烟草商业系统物流线路优化研究与应用

以优化福建漳州烟草物流公司配送区域线路为目的,结合烟草物流人工经验配送线路的具体情况,研究并提出了一种改进的柔性线路截取优化算法。该算法在硬性以及柔性线路截取算法的基础上,对柔性线路截取进行了改进,打破以往行政区域以及配送区域的概念,建立了“以量定车,以户定线,户量均衡,动态优化”的弹性送货新模式,从而实现车辆利用率最大化以及配送里程最小化目标。漳州烟草物流公司5年的实际应用表明,在周配送户数从25000户增加到30000户、卷烟销量从19万多箱增加到24万多箱的情况下,送货线路却从79条减少到62条,在企业经营降本增效上取得了良好的成效。      

卷烟商零动态线路优化和在途监控的研究及实现

本文通过研究烟草商业地市公司配送现状,分析其中存在的问题,在深入学习国家烟草局规范和标准基础上,采用GPS、GIS、GPRS等技术,借助禁忌搜索聚类算法、导航技术、基于启发式算法获得VPR问题实际上可操作的满意解、统计方法等设计,建立一套完整的智能化卷烟商零在途动态监管系统,大幅度提高了烟草商业配送运输效率,有效的降低了配送成本。      

基于机器学习的成品卷烟销售订单配送调度优化算法研究与应用

为解决卷烟工业企业成品卷烟销售订单配送调度过程中存在的工作效率低、管理不科学、人工作业量大等问题,基于机器学习设计了一种智能配送调度算法。首先将待调度的成品卷烟销售订单进行凝聚聚类,然后利用专家策略函数和机器策略函数构建搜索树,再通过构造调度优化评价函数进行配送调度决策。以红云红河烟草(集团)有限责任公司2020年4月部分成品卷烟销售订单为例,对精确算法、遗传算法以及机器学习算法进行对比测试。结果表明:与精确算法和遗传算法相比较,机器学习算法具有更好决策性能及全局收敛性,在相同待调度订单数据前提下,使用车辆数分别减少13.28%和10.14%,运输里程减少5%和7.2%,时间成本减少68%和45%,多点组合车次数分别增加11.75和8.07百分点。该方法可为提高成品卷烟销售订单配送调度效率提供支持。     

一种新的线搜索下CD型共轭梯度法的全局收敛性

根据一种具有可行性的线搜索条件,结合CD型共轭梯度法的计算公式,给出了一种无约束优化问题的算法,证明了这种线搜索可保证CD方法下降搜索方向的充分下降性,也提供了算法的全局收敛性结果及证明过程．     

基于聚类分析的烟草物流配送线路优化算法的研究与应用

为快速、高效地完成烟草配送过程,在兼顾地区特点的情况下减少配送成本和时间,以人工智能、运筹学等方法为理论依据,结合聚类分析算法对广西烟草物流配送过程进行了优化设计。基于聚类分析给出了优化算法模型及步骤,对烟草物流配送线路进行了中转站的区域规划、周期配送区域(片区时间区域)规划和线路优化。应用效果表明,优化后中转站数量由10个减少为7个,总配送路线减少30%,配送节点更集中,节省了配送费用和时间,提高了烟草物流的运营效率。     

卷烟配送中心选址优化研究与应用

为提高卷烟物流配送效率，节约配送里程和时间，合理选择配送中心修建数量及地址，从而整体降低配送成本，利用多视角聚类算法结合节点重要度排序理论，构建了卷烟物流配送中心选址优化模型。针对客户点之间不同类型的连接关系，构造双层特征网络，使用共识的配送区域划分矩阵来提取不同性质数据矩阵的共同聚类划分，再用不同的低维系数矩阵表示不同性质数据的异质性，利用KKT原则和乘法更新原则进行矩阵更新迭代，使用hard-cluster方法进行聚类，实现配送区域划分，同时，引入非负矩阵分解的稳定性误差用于选取最优的配送区域个数划分方案；最后使用介数中心性原理，实现基于节点重要度的配送中心选址，以实现配送区域划分的均匀性，降低物流成本，提升物流效率。在云南省保山市进行仿真实验，结果表明:（1）利用本文方法实现了卷烟物流配送中心的选址和对应配送区域的划分，经过优化后，配送总里程减少了6089.097 km，等同于配送时间减少了165.6 h，所有配送中心对应的服务区域较均衡，不存在长距离的跨配送中心送货存在；（2）该地区卷烟销量及配送需求与聚类划分结果基本一致。（3）优化后的单位卷烟物流配送成本显著降低，配送效率也得到了较大程度的提升。因此，使用该方法来解决卷烟配送中心选址和配送区域优化问题是有效且可行的。      

跨区域多配送中心车辆调度智能优化研究

跨区域多配送中心车辆调度是一个典型的NP难题,是当前运筹学领域的一个研究热点。本文采用边界分配法将该问题转化为单配送中心车辆调度问题,并结合遗传算法与蚁群算法求解跨区域配送最优调度方案。以贵州省黔东南州烟草公司物流中心卷烟配送为背景进行了仿真分析,结果表明了算法的有效性。      

突出服务 注重效率 全面提升卷烟销售网络运行质量和水平 周志明副总经理在2006年全区卷烟销售网络建设工作会上的讲话(摘要)

一、全国卷烟销售网络建设现场会召开情况(略)二、全区网建整体推进工作成效显著全区网建整体推进工作成效明显。完成了业务流程再造,推进了“三个集中”的实施,基本实现了“四个提升”和“五个统一”,实现了队伍转型及由传统商业向现代流通的转变。截止05年底,全区共有物流中心14个,卷烟配送中转站(含二级分拣库)60个,配送线路2482条,配送人员788人,客户经理829名,电话订货员270名,配送车辆568台。全区有持证零售客户167286户,实行电话订货的零售客户160943户,电话订货率达到96.21%,实行电子结算的客户85124户,电子结算率为50.89%。其中城…     

基于近代优化算法的生鲜团购配送最优路线模型

该项目以某优选的生鲜团购配送为例,在考虑各项约束的基础上,以生鲜配送中心点到各个小区的总配送路程最小为宗旨,利用回溯法选优搜索出生鲜配送的中心小区,配送中心选址可有效地降低社区团购的运营成本及物流度等等。然后就小区的生鲜团购配送的路线方面,结合实际,采用模拟退火算法建立模型,求解出生鲜配送的最优路径。优化后的路线能够提高配送效率,节省配送费用,节约人力、物力、财力等资源。     

公共卫生事件下烟草工商企业卷烟运输配送环节疫情防控压力模型研究

突发公共卫生事件时，卷烟运输配送环节因其范围广、交互性强等作业特性，成为疫情防控的关键部位。烟草工、商企业卷烟运输配送业务覆盖全国三百余个城市、五百余万零售客户，企业卷烟运输配送环节疾控压力迅速增加。本文以风险管理理论和RBS方法为基础，依据全国新型冠状病毒肺炎（COVID-19）疫情报告数据和烟草行业经济运行数据，从疫情发生的可能性、严重性和烟草行业敏感性等三个维度构建具有行业特征的城市疫情风险分级三维指标体系，并结合烟草工商企业卷烟运输配送作业数据，设计该环节疫情输入风险指标，计算企业所面临的疫情防控压力指数，从而建立公共卫生事件下烟草工商企业卷烟运输配送环节疫情防控压力模型，为烟草企业科学评估疫情防控压力、合理划分风险等级、有效制定分级分类防控策略提供依据。      

群体智能和智能优化算法(英文)

通常元启发式优化算法是基于群体智能的算法,这些算法通常被称为智能算法.此文综述一些广泛应用的优化算法,包括蚁群算法、蜜蜂算法、蝙蝠算法、布谷鸟搜索、萤火虫算法和粒子群优化.同时,还讨论启发式算法中具有挑战性的问题如参数调整和参数控制.     

Advances in Feature Selection Methods for Hyperspectral Image Processing in Food Industry Applications: A Review

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.     

网络爬行技术研究

阐述了基于整个W eb的爬行器、增量式的爬行器、基于主题的爬行器等不同类型网络爬行器的功能及优缺点;分析了近年来国内外网络爬行器的遍历算法,包括深度优先算法、广度优先算法、主题优先算法等.分析结果表明:基于遗传算法的爬行策略能够有效地加快抓取网页的速度和扩大搜索范围.     

Comparison of Optimization Techniques for Application to Food Product and Process Development

The super-simplex optimization was modified by incorporating a quadratic regression subroutine. The new algorithm was found to be more efficient in converging at the optimum than the following algorithms: fractional factorial designs, sequential single-factor search, pattern-search method, Morgan-Deming simplex and super-simplex. The efficiency of the modified super-simplex optimization was dependent on the‘fitness of the regression equation to the model equation. Response surface analysis was accurate only when the model equation was quadratic factorial and the boundary covered the optimum. When a constraint was imposed to restrict the search within the boundary, unlike other simplex algorithms including the weighted centroid method, the modified super-simplex algorithm circumvented a problem of the search stalling at the boundary. Since it is not unusual for optimization to be restricted within boundaries, the modified super-simplex algorithm could be useful in optimizing food processing and analysis. When the modified super-simplex technique is applied to -food research, even higher optimization efficiency could be expected by incorporating backward stepwise multiple regression analysis instead of the quadratic regression subroutine with regression equations of set formulae. To obtain a regression equation fit better to the true response surface, all information, including graphics, should be utilized.     

利用遗传算法模拟进化优化的仿真实验

遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｓ，简称ＧＡ）是一种基于自然选择原理和自然遗传机制的新颖搜索算法。它模拟自然界中的生命进化机理，在复杂系统中，能够迅速实现特定目标的全局寻优。本文提供一种基于遗传算法的函数寻优实验程序，从中可以形象地了解遗传算法用于计算机求解问题的各种基本策略及效果，体会到在复杂系统的设计中，遗传算法较许多传统搜索算法更有效的原因。     

自适应伪并行遗传算法在织机张力控制中的应用

为了克服基本遗传算法局部搜索能力差和存在未成熟收敛等问题,提出了自适应伪并行遗传算法,并用于PID参数优化;提出了采用遗传算法进行PID参数优化的方法,并具体为以ZA209i型织机张力控制系统PID参数的优化为例进行了仿真计算;为织机张力控制提供了新的方法.     

Incorporation of Uncertainty in Composition of Feeds into Least-Cost Ration Models. 1. Single-Chance Constrained Programming

Ration formulation by linear programming techniques assumes a perfect knowledge of all coefficients involved. Because of variation in composition of feedstuffs, this assumption cannot hold in practical situations. However, the problem can be reformulated as a chance-constrained programming model for which a deterministic equivalent can be derived. In the case where only one nutrient is considered random, the methodology is labeled as single-chance constrained programming. The exact deterministic equivalent requires nonlinear programming methods for which algorithms are not available to the feed industry. Three linear approximation algorithms were derived and compared with the exact solution given by nonlinear programming in the formulation of a premix feed. Prices from 1970 to 1979 for 17 feed ingredients were used. An algorithm based on a linear function of the standard deviation of the random nutrient within each feedstuff, for which a penalty parameter is iterated in a search for a desired probability, yielded results very similar to the exact solutions found by nonlinear programming methods. This methodology could be implemented easily by the feed industry.     

基于嗅觉可视化技术的眉茶等级分类方法

为了快速、准确地对眉茶等级进行分类,提出了一种基于嗅觉可视化技术的眉茶等级快速分类方法。首先,根据卟啉显色反应预实验结果,选定了12 种显色效果明显的卟啉指示剂制备嗅觉可视化传感器阵列,通过该传感器阵列与不同等级的眉茶茶汤进行反应,获取不同的特征图像。然后,对特征图像数据进行主成分分析和降维,将得到的不同维数的主成分分析结果作为输入变量,构建支持向量机（support vector machine,SVM）眉茶等级分类模型。最后,引入3 种群体智能优化算法（萤火虫算法、灰狼优化算法、布谷鸟算法）对SVM分类模型的惩罚因子c和核函数参数g进行优化。结果显示：未经优化的SVM分类模型对测试集的分类正确率为80%,所需的主成分个数为12 个;经过优化的SVM模型的分类正确率均有所提升,其中经过布谷鸟算法优化的SVM模型对测试集的分类正确率达到了93.3%,且所需的主成分个数减少为6 个。这表明应用嗅觉可视化技术能够实现对眉茶等级的分类,而通过群体智能优化算法优化SVM分类模型可以显著增强模型的性能,提高分类正确率。     

Optimization of the Viability of Probiotics in a New Fermented Milk Drink by the Genetic Algorithms for Response Surface Modeling

ABSTRACT: Calcium gluconate (0.0 to 0.5%), sodium gluconate (0.0 to 1.0%), and N-acetylglucosamine (0.0 to 1.0%) were added to skim milk to retain the viability of Lactobacillus acidophilus and Bifidobacterium longum. To carry out response surface modeling, the regression method was performed on experimental results to build mathematical models. The models were then formulated as an objective function in an optimization problem that was consequently optimized using a genetic algorithm approach to obtain the maximum viability of the probiotics. The genetic algorithms (GAs) were examined to search for the optimal value. The results indicated that GAs were very effective for optimizing the activity of probiotic cultures.     

Developing a Textile Design Recommendation System According to Consumers' Sensibilities

Abstract

Analysis of customers' sensibilities and preferences is an important strategy in a market that is becoming increasingly more customer oriented. The authors propose the Textile Design Recommendation System (TDRS) as an effective tool for focusing on customers' needs in the textile industry. The authors have developed a user interface tool that can suggest textile designs according to the users' needs. Collaborative filtering was adopted to search a textile design, and the performance of TDRS was tested according to three algorithms. This system may allow textile merchandisers to meet a consumer's needs more exactly and easily.