级联掺磷光纤喇曼激光器的解析优化

讨论了二级级联喇曼光纤激光器的解析解和优化设计.通过引入几何平均功率、增益因子和归一化光纤有效长度,将描述泵浦光和斯托克斯光沿喇曼增益光纤分布的微分方程组简化成代数方程组,在对泵浦光采用线性传播近似后,获得了二级级联喇曼激光器的解析解.所获得的解析解同数值模拟结果吻合得很好.利用该解析解可方便和快速地讨论级联喇曼激光器的优化设计,计算不同泵浦功率下的最佳光纤长度、输出光纤光栅反射率和转换效率.泵谱功率越大,最佳光纤长度越短,最佳输出光纤光栅反射率越小.     

Optimization of peak power of passively Q-switched lasers by taking into account intracavity laser spatial distribution

The intracavity photon density and the initial population-inversion density in the diode-pumped passively Q-switched lasers are assumed to be Gaussian spatial distributions. The space-dependent rate equations are solved numerically. The key parameters of an optimally coupled passively Q-switched laser under Gaussian approximation are determined, and a group of general curves are generated for the first time. These key parameters include the optimal normalized coupling parameter and the optimal normalized saturable absorber parameter that maximize the peak power, and the corresponding normalized energy, normalized peak power, and normalized pulse width. The curves clearly show the dependence of the optimal key parameters on the parameters of the gain medium, the saturable absorber, and the resonator. In addition, the importance including the space variation is also shown. The optimal calculations for a diode-pumped passively Nd:YVO₄ laser are presented to demonstrate the use of the curves and the related formulas.     

Optimization of the slowly actively Q-switched laser

By taking into account turnoff time of the slowly active Q-switch, the coupled equations of a slowly actively Q-switched laser are given. These rate equations are solved numerically. The key parameters of an optimally coupled actively Q-switched laser with the turnoff time are determined, and a group of general curves are generated for the first time. These key parameters include the optimal normalized coupling parameter which maximizes the output energy, and the corresponding normalized energy, normalized peak power and normalized pulse width. The curves clearly show the dependence of the optimal key parameters on the parameters of the gain medium and the resonator. In addition, the importance of including turnoff time of the slowly active Q-switch is also shown. The optimal calculations for an actively Q-switched Nd:YVO₄ laser with acoustic–optic modulator are presented to demonstrate the use of the curves and the related formulas.     

Multicategory ψ-Learning and Support Vector Machine: Computational Tools

Many margin-based binary classification techniques such as support vector machine (SVM) and ψ-learning deliver high performance. An earlier article proposed a new multicategory ψ-learning methodology that shows great promise in generalization ability. However,ψ-learning is computationally difficult because it requires handling a nonconvex minimization problem. In this article, we propose two computational tools for multicategory ψ-learning. The first one is based on d.c. algorithms and solved by sequential quadratic programming, while the second one uses the outer approximation method, which yields the global minimizer via sequential concave minimization. Numerical examples show the proposed algorithms perform well.     

A deterministic,multi-item inventory model with supplier selection and imperfect quality

This paper considers the scenario of supply chain with multiple products and multiple suppliers, all of which have limited capacity. We assume that received items from suppliers are not of perfect quality. Items of imperfect quality, not necessarily defective, could be used in another inventory situation. Imperfect items are sold as a single batch, prior to receiving the next shipment, at a discounted price. The demand over a finite planning horizon is known, and an optimal procurement strategy for this multi-period horizon is to be determined. Each of products can be sourced from a set of approved suppliers, a supplier-dependent transaction cost applies for each period in which an order is placed on a supplier. A product-dependent holding cost per period applies for each product in the inventory that is carried across a period in the planning horizon. Also a maximum storage space for the buyer in each period is considered. The decision maker, the buyer, needs to decide what products to order, in what quantities, with which suppliers, and in which periods. Finally, a genetic algorithm (GA) is used to solve the model.     

Optimal production scheduling for the dairy industry

The increasing variety of products offered by the food industry has helped the industry to respond to market trends, but at the same time has resulted in a more complex production process, which requires flexibility and an efficient coordination of existing resources. Especially in industrial yogurt production, there is a wide variety of products that differ in features like fat content, the whey used to produce the mixture, the flavor, the size of the container or the language on the label. The great diversification and the special features that characterize yogurt production lines (satisfaction of multiple due dates, variable processing times, sequence-dependent setup times and costs and monitoring of inventory levels), render generic scheduling methodologies impractical for real-world applications. In this work we present a customized Mixed Integer Linear Programming (MILP) model for optimizing yogurt packaging lines that consist of multiple parallel machines. The model is characterized by parsimony in the utilization of binary variables and necessitates the use of only a small pre-determined number of time periods. The efficiency of the proposed model is illustrated through its application to the yogurt production plant of a leading dairy product manufacturing company in Greece.     

Some remarks on the Minty vector variational principle

In scalar optimization it is well known that a solution of a Minty variational inequality of differential type is a solution of the related optimization problem. This relation is known as “Minty variational principle.” In the vector case, the links between Minty variational inequalities and vector optimization problems were investigated in [F. Giannessi, On Minty variational principle, in: New Trends in Mathematical Programming, Kluwer Academic, Dordrecht, 1997, pp. 93-99] and subsequently in [X.M. Yang, X.Q. Yang, K.L. Teo, Some remarks on the Minty vector variational inequality, J. Optim. Theory Appl. 121 (2004) 193-201]. In these papers, in the particular case of a differentiable objective function f taking values in Rm and a Pareto ordering cone, it has been shown that the vector Minty variational principle holds for pseudoconvex functions. In this paper we extend such results to the case of an arbitrary ordering cone and a nondifferentiable objective function, distinguishing two different kinds of solutions of a vector optimization problem, namely ideal (or absolute) efficient points and weakly efficient points. Further, we point out that in the vector case, the Minty variational principle cannot be extended to quasiconvex functions.     

Competence-driven project portfolio selection,scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.     

Investigation of Simulated Trading — A multi agent based trading system for optimization purposes

Some years ago, Bachem, Hochstättler, and Malich proposed a heuristic algorithm called Simulated Trading for the optimization of vehicle routing problems. Computational agents place buy-orders and sell-orders for customers to be handled at a virtual financial market, the prices of the orders depending on the costs of inserting the customer in the tour or for his removal. According to a proposed rule set, the financial market creates a buy-and-sell graph for the various orders in the order book, intending to optimize the overall system. Here I present a thorough investigation for the application of this algorithm to the traveling salesman problem.     

A hybrid approach for optimal design of signalized road network

For a signalized road network with expansions of link capacity, the maximum possible increase in travel demands is considered while total delays for travelers are minimized. Using the concept of reserve capacity of signal-controlled junctions, the problem of finding the maximum possible increase in travel demand and determining optimal link capacity expansions can be formulated as optimization programs. In this paper, we present a new solution approach for simultaneously solving the maximum increase in travel demands and minimizing total delays of travelers. A projected Quasi-Newton method is proposed to effectively solve this problem to the KKT points. Numerical computations and comparisons are made on real data signal-controlled networks where obtained results outperform traditional methods.