Performance Analysis of Flexible Manufacturing Systems with a Single Discrete Material-Handling Device

We present an analytical model for performance prediction of flexible manufacturing systems (FMSs) with a single discrete material-handling device (MHD). This configuration of FMS is significant for many reasons: it is commonly found in industry, it simplifies material-handling control, it is amenable to analytical modeling, and it forms a building block for more complex systems. Standard queueing models are inadequate to analyze this configuration because of the need to take into consideration many nontrivial issues such as state-dependent routing, interference from the MHD, and the analysis of the MHD. To account for state-dependent routing, we develop an iterative method that is built around mean value analysis. To analyze the MHD interference, we use two queueing network models. In the first, we ignore queueing at the MHD but model the interference from the MHD by inflating the station service times. The second network models the queueing for the MHD and estimates the blocking (inflation) times needed for the first model. By iterating between the two networks, we are able to predict the performance of this configuration of FMS. Our analytical estimates are validated against discrete event simulation and shown to be quite accurate for initial system design.     

A model for the minimum cost configuration problem in flexible manufacturing systems

This paper presents a mathematical programming model to help select equipment for a flexible manufacturing system, i.e., the selection of the types and numbers of CNC machines, washing stations, load/unload stations, transportation vehicles, and pallets. The objective is to minimize equipment costs and work-in-process inventory cost, while fulfilling production requirements for an average period. Queueing aspects and part flow interactions are considered with the help of a Jacksonian-type closed queueing network model in order to evaluate the system's performance. Since the related decision problem of our model can be shown to be NP-complete, the proposed solution procedure is based on implicit enumeration. Four bounds are provided, two lower and two upper bounds. A tight lower bound is obtained by linearizing the model through the application of asymptotic bound analysis. Furthermore, asymptotic bound analysis allows the calculation of a lower bound for the number of pallets in the system. The first upper bound is given by the best feasible solution and the second is based on the anti-starshaped form of the throughput function.     

Estimation of Part Waiting Time and Fleet Sizing in AGV Systems

As manufacturing systems have grown in size and complexity, material flow control has become one of the key issues for system efficiency, and determination of the number of vehicles required is an important issue in the design of the AGV (automatic guided vehicle) systems for automated material flow control. In an AGV system, a part issues a delivery request for its transportation, and then an empty vehicle is assigned based on a pre-determined vehicle selection rule and provides delivery service.This research presents a fleet sizing procedure for an AGV system with multiple pickup and delivery stations. A queueing model is used to estimate part waiting times. The fleet sizing procedure estimates the minimum number of vehicles needed to ensure a predefined part waiting time limit. While most stochastic models assume first-come-first-served or random vehicle selection rules for the selection of an empty vehicle, this model considers such additional rules as the nearest vehicle selection rule, which is the most popular among all vehicle selection rules. The performance of the proposed model is examined through computational experiments.     

Queues with Dropping Functions and General Arrival Processes

In a queueing system with the dropping function the arriving customer can be denied service (dropped) with the probability that is a function of the queue length at the time of arrival of this customer. The potential applicability of such mechanism is very wide due to the fact that by choosing the shape of this function one can easily manipulate several performance characteristics of the queueing system. In this paper we carry out analysis of the queueing system with the dropping function and a very general model of arrival process—the model which includes batch arrivals and the interarrival time autocorrelation, and allows for fitting the actual shape of the interarrival time distribution and its moments. For such a system we obtain formulas for the distribution of the queue length and the overall customer loss ratio. The analytical results are accompanied with numerical examples computed for several dropping functions.     

Modeling and design optimization of asynchronous flexible assembly systems with statistical process control and repair

This article presents the implementation of hybrid procedures involving the use of analytical performance evaluation techniques, discrete event simulation, and Monte Carlo optimization methods for the stochastic design optimization of asynchronous flexible assembly systems (AFASs) with statistical process control (SPC) and repair loops. AFASs are extremely complex and difficult to analyze in that such systems are subject to starvation and blocking effects, random jam occurrences at workstations, and splitting and merging of the assembly flow due to repair loops. Hence, an integrated approach simultaneously analyzing the interactions between product quality and optimal/near optimal system design is pursued. In the analytical analysis stage, a model based on GI/G/1 queueing network theory is used. In the Monte Carlo optimization stage, two alternative stochastic optimization approaches, namely, heuristic versions of stochastic quasigradient and simulated annealing algorithms, are implemented and compared in terms of their capabilities of solving complex AFAS design problems. The hybrid procedures presented appear to perform reasonably well in designing AFASs to reach a target production rate.     

Performance Analysis of Auction-Based Distributed Shop-Floor Control Schemes from the Perspective of the Communication System

A growing level of interest in academia and industry centers on the paradigm of distributed shop-floor control in which task and resource allocation in the manufacturing system is accomplished in a distributed manner through message passing and auction-based decision making among autonomous entities. Due to the prominent role played by the communication system in this paradigm, it is important to consider the requirements and performance characteristics of the communication system during the design and evaluation of distributed shop-floor control schemes. In this paper, we propose a two-phase methodology for analyzing auction-based shop-floor control schemes from the perspective of the communication system. In the first phase, the control scheme is modeled as a closed queueing network and performance measures related to the auctioning process and the communication system are obtained rapidly using asymptotic bounding analysis and mean value analysis. Control schemes identified as attractive in the first phase are then evaluated in greater detail during the second phase, using a discrete event simulation model. We illustrate this methodology using two-class and four-class control schemes and discuss insights learned about the impact of various control-scheme-related factors on the performance of the auctioning process and the communication system.     

Apoptosis and mammary gland involution: reviewing the process

Apoptosis is a process of programmed cell death. Mammary gland involution is a tissue remodelling process. Mammary epithelial cell apoptosis is an integral component of tissue remodelling but it is only one element. Equally important are the factors which degrade basement membrane and extracellular matrix. Both operations are required for completion of mammary gland involution. The primary apoptotic process occurs first and is temporally distinct from the second stage of involution typified by lobular-alveolar collapse. Local factors related to milk accumulation trigger the first stage, but loss of systemic hormonal stimulation governs the second stage. Changes in the expression patterns of cell cycle control genes and bcl-2 family member genes are found in the first stage. Proteinase gene activation dominates the second stage. These findings support a two stage model of mammary gland involution. Both mammary epithelial cell apoptosis and mammary gland remodelling advance through a process which includes both loss of survival factors and gain of death factors. This review focuses on signalling pathways and genetic controls which are activated and repressed during mammary gland involution.     

On the nature of errors involved in estimating stage-specific survival rates by Southwood's method for a population with overlapping stages

This paper has examined the effect of within-stage mortality on the estimation of stage-specific survival rates bySouthwood's (1978, p. 358) method. As pointed out bySouthwood , both the severity and timing of mortality affect the mean duration of a life stage, and consequently the estimate of the number of individuals entering that stage. Knowledge of the form of the survivorship curve permits correction of the estimate under certain circumstances. The use ofSouthwood's method with two overlapping stages having different rates and patterns of mortality leads to complex errors in the estimation of survival for the first stage. The nature of these errors is examined analytically and via a simulation model.Southwood's method is fairly robust, with moderate differences in mortality rates leading to acceptable errors in estimating survival for the first stage. When both the rate and pattern of mortality in both life stages are the same, then the survival estimate is made without error. Precise estimates of stage-specific survival will not usually be possible withSouthwood's method because of the errors introduced by the very parameters being measured. Direct measurement of mortality rates and survivorship patterns (seeSouthwood , 1978, p. 309) is strongly advised, at least in preliminary work.     

Variation transmission model for setting acceptance criteria in a multi-staged pharmaceutical manufacturing process

Pharmaceutical manufacturing processes consist of a series of stages (e.g., reaction, workup, isolation) to generate the active pharmaceutical ingredient (API). Outputs at intermediate stages (in-process control) and API need to be controlled within acceptance criteria to assure final drug product quality. In this paper, two methods based on tolerance interval to derive such acceptance criteria will be evaluated. The first method is serial worst case (SWC), an industry risk minimization strategy, wherein input materials and process parameters of a stage are fixed at their worst-case settings to calculate the maximum level expected from the stage. This maximum output then becomes input to the next stage wherein process parameters are again fixed at worst-case setting. The procedure is serially repeated throughout the process until the final stage. The calculated limits using SWC can be artificially high and may not reflect the actual process performance. The second method is the variation transmission (VT) using autoregressive model, wherein variation transmitted up to a stage is estimated by accounting for the recursive structure of the errors at each stage. Computer simulations at varying extent of variation transmission and process stage variability are performed. For the scenarios tested, VT method is demonstrated to better maintain the simulated confidence level and more precisely estimate the true proportion parameter than SWC. Real data examples are also presented that corroborate the findings from the simulation. Overall, VT is recommended for setting acceptance criteria in a multi-staged pharmaceutical manufacturing process.     

Distribution of the Fittest Individuals and the Rate of Muller's Ratchet in a Model with Overlapping Generations

Muller''s ratchet is a paradigmatic model for the accumulation of deleterious mutations in a population of finite size. A click of the ratchet occurs when all individuals with the least number of deleterious mutations are lost irreversibly due to a stochastic fluctuation. In spite of the simplicity of the model, a quantitative understanding of the process remains an open challenge. In contrast to previous works, we here study a Moran model of the ratchet with overlapping generations. Employing an approximation which describes the fittest individuals as one class and the rest as a second class, we obtain closed analytical expressions of the ratchet rate in the rare clicking regime. As a click in this regime is caused by a rare, large fluctuation from a metastable state, we do not resort to a diffusion approximation but apply an approximation scheme which is especially well suited to describe extinction events from metastable states. This method also allows for a derivation of expressions for the quasi-stationary distribution of the fittest class. Additionally, we confirm numerically that the formulation with overlapping generations leads to the same results as the diffusion approximation and the corresponding Wright-Fisher model with non-overlapping generations.     

Validation of a multi-criteria evaluation model for animal welfare

The aim of this paper was to validate an alternative multi-criteria evaluation system to assess animal welfare on farms based on the Welfare Quality® (WQ) project, using an example of welfare assessment of growing pigs. This alternative methodology aimed to be more transparent for stakeholders and more flexible than the methodology proposed by WQ. The WQ assessment protocol for growing pigs was implemented to collect data in different farms in Schleswig-Holstein, Germany. In total, 44 observations were carried out. The aggregation system proposed in the WQ protocol follows a three-step aggregation process. Measures are aggregated into criteria, criteria into principles and principles into an overall assessment. This study focussed on the first two steps of the aggregation. Multi-attribute utility theory (MAUT) was used to produce a value of welfare for each criterion and principle. The utility functions and the aggregation function were constructed in two separated steps. The MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) method was used for utility function determination and the Choquet integral (CI) was used as an aggregation operator. The WQ decision-makers’ preferences were fitted in order to construct the utility functions and to determine the CI parameters. The validation of the MAUT model was divided into two steps, first, the results of the model were compared with the results of the WQ project at criteria and principle level, and second, a sensitivity analysis of our model was carried out to demonstrate the relative importance of welfare measures in the different steps of the multi-criteria aggregation process. Using the MAUT, similar results were obtained to those obtained when applying the WQ protocol aggregation methods, both at criteria and principle level. Thus, this model could be implemented to produce an overall assessment of animal welfare in the context of the WQ protocol for growing pigs. Furthermore, this methodology could also be used as a framework in order to produce an overall assessment of welfare for other livestock species. Two main findings are obtained from the sensitivity analysis, first, a limited number of measures had a strong influence on improving or worsening the level of welfare at criteria level and second, the MAUT model was not very sensitive to an improvement in or a worsening of single welfare measures at principle level. The use of weighted sums and the conversion of disease measures into ordinal scores should be reconsidered.     

A framework for the prediction of scale-up when using compressible chromatographic packings

Experimental data are given for the solid pressure distributions in chromatography columns of various column aspect ratios packed with four types of agarose-based resin. The loss of column wall support at large scales can result in unexpectedly high pressures caused by the compression of the matrix via drag forces exerted by fluid flow through the bed. The need for an accurate model to predict flow conditions at increasing scale is essential for the scaling-up of chromatographic processes and for avoiding bed compression during operation. Several studies have generated correlations that allow for the prediction of column pressure drops, but they either are mathematically complex, which impairs their practical use, or require a large number of experiments to be performed before they can be used. In this study an empirical correlation was developed based on a previously proposed model, which links the critical velocity of operation of a chromatographic system (microcrit), to the gravity-settled bed height (L0), the column diameter (D), the feed viscosity (micro), and the compressibility of the chromatographic media used (micro 10%). The methodology developed in this study is straightforward to use and significantly reduces the burden of preceding laboratory-scale experimentation. The approach can be used to predict the critical velocity of any chromatographic system and will be useful in the development of chromatographic operations and for column sizing.     

Batch sizes and lead-time performance in flexible manufacturing systems

Production lead-time performance in flexible manufacturing systems is influenced by several factors which include: machine groupings, demand rates, machine processing rates, product batching, material handling system capacity, and so on. Hence, control of lead-time performance can be affected through the manipulation of one or more of these variables. In this article, we investigate the potential of batch sizing as a control variable for lead-time performance through the use of a queueing network model. We establish a functional relationship between the two variables, and incorporate the relationship in an optimization model to determine the optimal batch size(s) which minimizes the sum of annual work-in-process inventory and final inventory costs. The nonlinear batch sizing problem which results is solved by discrete optimization via marginal analysis. Results show that batch sizing can be a cheap and effective variable for controlling flexible manufacturing system throughput.     

Predictive modeling of single pass tangential flow filtration for continuous biomanufacturing

Opportunities for process intensification have made continuous biomanufacturing an area of active research. While tangential flow filtration (TFF) is typically employed within the biologics purification train to increase drug substance concentration, single-pass TFF (SPTFF) modifies its format by enabling continuity of this process and achieving a multifold concentration factor through a single-pass over the filtration membranes. In continuous processes feed concentration and flow rate are determined by the preceding unit operations. Therefore, tight control of SPTFF output concentration must be achieved through precise design of the membrane configuration, unlike TFF. However, predictive modeling can be utilized to identify configurations that achieve a desired target concentration across ranges of possible feed conditions with minimal experimental data, hence enabling accelerated process development and design flexibility. We hereby describe the development of a mechanistic model predicting SPTFF performance across a wide design space using the well-established stagnant film model, which we demonstrate is more accurate at higher feed flow rates. The flux excursion dataset was generated within time constraints and with minimal material consumption, showing the method's ability to be quickly adapted. While this approach eliminates characterizing complex physicochemical model variables or the need for users with specialized training, the model and its assumptions become inaccurate at low flow rates, below 25 L/m²/h, and high conversions, above 0.9. As this low flow rate, high conversion operating regime is relevant for continuous biomanufacturing, we explore the assumptions and challenges involved in predicting and modeling SPTFF processes, while suggesting added characterization to gain further process insight.     

吸入麻醉诱导阶段药物动力学模型的定量分析

考虑了给药浓度、氧流量对吸入麻醉诱导阶段的影响,建立了诱导期药物动力学模型.利用非线性拟合的方法给出了诱导阶段的麻醉药血药浓度的拟合曲线;讨论了给药浓度相同的情况下,氧流量对诱导阶段所需时间的影响,得到了诱导阶段的氧流量分别与吸入肺部的药物浓度和体内消除系数的关系式,利用这两个关系式和诱导阶段的一般解可确定在此阶段体内血药浓度和完成诱导的时间.     

Samples from subdivided populations yield biased estimates of effective size that overestimate the rate of loss of genetic variation

Many empirical studies estimating effective population size apply the temporal method that provides an estimate of the variance effective size through the amount of temporal allele frequency change under the assumption that the study population is completely isolated. This assumption is frequently violated, and the magnitude of the resulting bias is generally unknown. We studied how gene flow affects estimates of effective size obtained by the temporal method when sampling from a population system and provide analytical expressions for the expected estimate under an island model of migration. We show that the temporal method tends to systematically underestimate both local and global effective size when populations are connected by gene flow, and the bias is sometimes dramatic. The problem is particularly likely to occur when sampling from a subdivided population where high levels of gene flow obscure identification of subpopulation boundaries. In such situations, sampling in a manner that prevents biased estimates can be difficult. This phenomenon might partially explain the frequently reported unexpectedly low effective population sizes of marine populations that have raised concern regarding the genetic vulnerability of even exceptionally large populations.     

Stochastic models for an enzyme reaction in an open linear system

A stochastic model is developed for an enzyme reaction in an open linear system. The proposed model assumes that the open system maintains the concentration of substrate and inhibitor at constant levels and that the product molecules are removed from the system by a first order reaction. Stochastic models for several enzyme reactions occurring in this open system are shown to correspond to special cases of theGI/M/∞ queue. Takács’ (1958) results for this queueing system are used to obtain the stochastic properties of the enzyme systems. A specific model we studied assumed completely competitive inhibition in an open system. The stationary distribution for the number of product molecules in the system is obtained. The enzyme reaction which incorporated the “intermediate chain hypothesis” can also be investigated by the queueing theory approach. It is shown that for this open system, if the model which incorporated the intermediate chain hypothesis has the same deterministic properties as the Michaelis-Menten model, then the latter has greater stochastic variation than the former. Research supported by the NIH Training Grant No. GM 1237-05 awarded to the Department of Statistics, The Johns Hopkins University. This paper in whole or in part may be reproduced for any purpose of the United States Government.     

Leveraging single‐pass tangential flow filtration to enable decoupling of upstream and downstream monoclonal antibody processing

Decoupling upstream and downstream operations in biopharmaceutical production could enable more flexible manufacturing operations and could allow companies to leverage strategic or financial benefits that would be otherwise unattainable. A decoupling process was developed and scaled up utilizing single‐pass tangential flow filtration for volume reduction, followed by bulk freezing in single‐use bags prior to purification. Single‐pass tangential flow filtration can be used to continuously concentrate harvested cell culture fluid, reducing the volume by 15‐25× with a step yield of >96%. These concentration factors were reproduced with a second product, indicating that the process could be amenable to platform processes. Experimental data indicate that the product tested was stable for at least one year at ?40 or ?70°C. The concentration of the harvested cell culture fluid—either with or without a subsequent period of frozen storage—had no impact on the product quality attributes that were tested. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:405–411, 2018     

产业生态系统资源代谢分析方法

产业生态系统是由企业群、资源及环境组成的社会-经济-环境复合生态系统。资源代谢是其功能运行的重要保障。资源代谢在时间和空间尺度上的耗竭及阻滞是造成严重生态环境问题的主要原因。根据生态学原理,运用物质流分析手段解析了产业生态系统的物质流、能流及资金流结构,构建了产业生态系统资源代谢分析模型,提出了资源输入-使用-输出-循环共生四方面的资源代谢分析指标体系和基于模糊综合分析的资源代谢问题树分析方法。在此基础上提出了基于循环共生网络结构模型的生态管理模式。以期为产业资源的生态管理提供方法支撑。     

Modeling of purification operations in biotechnology: enabling process development, optimization, and scale-up

Process modeling involves the use of a set of mathematical equations to represent key physical phenomena involved in the process. An appropriately validated model can be used to predict process behavior with limited experimental data, identify critical ranges for process variables, and guide further process development. Although process modeling is extensively used in the chemical process industries, it has not been widely used in purification unit operations in biotechnology. Recent FDA guidelines encourage the use of process modeling during process development, along with multivariate statistical methods, detailed risk assessment, and other quantifiers of uncertainty. This paper will review recent advances in the modeling of key downstream unit operations: chromatography, filtration, and centrifugation. The focus will be on the application of modeling for industrial applications. Relevant papers presented at a session on this topic at the recent American Chemical Society National Meeting in San Francisco will also be reviewed.