柔性生产下马氏需求EPQ模型的最优(P,Q,T)策略

随机需求环境下研究柔性制造系统的经济生产批量(Economic Production Quantity,EPQ)模型的最优生产策略问题.在假设需求满足马尔可夫性且为需求状态依赖的条件下,根据顾客的需求程度对市场需求进行状态划分,建立了马氏需求模型,对系统未来的需求提出了科学的预测方法.在该模型的基础上,结合柔性制造系统的理论,构建了柔性生产下马氏需求EPQ模型.利用函数的凸性研究了新的EPQ模型的最优解的存在性,进而提出模型的最优(P,Q,T)策略.模型的数值算例表明,利用最优(P,Q,T)策略可以提高制造系统的生产率、缩短产品生产周期及实现小批量的生产模式.     

OD估计双层规划扩展模型

利用双层规划模型进行OD估计,建立双层规划扩展模型.考虑OD估计问题中的随机误差,基于Bayes估计和多元正态分布建立上层目标函数;考虑用户路径选择行为的随机性,基于随机用户均衡建立需求可变动的下层目标函数,同时该扩展模型能适应我国混合交通的实际,既能适用于拥挤网络、也能适用于非拥挤网络,最后通过算例证明此模型的有效性.     

几乎完全非线性多项式函数间的EA等价性

简述了几乎完全非线性(APN)多项式函数的研究现状,讨论了两类几乎完全非线性多项式函数间的扩张仿射(EA)等价性,给出了验证EA等价的一般方法.     

一个具有绝对拥挤性的政府消费性支出模型

本文研究了随机内生增长模型中具有绝对拥挤性的政府消费性支出.利用改进的连续时间随机分析方法--"附加效用"值函数法,获得了个体的最优消费、随机经济增长路径和个体的最优福利.     

带Poisson跳跃的正倒向随机微分对策的最大值原理与动态规划之间的关系

本文研究了带Poisson跳跃的零和正倒向随机微分对策的最大值原理与动态规划之间的关系;在一定的可微性假设下,建立了对偶过程、广义Hamilton函数和值函数之间的联系;作为主要结果的应用,讨论了金融市场中一类带有模型不确定性的递归效用投资组合优化问题.     

公债市场波动、消费动态与经济增长——基于递归效用与习惯形成的分析

本文在考虑公债市场波动的经济增长模型中引入递归效用和习惯形成,建立基于递归效用和习惯形成的随机经济增长模型,求得均衡时的最优消费和政府债券需求,讨论递归效用和习惯形成对最优消费和政府债券需求的影响,推导出消费的动态路径和经济增长路径,研究递归效用和习惯形成对消费动态路径和经济增长路径的影响.     

以费用为独立变量的随机机会约束规划模型研究

美军提出的"以费用为独立变量(CAIV)"方法从论证阶段开始,就强调需求、性能与费用三者之间的平衡,并采用费用-效能综合权衡优化模型进行系统费用和性能参数的最优决策.但是该模型没有考虑采办过程中的不确定因素,且由于决策者往往很关心权衡结果中费用与性能的置信水平问题,因此提出要将采办中的不确定性因素作为随机变量引入权衡模型,建立一种综合权衡优化的随机机会约束规划模型,并进行了示例验证.     

一般需求函数下的动态定价模型研究

信息技术的发展为企业深入了解消费者需求提供了可能。为了更好地利用所掌握的消费者消费习惯信息,实现企业效用最大化,本文从产品的一般需求函数出发,根据消费者对产品价格的消费反应模式,建立了有限计划期内企业效用函数最大化的模型,并给出了模型求解过程,得到了计划期末的价格水平P(T)为固定或非固定两种情况下的最优价格路径。通过严格的数学证明和数值实验,分析了关于价格路径的重要性质和其在企业动态定价决策中的重要意义。     

平板折叠桌最优设计的模型研究

现有一种创意平板折叠桌.建立初等函数模型,给出折叠桌的动态变化过程及开槽长度和桌脚边缘线的函数表达;根据"稳固性、加工难易、用材多少"等要求建立多目标函数的优化模型,采用基于多目标函数的"模拟退火"算法,求得一定条件下的最优设计方案.最后给出一种折叠桌设计软件的初等目标函数数学模型,并利用次模型设计出三角形、六边形两种新的创意平板折叠桌.     

基于可拓学的镍氢电池定制设计中矛盾问题求解

镍氢电池用途广泛,需要根据不同的应用需求定制设计相应性能的镍氢电池.在镍氢电池定制设计过程中,为了解决用户需求与工程技术特征的冲突问题,应用可拓学的矛盾问题处理方法建立镍氢电池不相容问题的可拓求解模型,通过可拓变换找出可拓求解模型中的核问题,计算变换后的相容度函数,识别出引起不相容问题的主要工程技术特征;通过改善这些工程技术特征,获得矛盾问题的可行优化方案.最终通过改进电池的切片毛刺和集耳形状、利用石墨烯取代导电炭黑试制出一种石墨烯镍氢电池,获得满足客户需求的高性能镍氢电池新产品.     

基于2可加模糊测度的多线性效用函数建模和求解

构建合适的多属性效用函数是多属性效用分析的关键。针对不同偏好假设,文献从可加独立、效用独立、效用依赖等分别进行了多属性效用函数构建的研究。然而,由于求解的复杂性,多属性效用理论的应用绝大部分限于可加效用函数和多乘效用函数。提出一种基于2可加模糊测度的多线性效用函数建模和求解方法。首先,证明多线性效用函数和基于模糊测度的多线性模型之间的等价性,提出利用基于模糊测度的多线性模型对多线性效用函数进行表示。其次,针对多线性模型的特点和模糊测度识别的复杂性,利用Banzhaf交互指数和2可加模糊测度对多线性模型进行表示,并利用最小方法差进行模糊测度和Banzhaf交互指数识别,进而实现多线性效用函数的求解。最后,将方法用于某可穿戴医疗设备基于顾客需求的多属性效用函数构建,确认了可行性。方法为多线性效用函数的求解提供了一种新思路。     

Optimal Design of Heat-Integrated Multipurpose Batch Facilities with Economic Savings in Utilities: A Mixed Integer Mathematical Formulation

The increasing importance of utilities rationalization is an aspect that must be addressed from the early steps of the industrial design procedure. This paper addresses this problem and presents a mathematical formulation for the detailed design of multi-purpose batch process facilities where heat-integration and economic savings in utilities are considered. This generalization appears as an extension of the work of Barbosa-Póvoa et al. [3] where some important design aspects were not considered. In particular, no consideration was given to the economic savings in utility requirements, while considering both the cost of the auxiliary structures (i.e. heat-exchanger through their transfer area) and the design of the utility circuits and associated piping costs. These aspects can appear quite relevant at the design level if the connectivity cost and consumption utilities account for a significant share of capital investment within the plant budget. Also, and at the operational level, heat-integration considerations often result in important plant savings. The problem is formulated as a Mixed Integer Linear Problem (MILP) where binary variables are introduced to characterise operational and topological choices, and continuous ones define the equipment capacities, as well as the amounts of material within the overall process. The applicability of the proposed model is shown via the solution of some illustrative examples.     

Graph Coloring with Adaptive Evolutionary Algorithms

This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EAs). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping Genetic Algorithm (GGA) on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping (GA) and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity.     

Moments of utility functions and their applications

This paper presents moments and cross-moments of utility functions and measures of utility dependence. We start with an interpretation of the nth moment of a utility function, and describe methods for its assessment in practice and consistency checks that need to be satisfied for any assessed moments. We then show how moments of a utility function (i) provide a new method to determine the parameters of a given functional form of a utility function and (ii) to derive the functional form of a utility function that satisfies some given moment assessments. Next, we derive a fundamental formula that relates the expected utility of a joint distribution to the expected utility of the marginal distributions for multiattribute utility functions. We use this formulation to provide an intuitive interpretation for cross-moments of utility functions and illustrate their use in (i) constructing multiattribute utility functions that incorporate utility dependence and (ii) in providing necessary conditions for utility independence in decisions with multiple attributes. We end with a new measure of utility dependence for multiattribute utility functions and work through several examples to illustrate the approach.     

Optimization in aircraft engineering including approximate structural evaluations by support vector machines

The minimum weight design of structures made of fiber reinforced composite materials leads to a class of mixed‐integer optimization problems for which evolutionary algorithms (EA) are well suited. Based on these algorithms the optimization tool package GEOPS has been developed at TU Dresden. For each design generated by an EA the structural response has to be evaluated. This is often based on a finite element analysis which results in a high computational complexity for each single design. Typical runs of EA require the evaluation of thousands of designs. Thus, an efficient approximation of the structural response could improve the performance considerably. To achieve this aim the constraints on the structural response are approximated by means of support vector machines (SVM). It is trained by means of exact structural evaluations for selected design alternatives only. Several ways to enhance the efficiency of such an optimization procedure are presented. As an example for a typical aircraft structure, a stiffened composite panel under compressive and shear loading is considered. The SVM is trained on geometrical and material data. Representing the design space of composite panels by ABD matrices turned out to be a valuable means for obtaining well trained SVMs. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

On the analysis of a simple evolutionary algorithm on quadratic pseudo-boolean functions

Evolutionary algorithms are randomized search heuristics, which are often used as function optimizers. In this paper the well-known (1+1) Evolutionary Algorithm ((1+1) EA) and its multistart variants are studied. Several results on the expected runtime of the (1+1) EA on linear or unimodal functions have already been presented by other authors. This paper is focused on quadratic pseudo-boolean functions, i.e., polynomials of degree 2, a class of functions containing NP-hard optimization problems. Subclasses of the class of all quadratic functions are identified where the (1+1) EA is efficient, for other subclasses the (1+1) EA has exponential expected runtime, but a large enough success probability within polynomial time such that a multistart variant of the (1+1) EA is efficient. Finally, a particular quadratic function is identified where the EA and its multistart variants fail in polynomial time with overwhelming probability.     

A generalized design approach to solution of the non-convex quadratic programming problem

This paper outlines a generalized, systematic design approach to solution of the non-convex quadratic programming problem. It is based on a previous formulation of utility of a general system in terms of efficiency and robustness of the system. The approach is to introduce a robustness term of arbitrary magnitude into the design utility function. Mathematically, this makes the problem convex. From a design approach, it yields a more general solution allowing specialization to proceed by decreasing robustness (on an initially convex utility surface in the feasible design space) until the region of the overall optimum is approached. The approach is mathematically related to the Metropolis technique of simulated annealing but a more systematic (less random) solution process is used. It is analogous also to the heuristic technique of Burkard and Bonniger. These two previous techniques are the most effective so far reported for the quadratic programming problem. The robustness approach provides an underpinning for each and opens up further solution options. Applications include layout of buildings and other constructed facilities and information technology layout problems.     

Portfolio Selection: A Compromise Programming Solution

A surrogate for an investor's bi-criteria utility function (profitability, safety) is proposed as an alternative methodology for selecting portfolios. The optimum is approximated by resorting to a recent utility theorem expounded in multi-criteria analysis. This method is developed for an ‘average’ investor and could be used as a routine procedure by investment consultants with incomplete information of the client's utility function.     

Making and breaking power laws in evolutionary algorithm population dynamics

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic forms or can only evaluate evolution over simple test functions. Analysis of EA behavior under more complex conditions is needed to broaden our understanding of this population-based search process. This paper presents an approach to analyzing EA behavior that can be applied to a diverse range of algorithm designs and environmental conditions. The approach is based on evaluating an individual’s impact on population dynamics using metrics derived from genealogical graphs. From experiments conducted over a broad range of conditions, some important conclusions are drawn in this study. First, it is determined that very few individuals in an EA population have a significant influence on future population dynamics with the impact size fitting a power law distribution. The power law distribution indicates there is a non-negligible probability that single individuals will dominate the entire population, irrespective of population size. Two EA design features are however found to cause strong changes to this aspect of EA behavior: (1) the population topology and (2) the introduction of completely new individuals. If the EA population topology has a long path length or if new (i.e. historically uncoupled) individuals are continually inserted into the population, then power law deviations are observed for large impact sizes. It is concluded that such EA designs can not be dominated by a small number of individuals and hence should theoretically be capable of exhibiting higher degrees of parallel search behavior.     

An efficient algorithm for mining high utility itemsets with negative item values in large databases

Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets with negative item values is important for mining interesting patterns like association rules. In this paper, we propose a novel method, namely HUINIV (High Utility Itemsets with Negative Item Values)-Mine, for efficiently and effectively mining high utility itemsets from large databases with consideration of negative item values. To the best of our knowledge, this is the first work that considers the concept of negative item values in utility mining. The novel contribution of HUINIV-Mine is that it can effectively identify high utility itemsets by generating fewer high transaction-weighted utilization itemsets such that the execution time can be reduced substantially in mining the high utility itemsets. In this way, the process of discovering all high utility itemsets with consideration of negative item values can be accomplished effectively with less requirements on memory space and CPU I/O. This meets the critical requirements of temporal and spatial efficiency for mining high utility itemsets with negative item values. Through experimental evaluation, it is shown that HUINIV-Mine outperforms other methods substantially by generating much less candidate itemsets under different experimental conditions.