率低的问题，提出了一种显著区域提取和目标精确分割相结合的红外舰船目标检测方法。首先，利用基于图论的视觉显著性(Graph-based Visual Saliency ,GBVS)模型计算待检测图像的显著图，使得目标区域信息增强；其次，结合舰船目标先验信息（长短轴、面积等），利用多级阈值划分算法提取关注的显著区域，并确定原图中候选目标区域；最后，利用空间约束模糊C均值(Fuzzy C-Means,FCM)算法对候选区域进行分割，结合目标先验知识对分割区域筛选并输出目标位置。所提方法在公开数据集IRShips上与相关方法进行比较，结果表明，相比直接进行全图目标搜索的方法，所提方法不仅准确率高、执行速度快，且检测目标的位置更加精确。     

利用改进特征金字塔模型的SAR图像多目标船舶检测

深度学习模型中的特征金字塔网络（Feature Pyramid Network，FPN）常被用作合成孔径雷达（Synthetic Aperture Radar，SAR）图像中多目标船舶的检测。针对复杂场景下多目标船舶检测问题，提出了一种基于改进锚点框的FPN模型。首先将特征金字塔模型嵌入传统的RPN（Region Proposal Network）并映射成新的特征空间用于目标检测，然后利用基于形状相似度距离（Shape Similar Distance,SSD）度量的Kmeans聚类算法优化FPN的初始锚点框，并使用SAR船舶数据集测试。实验结果表明，所提算法目标检测精确率达到98.62%，在复杂场景下与YOLO、Faster RCNN、FPN based on VGG/ResNet等模型进行对比，模型准确率提高，整体性能更好。     

一种基于自适应相似矩阵的谱聚类算法

为了消除在构建谱聚类算法的相似矩阵时,高斯核函数中尺度参数的波动影响,构建了一种自适应相似矩阵,并应用到谱聚类算法中。自适应相似矩阵中数据点间的距离度量采用测地距离算法,相距较近的两点间的距离近似于欧氏距离,相距较远的两点则先根据欧氏距离得到每个数据点的k个近邻点,然后累加近邻点的测地距离,由此得到每对数据点间的最短距离。两点间的局部密度用共享近邻的定义来表示,更好地刻画了数据集的本征结构。在5个人工数据集和国际通用UCI数据库中的5个真实数据集上进行实验。实验结果表明,所提算法的聚类准确率高于对比算法的准确率,对复杂分布数据有很强的自适应能力。研究成果为数据挖掘及机器学习提供了思路和方法。     

Consumer Trends and Attitudes to Functional Foods

Abstract

Consumers' needs have grown to such an extent that they are sought out of nutritious and pleasant organoleptic foods, as well as functional foods. This study explores consumer attitudes towards health nutrition products and particularly functional foods, using data from a survey to 325 consumers. The results designate that consumers recognize different kinds of functional foods, willing to pay a premium for their purchase. They perceive that functional foods may contribute to easily follow a healthy and balanced diet and reduce the risk of health problems but appeared anxious about the health benefits these foods communicate in their labelling.     

Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis

The COVID-19 pandemic, which started at Wuhan, has shut down world economies, prompting governments to impose drastic lockdown measures of the economy and the society. As these measures are exhausted, non-COVID-19 related issues such as those associated with the mental and physical well-being of people under lockdowns became an emerging concern. As these issues are evident, not to mention the economic downturn, governments are currently looking at designing lockdown relaxation efforts by simultaneously considering both public health and economic restart. Without documented experiences to rely on, governments are resorting to trial-and-error approach in creating a lockdown exit strategy while preventing succeeding waves of cases that may overwhelm healthcare facilities. Thus, this work pioneers the use of the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy (IF) sets along with the domain of public health and the emerging COVID-19 pandemic. The DEMATEL handles the intertwined causal relationships among guideline protocols for the relaxation strategy. The intuitionistic fuzzy set theory addresses the vagueness and uncertainty of human judgments in the context of the DEMATEL. A case study of the Philippine government response for the lockdown exit is presented to evaluate the applicability of the proposed method. Findings reveal that compliance of minimum public health standards, limited movement of persons, suspension of physical classes, the prohibition of mass gatherings, non-operation of category IV industries, and non-operation of hotels or similar establishments are the most crucial protocols for such strategy. These findings offer practical insights for the government to allocate resources and impose measures to ensure their implementation, as well as for developing mitigation efforts to cushion their socio-economic impacts. Policy insights and avenues for future works are also discussed.