基于Flexsim的某医药物流中心拣货流程优化研究

目的将Flexsim仿真软件应用于医药物流行业,以提高医药物流中心零货库拣货流程的运作效率。方法以某大型医药物流中心为研究对象,根据该零货库区的布局,使用Flexsim仿真软件进行建模仿真,并根据订单拣选作业流程运用Flexsim脚本语言和C语言进行了编程。分析模型运行结果,找出了瓶颈,并对拣货流程进行了优化。结果优化后的流程,提高了拣货作业的效率,缩短了拣货时间。结论采用建模仿真的方法,找出拣货流程的瓶颈问题,并进行合理优化,可以提高医药物流中心零货库的拣货效率。     

基于Flexsim对某医药物流中心立体仓库优化

目的 利用Flexsim软件仿真医药物流中心立体仓库的运作,缩短立体仓库出入库作业时间,优化运作过程人员与设备的组合。方法 以某大型医药物流中心为研究对象,根据实际立体仓库的布局,使用Flexsim仿真软件进行建模仿真时,首先采用遗传算法确定最优的出入库顺序,将结果在Flexsim中仿真运行,证明算法用于出入库仿真的实用性;然后对立体仓库整体流程进行仿真。结果 优化后的流程,出入库作业效率提高了12.4%,确立了仓储作业在(2,2,2,2)配置下,实现了人员设备的最优利用。结论 采用建模仿真的方法,找出了流程中的瓶颈,确认了最优出入库作业顺序,以及人员、设备组合,提高了医药物流中心的出入库效率,降低了设备、人员等成本。     

物流中心选址因素分析及方案评价方法

现代物流是现代化生产的重要组成部分,是企业的第三利润源泉,而物流中心的选址则对企业有极大的影响。影响物流中心选址的因素有:社会影响因素、经济效益影响因素、技术效益影响因素,这些因素对物流中心的选址提出一套简单的流程,结合主观和客观两方面用重心法、集思广益法和得菲尔法得到了三种选址备选方案,然后根据影响物流中心选址的因素用层次分析法建立了对备选方案综合分析的层次结构模型,计算出每个备选方案的相对权重,权重最大者即为最优的选址方案。结果表明,此物流中心选址方法及流程简单方便,结果正确,可用于今后的物流中心选址。     

兼顾成本与效率的物流中心设施布置优化设计

分析了常见物流中心设施布置设计方法的不足,针对物流中心这种典型的"类生产"型服务型企业,提出了一种有效兼顾物流成本与作业效率的物流中心设施布置优化设计方法,并在遗传算法求解过程中,提出了基于动态布置技术的布置方案初始种群产生方法。讨论了三种情况的布置方案,以进行分析对比。其中情况1,以作业区之间物流成本最低为单一目标;情况2,以最能影响作业效率的作业区之间的关联关系密切度最大为单一目标;情况3,兼顾物流成本与作业效率。算例分析表明:提出方法是合理有效的,情况3方案的物流成本只比情况1增大了7.5%,关联密切度只比情况2减小了7.8%,说明情况3方案有效地兼顾了物流成本与作业效率;动态布置技术有助于得到比较规则整齐的布置方案,增强了方案布置的合理性和灵活性。     

医药物流中心人工拣选作业流程优化及仿真

费用高、利润低已经成为制约我国医药物流行业发展的主要瓶颈。因此,研究降低医药物流中心人工拣选占有成本的方法对企业赢利具有重要价值。在对物流中心拣选作业流程及其影响因素分析的基础上,建立了基础模型,分别对巷道数不同拣选条目数相同及巷道数不同拣选条目数不同两种情形采用MATLAB进行了仿真,探讨了分区数与拣选条数之间的关系,并对拣选路径进行了分析,发现：1) 分区优于不分区,一般情况下,分区以2个为宜;2) 当分区超过2个后,拣选时间会逐渐增加;3) 在巷道数和拣选条目一定的情况下,分区越多越好;4) 在巷道超过96时,可以考虑分成3个拆零区。     

基于智慧物流的高校快递包装回收流程再造与优化研究

目的 通过对高校快递包装回收的流程进行再造和优化,以提高高校快递包装回收水平。方法 对现有流程进行分析,发现存在的问题,并提出再造思路和实施办法。结果 新流程可以有效节约资源,提高高校快递包装回收效率。结论 适应智慧物流发展趋势,高校快递包装回收流程再造和优化势在必行。     

下一站，医药物流!——中国医药物流面面观

随着GMP的尘埃落定，关于制药业的新闻逐渐从生产向销售和物流转移。据权威部门预测，2010年中国的医药市场价值将达到600亿美元，2020年将达到1200亿美元，从而将超过美国成为全球第一大市场。一方面，药品的出厂价不断降低，厂商叫苦不迭；一方面，老百姓对药品价格频频置疑，人人喊打。对于流通环节的改革之声似乎预示着医药物流业的春天到了。于是乎，这边医药物流轰轰烈烈地讨论着，那边物流中心风风     

面向现代物流的饮品包装数字化设计及优化研究

目的传统包装设计流程对于产品包装与物流环节的匹配性关注较少,造成物流浪费。为了解决这一问题,需要对传统包装设计流程进行改进。方法在分析产品包装生命周期内周转环节的基础上,采用逆向分析物流环节的方法,利用TOPS Pro软件进行模数分割以确定产品包装最大外尺寸系列;利用Pro/E绘图功能进行局部造型设计和结构设计,并利用行为建模方法进行容积敏感度分析;对造型进行优化,并确定容积及灌装高度。结果优化后的包装设计流程,采用模数分割、行为建模、敏感度分析等数字化设计手段,可以大大简化设计流程,同时设计结果对降低物流成本有较大帮助。结论优化后的设计流程在物流环节与产品包装的参数之间建立了合理联系,使设计结果更为可信。同时,减少了传统设计流程中设计的盲目性,提高了设计效率。     

基于模型的区域物流配送系统规划研究

以经济区域的卷烟配送物流系统规划为背景,将整个区域划分为若干个配送单元,依据各配送单元的需求量、物流中心的建设投入和运行成本,并将物流中心的成本分为固定成本和变动成本,特别地将送货过程的车辆费用从变动成本中划分出来,建立了物流系统的运筹学模型,并通过模型求解获得该物流系统的优化布局方案.     

4A级物流中心落户烟台高新区

<正>26日上午,国家4A级现代物流中心落成典礼在高新区举行,作为医药领域全省首家现代物流中心,将汇集万余种药品烟台中转。据介绍,物流中心投资近亿元,一期工程拥有仓储面积18000平米     

Profit-oriented shift scheduling of inbound contact centers with skills-based routing, impatient customers, and retrials

This paper presents a profit-oriented shift scheduling approach for inbound contact centers. The focus is on systems in which multiple agent classes with different qualifications serve multiple customer classes with different needs. We assume that customers are impatient, abandon if they have to wait, and that they may retry. A discrete-time modeling approach is used to capture the dynamics of the system due to time-dependent arrival rates. Staffing levels and shift schedules are simultaneously optimized over a set of different approximate realizations of the underlying stochastic processes to consider the randomness of the system. The numerical results indicate that the presented approach works best for medium-sized and large contact centers with skills-based routing of customers for which stochastic queueing models are rarely applicable.     

板材剪切配送企业的物料需求模型与算法研究

概述了传统的MRP计算和板材剪切配送企业MRP计算的不同之处,提出板材剪切配送企业物料需求计算存在的问题;针对这些问题,借鉴制造行业物料需求计划的一般原理并结合板材剪切配送企业行业的特点,给出板材剪切配送企业物料需求模型,提出一种符合板材剪切配送企业特点的、有效的物料需求计算方法．     

The role of artificial intelligence in business transformation: A case of pharmaceutical companies

Artificial intelligence (AI) is of great interest to researchers and practitioners as a means of achieving the necessary progress in the pharmaceutical industry. However, the role of AI and ways of transforming companies are not well studied. The purpose of the paper is to identify exactly how AI affects the key and support business processes of pharmaceutical companies. We offer a qualitative interview study of five large, five medium, and five small pharmaceutical companies. Based on scarce literature on the role of AI in the pharmaceutical industry, we considered which business processes are subject to transform within it and how they do so. We determine that small pharma companies significantly change research and development, master data management, analysis and reporting, and human resource business processes under the influence of AI. Large pharma companies use AI to transform production, sales, marketing, and analysis business processes. In turn, medium-sized companies are in the middle and individually transform their business processes depending on their specialization.     

Optimization of image processing parameters for large sets of in-process video microscopy images acquired from batch crystallization processes: Integration of uniform design and simplex search

A narrow particle size distribution with desired particle shape usually characterizes the expected product quality for pharmaceutical crystallization processes. Real-time estimation of particle size and shape from in-process video images is emerging as a new process analytical technology (PAT) tool for crystallization process monitoring and control. Any image processing algorithm involves a number of user-defined parameters and, typically, optimal values for these parameters are manually selected. Manual selection of optimal image processing parameters may become complex, time-consuming and unfeasible when there are a large number of images and particularly if these images are of varying qualities, as could happen in batch crystallization processes. This paper combines two optimization approaches to systematically locate optimal sets of image processing parameters — one approach is a model-based optimization method in conjunction with uniform experimental design; another approach is the Sequential Simplex Optimization method. Our study shows that these two approaches or a combination of them can successfully locate the optimal sets of parameters and the image processing results obtained with these parameters are better than those obtained via manual tuning. Combination of these two approaches also helps to overcome the drawbacks of each individual method. Our work also demonstrates that the optimal sets of parameters obtained from one batch of process images can also be successfully applied to another batch of process images that are obtained from the same system. The in-process video microscopy (PVM) images that are acquired from Monosodium Glutamate (MSG) seeded cooling crystallization process are used to demonstrate the workability of the proposed approach.     

Image vector histogram approach to nanoparticle sizing

A novel approach to measuring the size distribution of particles in the range of a few nanometers to a few micrometers is described. The method is based on processing multiple images of a sample of particles suspended in a liquid and undergoing Brownian motion. From each image, the centers of the particle positions are measured, then a histogram of the vectors connecting the centers in each image with all the centers in the next image is formed. This vector histogram contains information about the particle size distribution. A maximum-likelihood data inversion procedure to invert the data to yield a particle size distribution is described. Both computer simulation and experimental results are presented to demonstrate the effectiveness of the approach.     

Is 3D Printing of Pharmaceuticals a Disruptor or Enabler?

Additive manufacturing (AM) is a fast‐growing manufacturing approach that comes with the promise of delivering personalized medicine to treat individual patients. However, large‐scale commercial applications in the pharmaceutical industry have been limited. Here, some of the challenges are discussed along with some pharmaceutical products where AM has the potential to make a tangible impact and pave the way for more rapid adoption are highlighted.     

Emitting a-SiO(x)(Er) films and a-SiO(x)(Er)/a-Si:H microcavities doped with Er by remote magnetron sputtering technique

We have developed the technique of growing amorphous a-SiO(x)(Er) films and a-SiO(x)(Er)/a-Si:H multilayer structures based on spatially separating the processes of the decomposition of an oxygen-silane gas mixture in an rf glow discharge plasma and remote magnetron sputtering of an Er target. This approach allows us to control independently the film deposition rate, the Er-ion concentration and its depth distribution in the film. Time-resolved photoluminescence measurements have shown that films and planar microcavities with an Er-doped active layer exhibit internal quantum efficiency for Er ion emission of ～75%. The method that we suggest is a way of producing effectively emitting microcavity structures, in which the distribution profile of emission centers coincides with that of the electromagnetic field in individual layers of the structure.     

Characterization of chloramphenicol palmitate drug polymorphs by Raman mapping with multivariate image segmentation using a spatial directed agglomeration clustering method

Chemical imaging analysis holds great potential in probing the chemical heterogeneity of samples with high spatial resolution and molecular specificity. This paper demonstrates the implementation of Raman mapping for microscopic characterization of tablets containing chloramphenicol palmitate polymorphs with the aid of a new multivariate image segmentation approach based on spatial directed agglomeration clustering. This approach performs the agglomeration clustering by stepwise merging the pixels possessing both spatial closeness and spectral similarity into clusters that define the image segmentation. The incorporation of spatial closeness into the clustering process enables the approach to improve the robustness and avoid poorly defined image segmentation arising from clusters with highly separated pixels. Additionally, the stepwise merging of clusters offers an F-statistic-based procedure to automatically ascertain the number of image segments. Raman mapping analysis of tablets containing two polymorphs of chloramphenicol palmitate followed by multivariate image segmentation reveals that the proposed technique offers the identification of each polymorph and a quantitative visualization of the spatial distribution of the polymorphs identified. This technique holds promise in rapid, noninvasive, and quantitative polymorph analysis for pharmaceutical production processes.     

Population balance modeling applied to the milling of pharmaceutical extrudate for use in scale-up

This study modeled the particle size distribution (PSD) of pharmaceutical extrudates after milling by developing a so-called time-discrete population balance model (PBM). The PBM, which models size reduction as a series of breakage events, was formulated so that the model parameters separate the effect of material properties and milling process conditions. Because of this novel aspect, the PBM should have excellent predictive capability with specific applications in technology transfer and scale-up. To investigate this application, copovidone extrudate produced by the hot-melt extrusion process was milled using a lab-scale continuous impact mill (Fitz Mill). The effect of impeller speed and classification screen size on PSD of the extrudate was investigated. The PBM with parameters obtained by fitting lab-scale PSD data was then applied to model the PSD of the extrudate following milling by a pilot-scale continuous impact mill (Hosokawa mill). The study found that the parameters determined at the lab-scale can be used to model PSDs at the pilot-scale and may be generally applied to similar classification-type impact mills. Since technology transfer and scale-up can be material and time consuming, this approach may offer significant benefits to the pharmaceutical industry for the development of milling processes.     

A transient distribution of particle assemblies at the concluding stage of phase transformations

There are a number of experimental and theoretical papers testifying that the large-time behavior of the particle-size distribution function heavily depends on the initial conditions at the final stages of phase transformation processes. However, still now there is no theoretically derived distribution confirming these conclusions. The present paper is concerned with a new theoretical approach verifying the fact that the large-time distribution can actually be dependent on the details of the initial data. The concluding stage of evolution of a particulate ensemble as a result of coalescence and coagulation processes is considered. The transient kinetic and balance equations for the particle-size distribution function are modified into a single nonlinear equation for arbitrary collision frequency factors. This integro-differential equation is solved analytically in the limit of large times. The obtained particle-size distribution function depends on the initial condition and describes the concluding stages of transient phase transformation processes. The area of applicability of the present asymptotic solution for the large-time particle-size distribution is discussed. The obtained distribution function is in agreement with experiments.