超大尺寸激光测距大型测控网络研制

为了解决超大尺寸激光测距过程中所面临的测量任务大、测量点数多、数据庞大和巨型数据处理困难等问题,设计了超大尺寸激光测距大型测控网络的研制方案。提出将云计算平台与大型测控网络相结合,使得若干台具有串口通信功能的仪器协同工作,获得更强大的测量能力,实现了多种无线网络融合,并解决海量数据的存储及数据处理问题。实验结果表明:系统内存设定为2G,虚拟机硬盘设定为500G,当数据存储量超过500G时不会出现不能继续存储的问题,测量数据可以准确地被存储到云平台;一次性处理数据量超过2G时不会出现内存不足的问题,能够实现巨型数据的处理问题;Matlab和虚拟机结合能够很好的完成复杂运算任务,快速准确地确定被测关键点坐标。     

Pattern-set generation algorithm for the one-dimensional multiple stock sizes cutting stock problem

A pattern-set generation algorithm (PSG) for the one-dimensional multiple stock sizes cutting stock problem (1DMSSCSP) is presented. The solution process contains two stages. In the first stage, the PSG solves the residual problems repeatedly to generate the patterns in the pattern set, where each residual problem is solved by the column-generation approach, and each pattern is generated by solving a single large object placement problem. In the second stage, the integer linear programming model of the 1DMSSCSP is solved using a commercial solver, where only the patterns in the pattern set are considered. The computational results of benchmark instances indicate that the PSG outperforms existing heuristic algorithms and rivals the exact algorithm in solution quality.     

Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm

This paper describes the optimization of a load-bearing thermal insulation system characterized by hot and cold surfaces with a series of heat intercepts and insulators between them. The optimization problem is represented as a mixed variable programming (MVP) problem with nonlinear constraints, in which the objective is to minimize the power required to maintain the heat intercepts at fixed temperatures so that one surface is kept sufficiently cold. MVP problems are more general than mixed integer nonlinear programming (MINLP) problems in that the discrete variables are categorical; i.e., they must always take on values from a predefined enumerable set or list. Thus, traditional approaches that use branch and bound techniques cannot be applied.In a previous paper, a linearly constrained version of this problem was solved numerically using the Audet-Dennis generalized pattern search (GPS) method for MVP problems. However, this algorithm may not work for problems with general nonlinear constraints. A new algorithm that extends that of Audet and Dennis by incorporating a filter to handle nonlinear constraints makes it possible to solve the more general problem. Additional nonlinear constraints on stress, mass, and thermal contraction are added to that of the previous work in an effort to find a more realistic feasible design. Several computational experiments show a substantial improvement in power required to maintain the system, as compared to the previous literature. The addition of the new constraints leads to a very different design without significantly changing the power required. The results demonstrate that the new algorithm can be applied to a very broad class of optimization problems, for which no previous algorithm with provable convergence results could be applied.     

Minimisation of total tardiness for identical parallel machine scheduling using genetic algorithm

In recent years research on parallel machine scheduling has received an increased attention. This paper considers minimisation of total tardiness for scheduling of n jobs on a set of m parallel machines. A spread-sheet-based genetic algorithm (GA) approach is proposed for the problem. The proposed approach is a domain-independent general purpose approach, which has been effectively used to solve this class of problem. The performance of GA is compared with branch and bound and particle swarm optimisation approaches. Two set of problems having 20 and 25 jobs with number of parallel machines equal to 2, 4, 6, 8 and 10 are solved with the proposed approach. Each combination of number of jobs and machines consists of 125 benchmark problems; thus a total for 2250 problems are solved. The results obtained by the proposed approach are comparable with two earlier approaches. It is also demonstrated that a simple GA can be used to produce results that are comparable with problem-specific approach. The proposed approach can also be used to optimise any objective function without changing the basic GA routine.     

An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis

Selecting a proper machine tool is one of the important decisions a company has to make. Companies which fail to do so face many problems which negatively affect the firm's productivity, flexibility, precision and its responsiveness capabilities. Selection of a machine tool involves a lot of criteria to be simultaneously studied and so it requires a multi-criterion decision making (MCDM) method to solve it. Also the subjectivity involved in such decisions ask for the use of theories such as fuzzy and grey which are very effective in handling subjective inputs. This paper integrates the fuzzy analytic hierarchy process (AHP) and grey relational analysis approaches for the selection of a machine tool from a given set of alternatives. Fuzzy AHP is used to calculate the priority weights of the criteria. Subsequently grey relational analysis (GRA) is employed to rank the alternatives. A well known problem existing in literature has been picked up for the numerical illustration. The results obtained in this paper are better when compared with that existing in literature.     

Solution of non-linear differential equations by discrete least squares

A general-purpose technique has been developed for solving non-linear partial differential equations. A set of approximating functions with undetermined parameters is used to evaluate the differential equation and boundary conditions at discrete points, forming a set of residuals to be minimized. The parameters which minimize the sum of squared residuals are determined by a non-linear least-squares minimization technique. Initial value problems are solved by integrating the equations with respect to time at the fitting points by a predictor-corrector algorithm. The resulting formulation is independent of the form of the problem and the approximating functions, so that a broad class of problems may be solved with a single computer program. The technique is applied to several boundary and initial value problems in one and two spatial dimensions. The tecnique is applied to several boundary and initial value problems in one and two spatial dimensions.     

An inverse geometric problem: Position and shape identification of inclusions in a conductor domain

This work presents a methodology for identifying inclusions in a conductor domain. The methodology is based on electrical potential measurements on the external boundary of a conductor body subjected to a prescribed set of electrical current injections. The boundary of each inclusion is approximated by a special kind of spline, whose control points have an extra parameter related to the distance between the control point and the curve. Such special feature allows identification of smooth or sharp inclusions with the same initial guess. The identification is an inverse problem that, in this work, is solved by the Levenberg–Marquardt Method. This iterative method tries to locate the minimum of an objective function, the square of the norm of a residual vector function, given by the differences between electrical potential measurements and the computed ones. The computation of the electrical potential is called forward problem and it is solved by an implementation of the direct formulation of the Boundary Element Method (BEM). The present paper addresses two approaches for computing the derivatives of the residual function with respect to the minimization parameters, required by the Levenberg–Marquardt Method. The first one is based on finite differences and the second one is based on the direct differentiation of the integral equation of the BEM for potential problems. Performance comparisons of these two approaches are presented, based on numerical experiments of identification of inclusions with noisy datasets computationally obtained.     

A decomposition-based approach to flexible flow shop scheduling under machine breakdown

Manufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling under machine breakdown.     

带软时间窗的同时取送货车辆路径问题研究

考虑软时间窗下的车辆路径问题,客户点常伴有同时取送货的双重需求。针对此类问题,通过对软时间窗、车辆在途前后时间关系及二者融合问题进行刻画,同时将车辆行驶距离、车辆使用数、违反软时间窗总时间、客户满意度等纳入综合考量,构建相应混合整数非线性规划(mixed integer nonlinear programming, MINLP)模型。设计相应多目标优化求解算法,运用理想点法对目标函数进行转化,将多目标优化问题转化为单目标优化问题。结合相应算例集,运用LINGO 17.0全局求解程序求得每组算例的全局最优解。结果表明,针对带软时间窗的同时取送货车辆路径问题(vehicle routing problem with simultaneous pick-up and delivery and soft time windows, VRPSPDSTW),所建模型及算法是有效且可行的。     

Dominant graphs for rectilinear network design with barriers

Given a set of points on a Cartesian plane and the coordinate axes, the rectilinear network design problem is to find a network, with arcs parallel to either one of the axes, that minimizes the fixed and the variable costs of interactions between a specified set of pairs of points. We show that, even in the presence of arbitrary barriers, an optimal solution to the problem (when feasible) is contained in a grid graph defined by the set of given points and the barriers. This converts the spatial problem to a combinatorial problem. Finally we show connections between the rectilinear network design problem and a number of well-known problems. Thus this paper unifies the known dominating set results for these problems and extends the results to the case with barriers.     

Learning for efficient search

Learning for problem solving involves acquisition and storage of relevant knowledge from past problem solving instances in a domain in such a form that the information can be used to effectively solve subsequent problems in the same domain. Our interest is in the role of learning in problem solving systems that solve problems optimally. Such problems can be solved by an informed search algorithm like A*. Learning a stronger heuristic function leads to more effective problem solving. A set of arbitrary features of the domain induce a clustering of the state space. The heuristic information associated with each cluster may be learned. We discuss the use of a new form of information in the form ofh* set (the set of optimum cost values of all nodes of the cluster) and present an algorithm for using the information that is more effective than A*. A possibilistic (fuzzy set theoretic) extension of this algorithm is also presented. This version can handle incomplete information and is expected to find solutions faster in the average case with controlled relaxation in the optimality guarantee. We also discuss how to make the best use of the features, when the system has memory restrictions that limit the number of classes that can be stored.     

Algebraic geometry and group theory in geometric constraint satisfaction for computer-aided design and assembly planning

Mechanical design and assembly planning inherently involve geometric constraint satisfaction or scene feasibility (GCS/SF) problems. Such problems imply the satisfaction of proposed relations placed between undefined geometric entities in a given scenario. If the degrees of freedom remaining in the scene are compatible with the proposed relations or constraints, a set of entities is produced that populate the scenario satisfying the relations. Otherwise, a diagnostic of inconsistency of the problem is emitted. This problem appears in various forms in assembly planning (assembly model generation), process planning, constraint driven design, computer vision, etc. Previous attempts at solution using separate numerical, symbolic or procedural approaches suffer serious shortcomings in characterizing the solution space, in dealing simultaneously with geometric (dimensional) and topological (relational) inconsistencies, and in completely covering the possible physical variations of the problem. This investigation starts by formulating the problem as one of characterizing the solution space of a set of polynomials. By using theories developed in the area of algebraic geometry, properties of Grobner Bases are used to assess the consistency and ambiguity of the given problem and the dimension of its solution space. This method allows for die integration of geometric and topological reasoning. The high computational cost of Grobner Basis construction and the need for a compact and physically meaningful set of variables lead to the integration of known results on group theory. These results allow the characterization of geometric constraints in terms of the subgroups of the Special Group of Euclidean displacements in E³, SE(3). Several examples arc developed which were solved with computer algebra systems (MAPLE and Mathematica). They are presented to illustrate the use of the Euclidean group-based variables, and to demonstrate the theoretical completeness of the algebraic geometry analysis over the domain of constraints expressible as polynomials.     

一类灰色模糊决策问题的熵权分析方法

以灰色系统理论和模糊数学为基础，探讨了不确定型决策问题的特性，分析了一些相关成果中所给方法在直接处理灰色模糊数方面的优势与不足。运用优化理论和熵极大化准则，建立了基于灰色模糊关系的多属性群体决策方法，分别对属性权重向量已知和未知两种情况给出了简便实用的算法，通过算例说明了算法的合理性。     

A Coupled Thermo-Electromagnetic Formulation Based on the Cell Method

Two discrete approaches for 3-D weakly coupled thermo-electromagnetic, magnetically linear, quasi-static problems in bounded domains are presented and compared. Both approaches are based, as far as the electromagnetic equations are concerned, on discrete potentials to model both conducting and nonconducting regions, whereas the thermal problem is solved by direct use of the temperature as unknown. The code implementing the formulations is validated by comparing results with those obtained by a commercial axisymmetric package with similar space and time discretizations.     

一种基于扫描线的图像变形算法

在分析了现有图像变形方法的基础上,提出了一种基于扫描线的图像变形方法。首先指定原始图像的特征轮廓并获得特征轮廓的控制点,然后对控制点进行调整得到目标图像的边界约束,采用给出的扫描线算法对边界约束内部进行像素填充,得到变形图像。解决了变换过程中像素点的选取问题,更重要的是保证了变形后的图像颜色与原始图像变化趋势相同。     

Concepts of neighbourhood and universal compact yield towards achieving best pattern layouts

Clustering of a given set of patterns or nesting onto a stock is a typical and mingled issue in cutting stock problems. The traditional methods seem to be not intelligent enough to solve the problem due to the possibility of having combinations. A compact neighbourhood algorithm (CNA) that relates the number of neighbours and the sharing space between them is proposed. Based on the results of CNA, we can now define a universal compact yield (UCY) of stock for a specific pattern or cluster on a scientific basis. It also provides necessary geometrical hints of the subsequent nesting process with the consideration of various types of layout.     

Optimization‐based stability analysis of structures under unilateral constraints

This paper discusses an optimization‐based technique for determining the stability of a given equilibrium point of the unilaterally constrained structural system, which is subjected to the static load. We deal with the three problems in mechanics sharing the common mathematical properties: (i) structures containing no‐compression cables; (ii) frictionless contacts; and (iii) elastic–plastic trusses with non‐negative hardening. It is shown that the stability of a given equilibrium point of these structures can be determined by solving a maximization problem of a convex function over a convex set. On the basis of the difference of convex functions optimization, we propose an algorithm to solve the stability determination problem, at each iteration of which a second‐order cone programming problem is to be solved. The problems presented are solved for various structures to determine the stability of given equilibrium points. Copyright © 2008 John Wiley & Sons, Ltd.     

Co-evolutionary algorithm: An efficient approach for bilevel programming problems

The bilevel programming problem involves two optimization problems, which is hierarchical, strongly NP-hard and very challenging for most existing optimization approaches. An efficient universal co-evolutionary algorithm is developed in this article to deal with various bilevel programming problems. In the proposed algorithm, evolutionary algorithms are used to explore the leader's and the follower's decision-making spaces interactively. Unlike other existing approaches, in the suggested procedure the follower's problem is solved in two phases. First, an evolutionary algorithm is run for a few generations to obtain an approximation of lower level solutions. In the second phase, from all approximate solutions obtained above, only a small number of good points are selected and evolved again by a newly designed multi-criteria evolutionary algorithm. The technique refines some candidate solutions and can efficiently reduce the computational cost of obtaining feasible solutions. Proof-of-principle experiments demonstrate the efficiency of the proposed approach.