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.     

Computer control of fermentation processes

A review of computer control of fermentation processes is presented. Hardware and software technologies that have been used to implement computer control are discussed. This includes instrumentation, interfacing techniques, computer hardware configurations, data logging and documentation, displays and man-machine interaction, low-level control, back-up and error detection and programming techniques. Advanced control of fermentation processes with the utilization of modern control techniques is also presented. This topic is divided into steady state optimization and dynamic optimization. Finally, on-line estimation of bioreactor parameters for feedback control is presented.     

A neural network methodology of quadratic optimization.

According to the basic optimization principle of artificial neural networks, a novel kind of neural network model for solving the quadratic programming problem is presented. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. The stability and convergency of the neural network is investigated and the strategy of the neural optimization is discussed. The feasibility of the neural network method is verified with the computation examples. Results of the simulation of the neural network to solve optimum problems are presented to illustrate the computational power of the neural network method.     

Academic learning,a variational model

By assigning coordinates to the information space comprising all knowledge, rigorous mathematical interpretations can be placed on such terms as academic ability, memory and creativity such that these psychometric concepts can be incorporated into a framework of functional analysis which then permits the optimization of long-term academic learning processes through the location of the teaching trajectories in information space which will maximize the knowledge accumulated in a generalized educational system composed of a complex of subject-pupil-teacher interactions. The concepts of discrete and continuous information spaces are discussed in connection with subject-subject, subjectpupil and pupil-pupil interactions, and the advantages of using variational versus dynamic programming methods of optimizing alternative educational systems are evaluated.     

Dynamic motion planning of 3D human locomotion using gradient-based optimization

Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed.     

Data-based optimization of protein production processes

While data-based modeling is possible in various ways, data-based optimization has not been previously described. Here we present such an optimization technique. It is based on dynamic programming principles and uses data directly from exploratory experiments where the influence of the adjustable variables u were tested at various values. Instead of formulating the performance index J as a function of time t within a cultivation process it is formulated as a function of the biomass x. The advantage of this representation is that in most biochemical production processes J(x) only depends of the vector u of the adjustable variables. This given, mathematical programming techniques allow determining the desired optimal paths u _opt (x) from the x-derivatives of J(x). The resulting u _opt (x) can easily be transformed back to the u(t) profiles that can then be used in an improved fermentation run. The optimization technique can easily be explained graphically. With numerical experiments the feasibility of the method is demonstrated. Then, two optimization runs for recombinant protein formations in E. coli are discussed and experimental validation results are presented.     

Biomechanical model calculation of muscle contraction forces: a double linear programming method

This paper presents a novel scheme for the use of linear programming to calculate muscle contraction forces in models describing musculoskeletal system biomechanics. Models of this kind are frequently found in the biomechanics literature. In most cases they involve muscle contraction force calculations that are statically indeterminate, and hence use optimization techniques to make those calculations. We present a linear programming optimization technique that solves a two-objective problem with two sequential linear programs. We use the technique here to minimize muscle intensity and joint compression force, since those are commonly used objectives. The two linear program model has the advantages of low computation cost, ready implementation on a micro-computer, and stable solutions. We show how to solve the model analytically in simple cases. We also discuss the use of the dual problem of linear programming to gain understanding of the solution it provides.     

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.     

QBES: predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization

Solvent accessibility, one of the key properties of amino acid residues in proteins, can be used to assist protein structure prediction. Various approaches such as neural network, support vector machines, probability profiles, information theory, Bayesian theory, logistic function, and multiple linear regression have been developed for solvent accessibility prediction. In this article, a much simpler quadratic programming method based on the buriability parameter set of amino acid residues is developed. The new method, called QBES (Quadratic programming and Buriability Energy function for Solvent accessibility prediction), is reasonably accurate for predicting the real value of solvent accessibility. By using a dataset of 30 proteins to optimize three parameters, the average correlation coefficients between the predicted and actual solvent accessibility are about 0.5 for all four independent test sets ranging from 126 to 513 proteins. The method is efficient. It takes only 20 min for a regular PC to obtain results of 30 proteins with an average length of 263 amino acids. Although the proposed method is less accurate than a few more sophisticated methods based on neural network or support vector machines, this is the first attempt to predict solvent accessibility by energy optimization with constraints. Possible improvements and other applications of the method are discussed.     

Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm

Bioprocess and Biosystems Engineering - There exist several optimization strategies such as sequential quadratic programming (SQP), iterative dynamic programing (IDP), stochastic-based methods such...     

单形格子和单形重心设计统计模型的优化分析方法

单形格子和单形重心设计是两种非常实用的配方试验设计方法,但其统计模型的优化分析却很困难．本文通过对单形格子和单形重心设计基本原理的分析,根据数学规划理论,构建了专门对这两种试验设计的统计模型进行优化分析的方法,同时给出了应用实例．     

Modelling spatial dynamics of fish

Our ability to model spatial distributions of fish populations is reviewed by describing the available modelling tools. Ultimate models of the individual's motivation for behavioural decisions are derived from evolutionary ecology. Mechanistic models for how fish sense and may respond to their surroundings are presented for vision, olfaction, hearing, the lateral line and other sensory organs. Models for learning and memory are presented, based both upon evolutionary optimization premises and upon neurological information processing and decision making. Functional tools for modelling behaviour and life histories can be categorized as belonging to an optimization or an adaptation approach. Among optimization tools, optimal foraging theory, life history theory, ideal free distribution, game theory and stochastic dynamic programming are presented. Among adaptation tools, genetic algorithms and the combination with artificial neural networks are described. The review advocates the combination of evolutionary and neurological approaches to modelling spatial dynamics of fish.     

A trial design for evaluation of empiric programming of implantable cardioverter defibrillators to improve patient management

The delivery of implantable cardioverter defibrillator (ICD) therapy is sophisticated and requires the programming of over 100 settings. Physicians tailor these settings with the intention of optimizing ICD therapeutic efficacy, but the usefulness of this approach has not been studied and is unknown. Empiric programming of settings such as anti-tachycardia pacing (ATP) has been demonstrated to be effective, but an empiric approach to programming all VT/VF detection and therapy settings has not been studied. A single standardized empiric programming regimen was developed based on key strategies with the intention of restricting shock delivery to circumstances when it is the only effective and appropriate therapy. The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming for VT/VF detection and therapy in a broad group of about 900 dual chamber ICD patients. The trial will provide a better understanding of how particular programming strategies impact the quantity of shocks delivered and facilitate optimization of complex ICD programming.     

Dynamic decision-making in uncertain environments I. The principle of dynamic utility

Understanding the dynamics or sequences of animal behavior usually involves the application of either dynamic programming or stochastic control methodologies. A difficulty of dynamic programming lies in interpreting numerical output, whereas even relatively simple models of stochastic control are notoriously difficult to solve. Here we develop the theory of dynamic decision-making under probabilistic conditions and risks, assuming individual growth rates of body size are expressed as a simple stochastic process. From our analyses we then derive the optimization of dynamic utility, in which the utility of weight gain, given the current body size, is a logarithmic function: hence the fitness function of an individual varies depending on its current body size. The dynamic utility function also shows that animals are universally sensitive to risk and display risk-averse behaviors. Our result proves the traditional use of expected utility theory and game theory in behavioral studies is valid only as a static model.     

The merits of a parallel genetic algorithm in solving hard optimization problems

A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical and biomechanical optimization problems is compared to a sequential quadratic programming algorithm, a downhill simplex algorithm and a simulated annealing algorithm. When high-dimensional non-smooth or discontinuous problems with numerous local optima are considered, only the simulated annealing and the genetic algorithm, which are both characterized by a weak search heuristic, are successful in finding the optimal region in parameter space. The key advantage of the genetic algorithm is that it can easily be parallelized at negligible overhead.     

Optimal feed policy for fed-batch fermentation of ethanol production by Zymomous mobilis

Optimal feed control for the fed-batch fermentation process of ethanol production is studied. Additional inequality constraints are introduced in this optimization problem to assure the optimal solution in a reality region. Introducing an updating rule of augmented Lagrange multipliers to handle these inequality constraints, iterative dynamic programming can be used in a straightforward manner for the optimization of fed-batch fermentors. To obtain more accurate solution a method of sequential quadratic programming can be used to solve this problem again. As a result of this optimal control, the maximum production at final time is very close to the theoretical yield. Although sequential quadratic programming can be rapid convergence to the optimal solution, but very good initial starting points has to be used to ensure obtaining the global optimum. Experimental works were used to validate this study. The simulated results could fit the experiments satisfactorily.     

水稻苗床多目标营养施肥优化模型

根据水稻秧苗的壮秧标准,运用数学规划理论建立了水稻苗床多目标营养施肥优化模型,并运用该模型进行了水稻育苗营养土开发,取得了良好效果．     

METHODS FOR FASTER PARSIMONY ANALYSIS

Abstract — Several algorithms to speed up branch swapping searches for most parsimonious trees are described. The method for indirect tree length calculation when moving a clipped clade, based on final states for the divided tree, is expanded to take into account polymorphic characters, and to include the possibility of rejecting several locations as suboptimal by checking just one node. Three different algorithms for faster estimation of final state assignments for the divided tree based on calculations for the whole tree are presented. The first of these is approximate; it uses information from the final state sets for the whole tree. The second is exact, but it is slower than the first, and requires more memory; it is based on the union of the state sets of the descendants for each node. The third is also exact; it requires more memory and programming effort than the other two but it is faster, it is based on final and preliminary state sets for the whole tree ("incremental two-pass optimization"). Efficient ways to derive state assignments for collapsing trees, based on final states for the divided tree, are described. The recently proposed method of "incremental optimization" is discussed. It is likely that searches using that method will be no faster than searches using indirect calculation as originally described, and will be quite slower than the modified indirect calculation described here. Searches using that method will probably be significantly slowed down when zero-length branches are to be collapsed, since shortcuts for faster collapsing are not directly applicable.     

两种自然保护区设计方法——数学建模和计算机模拟

传统的自然保护区设计方法是打分法和Gap分析法,这两种方法简单易行但可靠性不高;地理信息系统(GIS)在保护区设计领域的应用也为人熟悉.关注近年来快速发展而国内使用不多的两种方法——数学建模和计算机模拟.数学建模主要用来从一组备选地块中选择一部分组成自然保护区,包括线性和非线性模型,用启发式算法或最优化算法求解.启发式算法具有速度快、灵活等优点,但解通常不是最优的,不能保证稀缺资源的最优化利用.最优化算法运算效率低,变量较多比如数百时就可能遇到计算困难,但解是最优的.预计两种算法都将继续发展.计算机模拟主要用于保护区评价、功能区划分、预测特定环境比如空间特征和气候变化对物种的影响等,多用启发式算法,与其它软件结合把结果以图画显示出来.两种方法特别是计算机模拟均要求保护区设计者有较强的专业知识.讨论了两种方法面临的问题和新的研究方向,至少包括:1)基础数据依然需要完善;2)一些新的因素比如动态性和不确定性如何在模型中考虑并与其它因素结合;3)气候变化预景下模拟参数如何评估和调整;4)如何协调保护与发展的关系;5)方法的实际应用需要研究者与决策者之间建立交流机制;6)多领域专家和相关利益方应有机会参与保护区设计.     

Comparative and phylogenetic analysis of developmental sequences

Event pairing has been proposed for the optimization of developmental sequences (event sequences) on a given phylogenetic hypothesis (cladogram) to determine instances of sequence heterochrony. Here, we show that event pairing is faulty, leading to the optimization of impossible hypothetical ancestors, the underestimation of the lengths of the developmental sequences on the tree, and the proposition of synapomorphies that are not supported by the data. When used for phylogenetic analysis, event pairing can even produce cladograms that are inconsistent with the data. These errors are caused by the fact that event pairing treats dependent features as if they were independent. We present a new method for comparative and phylogenetic analysis of developmental sequences that does not exhibit these errors. Our method applies Search-based character optimization and treats the entire developmental sequence as a single character that is then analyzed by using an edit cost function, which specifies the transformation cost between pairs of observed and unobserved character states, and dynamic programming. In other words, the developmental sequence is directly optimized on the tree. We used event pairing as an edit cost function, but others are possible.