On modeling the design of cellular manufacturing systems

A new optimization model is discussed for the design of cellular manufacturing systems. It is based on an integer programming formulation that updates some other models by eliminating redundant machine assignment and cost coefficients dependent on cell configuration. To reduce computational burdens, a simplified integer programming model and a decomposition algorithm are proposed. Several computer solutions were performed to evaluate the performance of the new model. The computational results are discussed.     

Time-varying optimal design for groundwater bioremediation: The pilot-scale study of a western Canadian site

This paper presents the development of an integrated simulation-clustering-optimization (ISCO) approach for supporting the groundwater bioremediation design. To investigate remediation performances, a subsurface model was employed to simulate contaminant transport. A mixed integer nonlinear optimization model was formulated to evaluate different remediation strategies. Multivariate relationships based on stepwise clustering analysis were developed to facilitate the incorporation of a simulation model within a nonlinear optimization framework. Based on the developed statistical relationships, a prediction system was constructed to provide all possible outputs for the proposed remediation design. By using the prediction system, the optimal strategy to achieve the remediation objective can be directly located. The proposed ISCO approach can improve bioremediation designs by providing adjustable schedules for operating wells from stage to stage, which will make the optimization more realistic. This approach was examined through the pilot-scale study of a western Canadian site. The optimization results demonstrated that 31% of the cost can be reduced by using a multi-stage design to achieve the same remediation objective as that of a single-stage design.     

Optimal operating mode for enantioseparation of SB-553261 racemate based on simulated moving bed technology

The performance of the simulated moving bed (SMB) technology and its modification, the Varicol process, was optimized using an experimentally verified model for the enantioseparation of SB-553261 racemate. Single and multiobjective optimizations have been carried out for both existing as well as design stage and their efficiencies were compared. The optimization problem involves a relatively large number of decision variables, both continuous variables such as flow rates, switching time and length of the columns, as well as discrete variables like number and distribution of columns. A state-of-the-art new optimization technique based on a genetic algorithm (nondominated sorting genetic algorithm with jumping genes) was utilized which allows handling of these complex optimization problems. The optimization results showed that significant improvement could be made to the chiral drug separation process using both the SMB and the Varicol process. It was found that the performance of a Varicol process is superior to that of a SMB process in terms of treating more feed using less desorbent or increasing productivity while at the same time achieving better product quality. Optimum results were explained using equilibrium theory by locating them in the pure separation region.     

CFD-aided cell settler design optimization and scale-up: effect of geometric design and operational variables on separation performance

The inclined multiplate (lamella) gravity settler has proven to be an effective cell retention device in industrial perfusion cell culture applications. Investigations on the effects of geometric design and operational variables of the cell settler are crucial to understanding how to best improve the settler performance. Maximizing the harvest/perfusion flow rate while minimizing viable cell loss out of the harvest is the primary challenge for optimization of the settler design. This study demonstrated that computational fluid dynamics (CFD) can be utilized to accurately model and evaluate the settler separation performance for near-monodisperse suspensions and therefore aid in the design optimization of the settler under these baseline conditions. With the preferred geometric features that were identified from CFD modeling results, we proposed design guidelines for the scale-up of these multiplate settler systems. With these guidelines and performance verification using the CFD model, a new large-scale settler was designed and fabricated for a perfusion cell culture process using a minimally aggregating production cell line. Perfusion cell culture runs with this particular cell line were performed with this settler, and the CFD model was able to predict the initial ramp-up performance, proving it to be a valuable scale-up design tool for this production process.     

Cost-intelligent application-specific data layout optimization for parallel file systems

Parallel file systems have been developed in recent years to ease the I/O bottleneck of high-end computing system. These advanced file systems offer several data layout strategies in order to meet the performance goals of specific I/O workloads. However, while a layout policy may perform well on some I/O workload, it may not perform as well for another. Peak I/O performance is rarely achieved due to the complex data access patterns. Data access is application dependent. In this study, a cost-intelligent data access strategy based on the application-specific optimization principle is proposed. This strategy improves the I/O performance of parallel file systems. We first present examples to illustrate the difference of performance under different data layouts. By developing a cost model which estimates the completion time of data accesses in various data layouts, the layout can better match the application. Static layout optimization can be used for applications with dominant data access patterns, and dynamic layout selection with hybrid replications can be used for applications with complex I/O patterns. Theoretical analysis and experimental testing have been conducted to verify the proposed cost-intelligent layout approach. Analytical and experimental results show that the proposed cost model is effective and the application-specific data layout approach can provide up to a 74% performance improvement for data-intensive applications.     

Inference of Vohradsky's Models of Genetic Networks by Solving Two-Dimensional Function Optimization Problems

The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský''s model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský''s model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský''s models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.     

Flexibility analysis of a surface mount technology electronic assembly plant: An integrated model using simulation

This paper focuses on developing an integrated model using simulation to evaluate the effect of several independent variables on the performance of a surface mount technology (SMT) production line. Real data and an existing SMT line from a high product mix/low volume electronics manufacturer are used to conduct the analysis. The independent variables used are set-up formation policies (group technology based family grouping methods), machine feeder types, similarity factor in set-up formation, parts reduction at design step of products, and inter-families and intra-family scheduling rules. In addition, a new method of grouping products is proposed. The measures of performance evaluated by the model are average lead time, average work-in-process (WIP) inventory and average set-up time. Data analysis shows that the proposed method of grouping products will reduce set-up time and lead time while slightly increasing WIP. The proposed simulation model helps assess the effects of some of the independent variables on line performance. Recommendations are made in order to help the user choose the best alternative to improve production line productivity and flexibility.     

Potential of the pseudo-inverse method as a constrained static optimization for musculo-tendon forces prediction

Inverse dynamics combined with a constrained static optimization analysis has often been proposed to solve the muscular redundancy problem. Typically, the optimization problem consists in a cost function to be minimized and some equality and inequality constraints to be fulfilled. Penalty-based and Lagrange multipliers methods are common optimization methods for the equality constraints management. More recently, the pseudo-inverse method has been introduced in the field of biomechanics. The purpose of this paper is to evaluate the ability and the efficiency of this new method to solve the muscular redundancy problem, by comparing respectively the musculo-tendon forces prediction and its cost-effectiveness against common optimization methods. Since algorithm efficiency and equality constraints fulfillment highly belong to the optimization method, a two-phase procedure is proposed in order to identify and compare the complexity of the cost function, the number of iterations needed to find a solution and the computational time of the penalty-based method, the Lagrange multipliers method and pseudo-inverse method. Using a 2D knee musculo-skeletal model in an isometric context, the study of the cost functions isovalue curves shows that the solution space is 2D with the penalty-based method, 3D with the Lagrange multipliers method and 1D with the pseudo-inverse method. The minimal cost function area (defined as the area corresponding to 5% over the minimal cost) obtained for the pseudo-inverse method is very limited and along the solution space line, whereas the minimal cost function area obtained for other methods are larger or more complex. Moreover, when using a 3D lower limb musculo-skeletal model during a gait cycle simulation, the pseudo-inverse method provides the lowest number of iterations while Lagrange multipliers and pseudo-inverse method have almost the same computational time. The pseudo-inverse method, by providing a better suited cost function and an efficient computational framework, seems to be adapted to the muscular redundancy problem resolution in case of linear equality constraints. Moreover, by reducing the solution space, this method could be a unique opportunity to introduce optimization methods for a posteriori articulation of preference in order to provide a palette of solutions rather than a unique solution based on a lot of hypotheses.     

Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile

In this paper, we propose a new hybrid method based on Correlation-based feature selection method and Artificial Bee Colony algorithm,namely Co-ABC to select a small number of relevant genes for accurate classification of gene expression profile. The Co-ABC consists of three stages which are fully cooperated: The first stage aims to filter noisy and redundant genes in high dimensionality domains by applying Correlation-based feature Selection (CFS) filter method. In the second stage, Artificial Bee Colony (ABC) algorithm is used to select the informative and meaningful genes. In the third stage, we adopt a Support Vector Machine (SVM) algorithm as classifier using the preselected genes form second stage. The overall performance of our proposed Co-ABC algorithm was evaluated using six gene expression profile for binary and multi-class cancer datasets. In addition, in order to proof the efficiency of our proposed Co-ABC algorithm, we compare it with previously known related methods. Two of these methods was re-implemented for the sake of a fair comparison using the same parameters. These two methods are: Co-GA, which is CFS combined with a genetic algorithm GA. The second one named Co-PSO, which is CFS combined with a particle swarm optimization algorithm PSO. The experimental results shows that the proposed Co-ABC algorithm acquire the accurate classification performance using small number of predictive genes. This proofs that Co-ABC is a efficient approach for biomarker gene discovery using cancer gene expression profile.     

Optimization and simulation of continuous affinity-recycle extraction (care).

Simulation and optimization of continuous affinity recycle extraction (CARE), a protein purification unit operation based on protein adsorption to solid phase adsorbents, is described in this paper. Rather than packing conventional adsorbent particles in a fixed bed (column), solid/liquid contact is carried out in well-mixed reactors. Continuous operation is achieved by recirculation of the adsorbent particles between two or more contactors. The feasibility of this purification scheme was established with the recovery and isolation of the enzyme beta-galactosidase from E.coli, using the affinity support PABTG/Agarose. A mathematical model describing system performance was developed. The mathematical model was used to optimize several facets of the system design and operation. The base two-stage contractor design was modified by the addition of an intermediate wash stage as well as the incorporation of multiple adsorption stages. These design modifications serve to increase purification, concentration and recovery while utilizing the same amount of adsorbent. The methodology for defining and optimizing objective functions was developed and experimentally validated. Finally, optimum system start-up protocols, minimizing the time required to reach steady-state operation, were developed and experimentally validated. The impact of early introduction of adsorptive purification in a downstream processing sequence, with CARE, was evaluated and is described. Through the early introduction of a highly specific adsorptive step, significant purification is achieved simultaneously with clarification and concentration. In addition, purification performance in CARE was contrasted with that achievable in conventional column chromatography.     

Individualized performance prediction of sleep-deprived individuals with the two-process model.

We present a new method for developing individualized biomathematical models that predict performance impairment for individuals restricted to total sleep loss. The underlying formulation is based on the two-process model of sleep regulation, which has been extensively used to develop group-average models. However, in the proposed method, the parameters of the two-process model are systematically adjusted to account for an individual's uncertain initial state and unknown trait characteristics, resulting in individual-specific performance prediction models. The method establishes the initial estimates of the model parameters using a set of past performance observations, after which the parameters are adjusted as each new observation becomes available. Moreover, by transforming the nonlinear optimization problem of finding the best estimates of the two-process model parameters into a set of linear optimization problems, the proposed method yields unique parameter estimates. Two distinct data sets are used to evaluate the proposed method. Results of simulated data (with superimposed noise) show that the model parameters asymptotically converge to their true values and the model prediction accuracy improves as the number of performance observations increases and the amount of noise in the data decreases. Results of a laboratory study (82 h of total sleep loss), for three sleep-loss phenotypes, suggest that individualized models are consistently more accurate than group-average models, yielding as much as a threefold reduction in prediction errors. In addition, we show that the two-process model of sleep regulation is capable of representing performance data only when the proposed individualized model is used.     

Design and analysis of gastroenterology (GI) clinic in Digestive Health Center of University of Wisconsin Health

This paper is devoted to the design and analysis of the gastroenterology (GI) clinic in the Digestive Health Center (DHC) of University of Wisconsin Health. The DHC will consolidate several existing clinic and endoscopy locations into a single center. First, the work flow at a current GI clinic is studied. A Markov chain model is developed and then extended to non-Markovian case to evaluate patient average length of stay and staff utilization. The model is validated by the data observed in the clinic. It is shown that the model can provide accurate estimation of system performance. Then, using such a model, design options of the new GI clinic in the DHC are studied. To investigate the impact of different system configurations, what-if analyses are carried out and different patient check-out processes are investigated. Finally, recommendations for enhancing service at the new GI clinic are proposed to the DHC leadership.     

Multi-Target Analysis and Design of Mitochondrial Metabolism

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.     

Kinetic modeling of lipase‐catalyzed esterification reaction between oleic acid and trimethylolpropane: A simplified model for multi‐substrate multi‐product ping–pong mechanisms

Kinetic models are among the tools that can be used for optimization of biocatalytic reactions as well as for facilitating process design and upscaling in order to improve productivity and economy of these processes. Mechanism pathways for multi‐substrate multi‐product enzyme‐catalyzed reactions can become very complex and lead to kinetic models comprising several tens of terms. Hence the models comprise too many parameters, which are in general highly correlated and their estimations are often prone to huge errors. In this study, Novozym^®435 catalyzed esterification reaction between oleic acid (OA) and trimethylolpropane (TMP) with continuous removal of side‐product (water) was carried out as an example for reactions that follow multi‐substrate multi‐product ping‐pong mechanisms. A kinetic model was developed based on a simplified ping‐pong mechanism proposed for the reaction. The model considered both enzymatic and spontaneous reactions involved and also the effect of product removal during the reaction. The kinetic model parameters were estimated using nonlinear curve fitting through unconstrained optimization methodology and the model was verified by using empirical data from different experiments and showed good predictability of the reaction under different conditions. This approach can be applied to similar biocatalytic processes to facilitate their optimization and design. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:1422–1429, 2013     

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.     

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.     

基于启发式广度优先搜索的SVR参数优化方法研究

支持向量回归机(Support vector regressio,SVR)模型的拟合精度和泛化能力取决于其相关参数的选择,其参数选择实质上是一个优化搜索过程。根据启发式广度优先搜索(Heuristic Breadth first Search,HBFS)算法在求解优化问题上高效的特点,提出了一种以k-fold交叉验证的最小化误差为目标,HBFS为寻优策略的SVR参数选择方法,通过3个基准数据集对该模型进行了仿真实验,结果表明该方法在保证预测精度前提下,大幅度的缩短了训练建模时间,为大样本的SVR参数选择提供了一种新的有效解决方案。     

The influence of the way the muscle force is modeled on the predicted results obtained by solving indeterminate problems for a fast elbow flexion

A critical point in models of the human limbs when the aim is to investigate the motor control is the muscle model. More often the mechanical output of a muscle is considered as one musculotendon force that is a design variable in optimization tasks solved predominantly by static optimization. For dynamic conditions, the relationship between the developed force, the length and the contraction velocity of a muscle becomes important and rheological muscle models can be incorporated in the optimization tasks. Here the muscle activation can be a design variable as well. Recently a new muscle model was proposed. A muscle is considered as a mixture of motor units (MUs) with different peculiarities and the muscle force is calculated as a sum of the MUs twitches. The aim of the paper is to compare these three ways for presenting the muscle force. Fast elbow flexion is investigated using a planar model with five muscles. It is concluded that the rheological models are suitable for calculation of the current maximal muscle forces that can be used as weight factors in the objective functions. The model based on MUs has many advantages for precise investigations of motor control. Such muscle presentation can explain the muscle co-contraction and the role of the fast and the slow MUs. The relationship between the MUs activation and the mechanical output is more clear and closer to the reality.     

A novel mathematical model of activation and sensitization of platelets subjected to dynamic stress histories

Blood recirculating devices, such as ventricular assist devices and prosthetic heart valves, are burdened by thromboembolic complications requiring complex and lifelong anticoagulant therapy with its inherent hemorrhagic risks. Pathologic flow patterns occurring in such devices chronically activate platelets, and the optimization of their thrombogenic performance requires the development of flow-induced platelet activation models. However, existing models are based on empirical correlations using the well-established power law paradigm of constant levels of shear stress during certain exposure times as factors for mechanical platelet activation. These models are limited by their range of application and do not account for other relevant phenomena, such as loading rate dependence and platelet sensitization to high stress conditions, which characterize the dynamic flow conditions in devices. These limitations were addressed by developing a new class of phenomenological stress-induced platelet activation models that specifies the rate of platelet activation as a function of the entire stress history and results in a differential equation that can be directly integrated to calculate the cumulative levels of activation. The proposed model reverts to the power law under constant shear stress conditions and is able to describe experimental results in response to a diverse range of highly dynamic stress conditions found in blood recirculating devices. The model was tested in vitro under emulated device flow conditions and correlates well with experimental results. This new model provides a reliable and robust mathematical tool that can be incorporated into computational fluid dynamic studies in order to optimize design, with the goal of improving the thrombogenic performance of blood recirculating devices.