内蒙古石灰窑沟铁矿地质特征及找矿标志

石灰窑沟铁矿区位于赤峰市北西约80 km处，地处西拉木伦多金属成矿带的南侧，区域位于内蒙古地轴东段与内蒙海西褶皱带接触带，Ⅲ级构造单元为云雾山隆起的北东端和赤峰—铭山大断裂2条构造单元的分界线处，本矿床围岩蚀变具有分带性，是一个受断裂和褶皱复合控制的矽卡岩型矿床。为探讨该矿区的地质特征及找矿标志，通过近几年对本区地质勘查工作的总结分析，认为该矿床为矽卡岩型矿床，围绕构造发育部位，岩体与地层接触带及附近且与高精度磁测异常套合区是主要的找矿标志。     

Heuristics based server consolidation with residual resource defragmentation in cloud data centers

Server Consolidation is one of the foremost concerns associated with the effective management of a Cloud Data Center as it has the potential to accomplish significant reduction in the overall cost and energy consumption. Most of the existing works on Server Consolidation have focused only on reducing the number of active physical servers (PMs) using Virtual Machine (VM) Live Migration. But, along with reducing the number of active PMs, if a consolidation approach reduces residual resource fragmentation, the residual resources can be efficiently used for new VM allocations, or VM reallocations, and some future migrations can also be reduced. None of the existing works have explicitly focused on reducing residual resource fragmentation along with reducing the number of active PMs to the best of our knowledge. We propose RFAware Server Consolidation, a heuristics based server consolidation approach which performs residual resource defragmentation along with reducing the number of active PMs in cloud data centers.     

Bin packing and related problems: General arc-flow formulation with graph compression

We present an exact method, based on an arc-flow formulation with side constraints, for solving bin packing and cutting stock problems—including multi-constraint variants—by simply representing all the patterns in a very compact graph. Our method includes a graph compression algorithm that usually reduces the size of the underlying graph substantially without weakening the model.Our formulation is equivalent to Gilmore and Gomory׳s, thus providing a very strong linear relaxation. However, instead of using column-generation in an iterative process, the method constructs a graph, where paths from the source to the target node represent every valid packing pattern.The same method, without any problem-specific parameterization, was used to solve a large variety of instances from several different cutting and packing problems. In this paper, we deal with vector packing, bin packing, cutting stock, cardinality constrained bin packing, cutting stock with cutting knife limitation, bin packing with conflicts, and other problems. We report computational results obtained with many benchmark test datasets, some of them showing a large advantage of this formulation with respect to the traditional ones.     

Mathematical programming algorithms for bin packing problems with item fragmentation

In this paper we consider a class of bin packing problems from the literature having the following distinctive feature: items may be fragmented at a price. Problems of this kind arise in diverse application fields like logistics and telecommunications, and have already been extensively tackled from an approximation point of view. We focus on the case in which splitting produces no overhead, a fixed number of bins is given and the number of fragments or fragmentations needs to be minimized. We first investigate the theoretical properties of the problem. Then we elaborate on them to devise mathematical programming models and algorithms, yielding both exact optimization algorithms and effective heuristics. An extensive experimental campaign proves that our approach is very effective, and highlights which features make an instance computationally harder to solve.     

Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar.In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.     

长春市某高校间歇采暖节能潜力分析

针对学校建筑采暖负荷特点,采用能耗分析的方法分析研究采用间歇供暖的节能性.两种运行方式耗热量对比,建筑面积为10.6 W㎡的学校建筑,采用间歇采暖运行方式可以节省能量2 913 734 kWh,相当于节约标准煤556t,并减少水泵耗电31 691 kWh,相当于节省原来用能的11.7%.由于采用间歇采暖的运行方式使用小流量供暖,不但节约了燃料消耗量,减少了污染物排放,而且也显著降低了运行费用,是一种较为理想的节能方法.     

泌阳凹陷古城地区地震资料连片处理及认识

地震资料连片处理的目的是尽可能地克服不同年度、不同仪器、不同施工参数等造成的影响,同时消除不同的处理参数及处理流程所导致的差异。连片地震资料可以用于整体区域地震解释和地质认识。根据新老资料采集参数的特点,在静校正、叠前去噪、振幅补偿、叠前叠后提高分辨率等方面进行攻关,系统地解决了新老资料连片处理中的关键问题。通过处理资料的分析认为连片资料比较适合构造解释,而属性研究必须根据具体情况,一般是面元均化的网格与单块网格相比,网格差别大的,最好使用单块处理的资料,差别小的或者网格没有差别的,采用连片处理的资料进行研究。     

聚烯烃重力掺混料仓及其评价

介绍了聚烯烃掺混料仓的常用形式和工作原理,并对5种常用掺混料仓的特点和性能进行了综合评价,提出了掺混料仓选用的原则。     

New Bounds for Multidimensional Packing

Abstract. New upper and lower bounds are presented for a multidimensional generalization of bin packing called box packing. Several variants of this problem, including bounded space box packing, square packing, variable-sized box packing and resource augmented box packing are also studied. The main results, stated for d=2 , are as follows: a new upper bound of 2.66013 for online box packing, a new 14/9 + ɛ polynomial time offline approximation algorithm for square packing, a new upper bound of 2.43828 for online square packing, a new lower bound of 1.62176 for online square packing, a new lower bound of 2.28229 for bounded space online square packing and a new upper bound of 2.32571 for online two-sized box packing.     

Bounded Space On-Line Bin Packing: Best Is Better than First

We present a sequence of new linear-time, bounded-space, on-line bin packing algorithms, the K -Bounded Best Fit algorithms (BBF _K ). They are based on the Θ(n log n) Best Fit algorithm in much the same way as the Next-K Fit algorithms are based on the Θ(n log n) First Fit algorithm. Unlike the Next-K Fit algorithms, whose asymptotic worst-case ratios approach the limiting value of \frac 17 10 from above as K \rightarrow ∈fty but never reach it, these new algorithms have worst-case ratio \frac 17 10 for all K \geq 2 . They also have substantially better average performance than their bounded-space competition, as we have determined based on extensive experimental results summarized here for instances with item sizes drawn independently and uniformly from intervals of the form (0, u] , 0 < u ≤ 1 . Indeed, for each u < 1 , it appears that there exists a fixed memory bound K(u) such that BBF _K(u) obtains significantly better packings on average than does the First Fit algorithm, even though the latter requires unbounded storage and has a significantly greater running time. For u = 1 , BBF _K can still outperform First Fit (and essentially equal Best Fit) if K is allowed to grow slowly. We provide both theoretical and experimental results concerning the growth rates required. Received November 30, 1992; revised November 5, 1999.