Local Search Algorithms for the Bin Packing Problem and Their Relationships to Various Construction Heuristics

The tradeoff between the speed and quality of the solutions obtained by various construction and local search algorithms for the elementary bin packing problem (BPP) are analyzed to obtain useful information for designing algorithms for real-world problems that can be modeled as BPPs. On the basis of intensive computational experiments, we observe that the framework of a solution (i.e., a part of a solution consisting of large items or items with tight constraints) should be constructed in the early stages of a local search. New local search algorithms are proposed as empirical support for the observation.     

Book review of advances in gabor analysis,applied and numerical harmonic analysis,Hans G. Feichtinger and Thomas Strohmer (Eds.), Birkhäuser,Boston-Basel-Berlin, 2002, 356 pages. ISBN 0-8176-4239-0

In some recent publications it was shown that certain stationary stochastic dynamic programming problems with general state and action spaces can be solved by generalized linear programming. It Is the main aim of the present paper to demonstrate that a similar linear programming approach is feasible even in the non-stationary case. For this end, we formulate a programming problem (D^?) and show that (D^?) is equivalent to the problem of finding a p=optimal policy for the stochastic dynamic program, whereas a modification of (D^?) turns out to be the dual program of a pair of general linear programs.     

An improved method on group decision making based on interval-valued intuitionistic fuzzy prioritized operators

Interval-valued intuitionistic fuzzy prioritized operators are widely used in group decision making under uncertain environment due to its flexibility to model uncertain information. However, there is a shortcoming in the existing aggregation operators (interval-valued intuitionistic fuzzy prioritized weighted average (IVIFPWA)) to deal with group decision making in some extreme situations. For example, when an expert gives an absolute negative evaluation, the operators could lead to irrational results, so that they are not effectively enough to handle group decision making. In this paper, several examples are illustrated to show the unreasonable results in some of these situations. Actually, these unreasonable cases are common for operators in dealing with product averaging, not only emerging in IVIFPWA operators. To overcome the shortcoming of these kinds of operators, an improvement of making slight adjustment on initial evaluations is provided. Numerical examples are used to show the efficiency of the improvement.     

Fuzzy prioritized operators and their application to multiple attribute group decision making

In this paper, we investigate the triangular fuzzy multiple attribute group decision making (MAGDM) problem in which the attributes and experts are in different priority level. Motivated by the ideal of prioritized aggregation operators (R.R. Yager, Prioritized aggregation operators, International Journal of Approximate Reasoning 48 (2008) 263–274.), we develop some prioritized aggregation operators for aggregating triangular fuzzy information, and then apply them to develop some models for triangular fuzzy multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

This paper proposes a novel approach for the estimation of spectroscopic data by combining the predictions of an ensemble of estimators using the induced ordered weighted averaging (IOWA) fusion operators. For ensemble generation, we use Gaussian process regression (GPR) and extreme learning machine (ELM) estimators associated with different kernels. To render the model selection issue of ELM as efficiently as in the GPR Bayesian estimation method, we develop an automatic solution based on the powerful differential evolution (DE) algorithm. During the fusion process, the IOWA operator needs two things: (1) an order‐inducing value; and (2) a way to determine its weights. For the order‐inducing value, we propose to use the residual of each estimated output value. Because we cannot compute the true residual, we explore the idea of estimating the residuals themselves by associating to each estimator of the ensemble a second estimator of the same kind called a residual estimator. To learn the weights associated with these nonlinear operators, the proposed method relies on the concept of prioritized aggregation, where we generate the weights directly from the estimated residuals. Experimental results obtained on three real spectroscopic datasets confirm the interesting capabilities of the proposed IOWA fusion method. Copyright © 2013 John Wiley & Sons, Ltd.     

基于准则间关系的多准则语言决策方法

在多准则语言决策问题中,准则间的关系一般有三种:相互独立、相互影响、优先关系.基于这三种关系,结合比例二元组语义信息提出了一种新的多准则语言决策方法.介绍了比例二元组优先加权平均(P2TPWA)算子,并讨论了有关P2TPWA算子的相关性质.另外,将Choquet积分拓展到了比例二元组语言环境,并将P2TPWA算子和比例二元组语言Choquet积分用于实际的决策问题中.     

基于双边犹豫模糊非均衡语言优先集成算子的应急响应预案评估方法

在应用多属性决策理论求解应急响应预案评估问题时,问题结构的复杂性往往使决策者评价信息存在高度不确定性且属性相对重要性仅能以优先级关系来表征。为此,本文首先提出了双边犹豫模糊非均衡语言集这种新型信息形式以使决策者能够灵活有效的表征复杂评价信息,并定义了运算法则、熵和距离测度;其次,基于熵测度开发了双边犹豫模糊非均衡语言优先加权集成算子,并构建了能够考虑属性优先关系的多属性决策方法;进一步针对属性相对重要性不能由定性分析获得的情况,设计了客观权重确定方法,并构建了另一种更具实际灵活性的VIKOR决策方法;最后,实例研究表明了方法的有效性与优势。     

Stability and optimization of a production inventory system under prioritized base-stock control

Email: dongping.song{at}plymouth.ac.uk Received on 19 July 2007. Accepted on 1 May 2008. A make-to-stock system producing two part-types with stochasticprocessing times and random demands is considered. A prioritizedbase-stock policy is used to control the production to meetexogenous Poisson demands, where the unmet high-priority demandsare partially backordered due to their limited patience andthe unmet low-priority demands are fully backordered. Basedon the matrix analytical method, a necessary and sufficientcondition for the stability of the system is provided. The explicitstationary distribution is derived using the spectral expansionapproach. Interesting steady-state performance measures suchas stock-out probabilities and lost-sale fraction are then calculated.By investigating the structural properties of the objectivefunctions, simple procedures are presented to find the optimalbase-stock levels either to minimize the expected cost or tosatisfy the stock-out probability and the lost-sale fractionconstraints. In addition, the optimization problem with respectto the maximum backlog level for part-type one and two base-stocklevels is addressed and a solution procedure is presented. Numericalexamples are given to demonstrate the results.