基于列生成算法求解动态库存板坯分配问题

针对钢铁企业中遇到的动态库存板坯分配问题进行了研究。建立了一个0-1整数规划数学模型,该模型的目标是最小化板坯与合同规格差异费用以及板坯在库停留所产生的库存成本费用之和。根据问题特点,使用Danzig-Wolfe策略将这个模型分解为一个带有集划分约束的主问题和一个具有背包特征约束的价格子问题,开发了分支价格算法进行求解。计算结果表明所开发的分支价格算法能够最优求解生产实际问题。     

Optimal distributed linear averaging

A new optimal distributed linear averaging (ODLA) problem is presented in this paper. This problem is motivated by the distributed averaging problem which arises in the context of distributed algorithms in computer science and coordination of groups of autonomous agents in engineering. The aim of the ODLA problem is to compute the average of the initial values at nodes of a graph through an optimal distributed algorithm in which the nodes in the graph can only communicate with their neighbors. Optimality is given by a minimization problem of a quadratic cost functional under infinite horizon. We show that this problem has a very close relationship with the notion of semistability. By developing new necessary and sufficient conditions for semistability of linear discrete-time systems, we convert the proposed ODLA problem into an equivalent, constrained optimization problem and then derive a solvable, fixed-structure convex optimization problem.     

A bi-level programming for logistics network design with system-optimized flows

This paper proposes a bi-level programming for a logistics network design problem with system-optimized flows. We applied the Wardrop’s second principle to the logistics network design problem. A system-optimized logistics network design problem can be formulated as a bi-level program. For the system-optimized flows, a user equilibrium traffic assignment problem with marginal costs can be solved at the lower level problem. Due to the non-differentiability of the perturbed solutions in system-optimized flows, we present a novel solution algorithm to efficiently solve the logistics network design problem. By using the subgradients of the objective function, a new projection method is proposed with global convergence. Numerical calculations are implemented using a grid-size hypothetical network and comparisons are made with other alternatives in solving the logistics network design problem. Numerical results disclose that the proposed method has successful solved the logistics network design problem and achieved significant performance both in computational efficacy and cost reduction when compared to other alternatives.     

A New Genetic Algorithm for Loop Tiling

Tiling is a known problem especially in the field of computational geometry and its related engineering branches. In fact, a tile is a set of points in the Cartesian space. The goal is to partition the space of the points as tiles with optimal dimensions and shapes such that a number of predefined semantic relations holds amongst the tiles. So far, this problem has been solved in special cases with two or three dimensions. The problem of determining the optimal tile is an NP-Hard problem. Presenting a novel constraint genetic algorithm in this paper, we have been able to solve the tiling problem in Cartesian spaces with more than two dimensions, for the loop parallelization problem.     

The complexity of single machine scheduling with two distinct deadlines and identical decreasing rates of processing times

The makespan problem on a single machine for a set of tasks with two distinct deadlines and identical decreasing rates of processing times is considered in this paper. Ho et al. [1] have proposed a model of a task system in which the processing time of a task decreases with its starting time. When the decreasing rate is identical, the computational complexity of the makespan problem with two distinct deadlines is posed as an open problem. In this paper we show that the problem is NP-complete. It follows that both the corresponding flow time problem and maximum lateness problem are also NP-complete.     

相关工件车间调度问题的拓扑算法

针对加工装配型离散制造企业实际生产的特点,提出了一类用于表示工序之间偏序关系的相关工件车间调度问题。为了利用已有的求解表示工序之间的线序关系的传统车间调度算法求解相关工件车间调度问题,设计了一种拓扑算法,该算法能够将工序之间的偏序关系转化为线序关系,将相关工件车间调度问题转化为传统的车间调度问题,通过实证研究,结果表明了拓扑算法是可行和高效的。     

Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search

In this study, we investigate the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering association rules time-windows (ARTW). We formulate the problem as a mathematical model using integer programming that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighbourhood search (VNS) algorithm is developed to solve the problem with near-optimal solutions. Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger time-coverage rate than the lower bounds, thus laying a good foundation for mining ARTW.     

正弦曲线三点拟合问题的一种新方法

根据三个样本点拟合一条正弦曲线是计算机仿真和信号测量中的一个基本问题。目前流行的方法是最小二乘法。这种问题是一个三维以上的非线性最小化问题,计算规模与拟合具有三个参数一般曲线的计算规模相当。根据正弦曲线对应于某平面与某圆柱面交线的规律,提出了一种新的用于信号测量的正弦曲线拟合方法,它将原问题转化成为一个关于周期的单变量函数的最小化问题。新方法几何意义明确,算法实现简洁,结果可靠。     

集装箱装入问题的研究

集装箱装载问题是一种有广泛应用背景的组合优化问题,它属于NP-hard问题。禁忌搜索算法(TS)是求解组合问题的一种主要方法,有很强的全局搜索能力。集装箱装入属于有多种约束的空间资源优化问题。约束条件多,求解困难。根据同类型货物一次性装载的思想,提出了一种新的基于空间划分的启发式算法。     

The Machine Duplication Problem in a Job Shop with Two Jobs

This paper addresses a problem related to the classical job shop scheduling problem with two jobs. The problem consists in concurrently determining the best subset of machines to be duplicated and the optimal scheduling of the operations in order to minimize completion time. Such a problem arises in the tool management for a class of flexible manufacturing cells. The job shop with two jobs is first reviewed, the application of the classical search algorithm A* to this problem is discussed and its performance compared with a previous approach. The complexity of the machine duplication problem is then analysed. The problem is proved to be in general NP-hard in the strong sense, but in a class of special cases, relevant from the applications viewpoint, it can be solved in polynomial time by a dynamic programming algorithm. A heuristic based on such an algorithm and on A* is proposed for the general problem; the results are satisfactory in terms of both efficiency and quality of the solution.     

A Cut-Based Algorithm for the Nonlinear Dual of the Minimum Cost Network Flow Problem

We consider a convex, or nonlinear, separable minimization problem with constraints that are dual to the minimum cost network flow problem. We show how to reduce this problem to a polynomial number of minimum s,t-cut problems. The solution of the reduced problem utilizes the technique for solving integer programs on monotone inequalities in three variables, and a so-called proximity-scaling technique that reduces a convex problem to its linear objective counterpart. The problem is solved in this case in a logarithmic number of calls, O(log U), to a minimum cut procedure, where U is the range of the variables. For a convex problem on n variables the minimum cut is solved on a graph with O(n²) nodes. Among the consequences of this result is a new cut-based scaling algorithm for the minimum cost network flow problem. When the objective function is an arbitrary nonlinear function we demonstrate that this constrained problem is solved in pseudopolynomial time by applying a minimum cut procedure to a graph on O(nU) nodes.     

Dominant pole and eigenstructure assignment for positive systems with state feedback

In this paper, the dominant pole assignment problem, the dominant eigenstructure assignment problem and the robust dominant pole assignment problem for linear time-invariant positive systems with state feedback are considered. The dominant pole assignment problem is formulated as a linear programming problem, and the dominant eigenstructure problem is formulated as a quasiconvex optimisation problem with linear constraints. The robust dominant pole assignment problem is formulated as a non-convex optimisation problem with non-linear constraints which is solved using particle swarm optimisation (PSO) with an efficient scheme which employs the dominant eigenstructure assignment technique to accelerate the convergence of the PSO procedure. Each of the three problems can be further constrained by requiring that the controller has a pre-specified structure, or the gain matrix have both elementwise upper and lower bounds. These constraints can be incorporated into the proposed scheme without increasing the complexity of the algorithms. Both the continuous-time case and the discrete-time case are treated in the paper.     

Computing near-optimal solutions for the dominating subset with minimal weight problem

In this work, we consider a combinatorial “dominating subset with minimal weight” problem, which is an associative problem for solving global optimization problem. This problem can be expressed as a kind of assignment problem. The mathematical model and the economical interpretations of the problem are given and its properties are described. Then, we propose a new algorithm which has a ratio bound in polynomial time, by using above properties for solving the problem and present the results of numerical experiments.     

An enhanced timetabling procedure for the no-wait job shop problem: a complete local search approach

In this paper we consider the no-wait job shop problem with a makespan objective. This problem has usually been addressed by its decomposition into a sequencing and a timetabling problem. Here, first we focus on the timetabling problem and take advantage of the symmetry of the problem in order to suggest a new timetabling procedure. Secondly, we suggest embedding this timetabling into a recent metaheuristic named complete local search with memory.     

Multi-level qualitative reasoning applied to CMOS digital circuits

This paper develops a general framework for efficient problem solving at multiple levels of abstraction in complex systems. The goal is to address two important control issues in the behavior generation process: (i) the selection problem, which deals with the right level of detail to solve a problem; and (ii) the efficiency problem, which deals with information transfer from higher levels of abstraction to focus problem solving at more detailed levels. A problem solver has been developed for CMOS circuit analysis. This problem solver produces solution traces that integrates information from various levels, and uses information derived at more abstract levels to guide problem solving at more detailed levels. The next step is to apply this problem solver to a wide variety of tasks such as integrated circuit design and diagnosis.     

Scheduling collection of recyclable material at Northern Illinois University campus using a two-phase algorithm

This paper considers the problem of collecting recyclable materials in a large university campus with many buildings. It models the problem as a vehicle routing problem with additional constraints and applies a two-phase algorithm in order to provide a heuristic solution.     

Defuzzification as a special way of dealing with retranslation

The problem of defuzzification is examined in this paper from a broader perspective as a special way of dealing with the general problem of retranslation. The paper includes an overview of different formulations of the problem of defuzzification, as well as an overview of methods that have been suggested in the literature for dealing with the problem. Our own approach to defuzzification, which is described in the paper in more details, is based on relevant measures of uncertainty-based information. This paper is a companion to our recent paper that addresses the general problem of retranslation in computing with perceptions (Martin and Klir 2006).     

过程间并发程序分析不可判定的一个新证明方法

过程间并发程序分析问题是一个不可判定问题,理解这个不可判定问题的来源是发展一个有效的分析算法的基础.现有的证明[1]通过构造三个并发任务的PCP问题实例,证明过程间并发程序分析是一个不可判定问题.利用反射的思想,仅仅用两个并发任务构造该问题的一个PCP问题实例,证明在两个并发任务的情况下,过程间并发程序分析是一个不可判定问题.     

Speed tracking control of PM synchronous motor by internal model design

In this article, we consider a speed tracking and load torque disturbance rejection problem of PM synchronous motor by internal model design. The problem is first formulated as a global robust output regulation problem of a special class of multivariable systems. Then the output regulation problem is further converted into a global stabilisation problem of an augmented system composed of the original plant and an internal model. As the augmented system does not take any known special form, we have developed a specific tool to deal with the stabilisation problem. In particular, a generalised changing supply function technique applicable to non-input-to-state stable (ISS) systems is developed. This technique, in conjunction with a particular nonlinear internal model, leads to an effective solution to the problem.     

QPSO算法在训练支持向量机中的应用

训练支持向量机的本质问题就是求解二次规划问题，但对大规模的训练样本来说，求解二次规划问题困难很大。遗传算法和粒子群算法等智能搜索技术可以在较少的时间开销内给出问题的近似解。量子粒子群优化（QPSO）算法是在经典的微粒群算法的基础上所提出的一种有较高收敛性和稳定性的进化算法。将操作简单而收敛快速的QPSO算法运用于训练支持向量机，优化求解二次规划问题，为解决大规模的二次规划问题开辟了一条新的途径。