Multi-objective optimization algorithm for multi-page invoice order scheduling problem

针对多联票据订单调度问题进行研究,考虑最小化所有订单的最大完成时间、平均完成时间、最大延迟时间和平均延迟时间四个目标函数,提出一种多目标混合遗传算法。基于问题自身的特点提出完整解的构造算法,使用随机变动的权重实现多维多方向的搜索,设计了基于非主导性比较的局部搜索算法以寻找更多更好的非主导解,建立与更新全局非主导解集档案以保留最好的Pareto近似最优解。采用Pareto兼容的衡量参数,将该算法与两种对照算法进行了比较。大量算例的试验结果表明,该算法能够得到更接近于Pareto前沿且多样性分布更好的Pareto近似最优解集。     

A parallel multi-population biased random-key genetic algorithm for a container loading problem

This paper presents a multi-population biased random-key genetic algorithm (BRKGA) for the single container loading problem (3D-CLP) where several rectangular boxes of different sizes are loaded into a single rectangular container. The approach uses a maximal-space representation to manage the free spaces in the container. The proposed algorithm hybridizes a novel placement procedure with a multi-population genetic algorithm based on random keys. The BRKGA is used to evolve the order in which the box types are loaded into the container and the corresponding type of layer used in the placement procedure. A heuristic is used to determine the maximal space where each box is placed. A novel procedure is developed for joining free spaces in the case where full support from below is required. The approach is extensively tested on the complete set of test problem instances of Bischoff and Ratcliff [1] and Davies and Bischoff [2] and is compared with 13 other approaches. The test set consists of 1500 instances from weakly to strongly heterogeneous cargo. The computational experiments demonstrate that not only the approach performs very well in all types of instance classes but also it obtains the best overall results when compared with other approaches published in the literature.     

Deriving reservoir operating policies using classifier systems

An application of classifier systems to time variant, water resources allocation problems is described, the aim of the work being to assess the suitability of the technique for deriving control strategies. The difficulties of allocating credit to classifiers where co-operating sequences of rules have to be developed are discussed and methods for overcoming some of the difficulties are covered. The training of the classifier on two problems, the first containing a single, surface water reservoir and the second two reservoirs is used to develop techniques and the results show that classifiers possess an ability to learn about the domain. However the resulting operating strategies are not appropriate for the operation of water resources systems. The work indicates that in their current format, classifier systems cannot learn to operate systems where long, interdependent chains of decisions are involved.     

Optimization of variable demand fuzzy economic order quantity inventory models without and with backordering

A variable demand inventory model was developed for minimizing inventory cost, treating the holding and ordering costs and demand as independent fuzzy variables. Thereafter, backordering cost was also considered as an independent fuzzy variable. Fuzzy expected value model and fuzzy dependent chance programming model were constructed to find the optimal economic order quantity, which would minimize the fuzzy expected value of the total cost, so that the credibility of the total cost not exceeding a certain budget level was maximized. Optimization was carried out using genetic algorithms and particle swarm optimization algorithm, and their performances were compared. The developed model was found to be efficient not only in one artificial case study but also in two data sets collected from the industries. Therefore, this model could solve real-world problems, too.     

Projection data replenishment algorithm for limited angle tomography

A new iterative algorithm of tomographic reconstruction of objects on the basis of projection data available in a limited range of angles only is proposed. The algorithm is based on calculating artificial projections in those directions where projection data are unavailable. By means of numerical simulations, it is verified that the algorithm developed ensured high quality of reconstruction up to the angular interval of 45–60°.     

Electric distribution network reconfiguration based on a fuzzy multi-criteria decision making algorithm

Topological reconfiguration is an important tool for the planning and operation of electric power distribution systems. The amount of time that an algorithm spends to obtain an alternative topological status for the system is not a primary concern, as the main goals of distribution operation planning are the reduction of power losses, the enhancement of the voltage profile, and the increase of reliability levels. The utility can use multiple criteria regarding the observation of regulation policies and public awareness to drive the topological reconfiguration. Several researchers are looking for new optimization methods, as the complexity of this combinatorial issue is high in large systems and the classic optimization methods are failing to address the problem reasonably. Therefore, a new fuzzy multi-criteria decision making algorithm for the proper processing of the information sources available at the utilities in the context of distribution network reconfiguration is proposed. The algorithm is evaluated through the use of a proof-of-concept implementation in a set of case studies based on actual distribution systems.     

A branch and bound enhanced genetic algorithm for scheduling a flowline manufacturing cell with sequence dependent family setup times

This paper addresses the permutation flowline manufacturing cell with sequence dependent family setup times problem with the objective to minimize the makespan criterion. We develop a cooperative approach including a genetic algorithm and a branch and bound procedure. The latter is probabilistically integrated in the genetic algorithm in order to enhance the current solution. Moreover, the application of the branch and bound algorithm is based upon the decomposition of the problem into subproblems. The performance of the proposed method is tested by numerical experiments on a large number of representative problems.     

自适应噪声抵消系统的设计与性能仿真

LabVIEW作为一种图形化虚拟仪器编程语言,其在信号的生成、分析和处理上优势明显。利用其可以设计虚拟的自适应噪声抵消系统,从而可以应用于实际工程中对噪声抵消性能的测试验证。本文针对强噪声背景下有用信号的提取问题,介绍了自适应噪声抵消的原理,并对3种基本自适应滤波算法的步骤与流程进行了阐述。基于LabVIEW图形化虚拟仪器编程语言设计了虚拟自适应噪声抵消系统,系统结构简单,人机交互界面较好。在该系统下以3种自适应滤波算法为例仿真验证了的噪声抵消的性能,结果表明,3种自适应滤波算法的滤波性能与实际分析相符,验证了所设计系统的有效性和正确性。     

Drawing in Time: Processes of Design and Fabrication

Drawing embeds time within itself, notating changes in space and duration. For Babak Bryan and Henry Grosman , founding partners of BanG studio in Long Island City, New York: ‘The drawing is a result of process, but it also describes process.’ It is only through this temporal practice that an essential connection is developed between drawing and building. With the widespread adoption of building information modelling (BIM), linear graphic representation finds itself in crisis, challenged by the static inertia of the model. As Bryan and Grosman describe, the well-crafted drawing remains essential to their studio's approach informing the method of ‘making and remaking’.     

模式匹配在条件接收系统中的应用

条件接收系统（Conditional Access System ,CAS）是付费数字电视的核心技术,是对数字电视内容的一种保护手段,保证用户只有在满足一定的条件下才能正常收看特定的内容[1]。通过前端条件接收系统,可以向用户发送授权指令、邮件、公告等信息,同时前端条件接收系统还会接受来处营业厅的催缴费信息、加授权或者减授权的指令,即前端系统对外是具有一定的开放性。因此,对邮件、公告信息、催缴费信息等内容建立审查机制是十分必要的,而这些内容大多数为文字,使用模式匹配算法来建立自动审查机制是一个好的选择。本文主要介绍文本过滤在条件接收系统中的重要性、如何将模式匹配算法加入到条件接收系统中。