A reactive GRASP and path relinking for a combined production–distribution problem

An NP-hard production–distribution problem for one product over a multi-period horizon is investigated. The aim is to minimize total cost taking production setups, inventory levels and distribution into account. An integer linear model is proposed as a compact problem specification but it cannot be solved to optimality for large instances. Instead of using a classical two-phase approach (production planning and then route construction for each day), metaheuristics that simultaneously tackle production and routing decisions are developed: a GRASP (greedy randomized adaptive search procedure) and two improved versions using either a reactive mechanism or a path-relinking process. These algorithms are evaluated on 90 randomly generated instances with 50, 100 and 200 customers and 20 periods. The results confirm the interest of integrating production and distribution decisions, compared to classical two-phase methods. Moreover, reaction and path-relinking give better results than the GRASP alone.     

On convexified robust control synthesis

In this paper, we study a convexified robust control problem and its relation to gain-scheduling control. It reveals that the robust control synthesis condition becomes convex under a special plant structure. Moreover, for this class of robust control problems, the gain-scheduling control approach from scaled small-gain theorem will not provide any performance improvement over convexified robust controllers. Based on this observation, a convexified robust control synthesis framework with stringent performance and computational efficacy is proposed.     

Characterizations of context-sensitive languages and other language classes in terms of symport/antiport P systems

We give “syntactic’’ characterizations of context-sensitive languages (CSLs) in terms of some restricted models of symport/antiport P systems. These are the first such characterizations of CSLs in terms of P systems. In particular, we show the following for any language L over a binary alphabet:

(1)

Let m   be any integer ≥1$\ge 1$. Then L is a CSL if and only if it can be accepted by a restricted symport/antiport P system with m membranes and multiple number of symbols (objects). Moreover, holding the number of membranes at m, there is an infinite hierarchy in computational power (within the class of binary CSLs) with respect to the number of symbols.     

A simple approach for robust economic design of control charts

Robust economic control chart designs incorporating robustness with regard to the uncertainty in the cost and process parameters, reported in the literature, fall into two broad categories. One category of robust economic designs considers the uncertainty in the estimates of the cost and process parameters and makes the design suitable to operate well in any scenario arising out of the uncertainty. Other category of robust economic design considers different possible discrete scenarios for a single process and makes the control chart robust to all possible scenarios. The approach suggested for the robust designs for each category is different and somewhat complicated. The present paper proposes a design approach by introducing a simple statistic for the robust economic design problem of a ‘process with multiple scenarios’. SGA has been used in the optimization. The suggested statistic handles the information regarding the relative frequency of occurrence of each scenario of the process for the robust designs.     

On the formulation and solution of robust performance problems

A new approach to robust performance problems is proposed in this paper. The approach involves the optimisation of the so-called performance weights subject to a constraint, formulated in terms of the structured singular value, which ensures the existence of a stabilising feedback compensator that achieves robust performance with respect to the optimised performance weights and an uncertain plant set. Optimising over the performance weights in this way gives rise to an algorithm for systematically trading-off desired performance against specified plant uncertainty and performance limitations due to plant dynamics. The algorithm also yields a robust controller. The designer is only required to specify the uncertain plant set and an optimisation directionality, which is used to reflect desired closed-loop performance over frequency in terms of a corresponding cost function. Design of this directionality appears to be simpler than designing the performance weights directly.     

Steelmaking technology for a sustainable society

For the reduction of CO₂ emission, two major developments are being conducted in COURSE50 (“ CO₂ Ultimate Reduction in the Steelmaking Process by Innovative Technologies for Cool Earth 50”). The one is separation of CO₂ gas from BFG (Blast Furnace Gas) and recharge of the rest of BFG including H₂ and CO into blast furnace. Hydrogen iron ore reduction technology is also going to be developed. The other one is amplification of H₂ gas from CH₄, for example, in COG (Coke Oven Gas). The produced hydrogen gas will be supplied to the society or the reformed COG will be charged to blast furnace. In addition to these drastic challenging technology developments, a variety of measures for CO₂ reduction is under taken. In the frame of Asia-Pacific Partnership on Clean Development and Climate, the best available technology for energy savings are discussed to be transferred within seven member nations, which has the effect of 1.27 million ton reduction of CO₂ emission a year. By supplying energy saving steel products to society such as high strength steels for automobiles and ships, which realizes the fuel consumption reduction, high performance electrical steels for motors and transformers, which realize electricity loss reduction, and by recycling waste city plastics and tires in steel processes for hydrogen gas generation, chemical raw material conversion and iron ore reduction, etc., it is expected that equivalent 10% reduction of CO₂ gas emission in steel production is counted. In steelmaking process the reduction of refining slags contributes materials use efficiency and less emission of unuseful byproducts. The control and utilization of nonmetallic inclusions, such as deoxidation products, are one of the key technology for obtaining product performance, which is required in the above-mentioned steel products. In order to optimize steelmaking process for these purposes, computational thermodynamics is applied. Optimization of demanganization, and control of chemical composition of nonmetallic inclusions by the use of computational thermodynamics are mentioned.     

Makespan minimization on single batch-processing machine via ant colony optimization

This paper investigates the problem of minimizing makespan on a single batch-processing machine, and the machine can process multiple jobs simultaneously. Each job is characterized by release time, processing time, and job size. We established a mixed integer programming model and proposed a valid lower bound for this problem. By introducing a definition of waste and idle space (WIS), this problem is proven to be equivalent to minimizing the WIS for the schedule. Since the problem is NP-hard, we proposed a heuristic and an ant colony optimization (ACO) algorithm based on the theorems presented. A candidate list strategy and a new method to construct heuristic information were introduced for the ACO approach to achieve a satisfactory solution in a reasonable computational time. Through extensive computational experiments, appropriate ACO parameter values were chosen and the effectiveness of the proposed algorithms was evaluated by solution quality and run time. The results showed that the ACO algorithm combined with the candidate list was more robust and consistently outperformed genetic algorithm (GA), CPLEX, and the other two heuristics, especially for large job instances.     

Vector transport for shape-from-shading

In this paper we describe a new shape-from-shading method. We show how the parallel transport of surface normals can be used to impose curvature consistency and also to iteratively update surface normal directions so as to improve the brightness error. We commence by showing how to make local estimates of the Hessian matrix from surface normal information. With the local Hessian matrix to hand, we develop an “EM-like” algorithm for updating the surface normal directions. At each image location, parallel transport is applied to the neighbouring surface normals to generate a sample of local surface orientation predictions. From this sample, a local weighted estimate of the image brightness is made. The transported surface normal which gives the brightness prediction which is closest to this value is selected as the revised estimate of surface orientation. The revised surface normals obtained in this way may in turn be used to re-estimate the Hessian matrix, and the process iterated until stability is reached. We experiment with the method on a variety of real world and synthetic data. Here we explore the properties of the fields of surface normals and the height data delivered by the method.     

Likelihood-free Bayesian estimation of multivariate quantile distributions

In this paper, we present new multivariate quantile distributions and utilise likelihood-free Bayesian algorithms for inferring the parameters. In particular, we apply a sequential Monte Carlo (SMC) algorithm that is adaptive in nature and requires very little tuning compared with other approximate Bayesian computation algorithms. Furthermore, we present a framework for the development of multivariate quantile distributions based on a copula. We consider bivariate and time series extensions of the g-and-k distribution under this framework, and develop an efficient component-wise updating scheme free of likelihood functions to be used within the SMC algorithm. In addition, we trial the set of octiles as summary statistics as well as functions of these that form robust measures of location, scale, skewness and kurtosis. We show that these modifications lead to reasonably precise inferences that are more closely comparable to computationally intensive likelihood-based inference. We apply the quantile distributions and algorithms to simulated data and an example involving daily exchange rate returns.     

Multivariate time series modeling and classification via hierarchical VAR mixtures

A novel class of models for multivariate time series is presented. We consider hierarchical mixture-of-expert (HME) models in which the experts, or building blocks of the model, are vector autoregressions (VAR). It is assumed that the VAR-HME model partitions the covariate space, specifically including time as a covariate, into overlapping regions called overlays. In each overlay a given number of VAR experts compete with each other so that the most suitable one for the overlay is favored by a large weight. The weights have a particular parametric form that allows the modeler to include relevant covariates. Estimation of the model parameters is achieved via the EM (expectation-maximization) algorithm. A new algorithm to select the optimal number of overlays, the number of VAR models and the model orders of the VARs that define a particular VAR-HME model configuration, is also developed. The algorithm uses the Bayesian information criterion (BIC) as an optimality criterion. Issues of model checking and inference of latent structure in multiple time series are investigated. The new methodology is illustrated by analyzing a synthetic data set and a 7-channel electroencephalogram data set.     

On a new NBUE property in multivariate sense: An application

Since multivariate lifetime data frequently occur in applications, various properties of multivariate distributions have been previously considered to model and describe the main concepts of aging commonly considered in the univariate setting. The generalization of univariate aging notions to the multivariate case involves, among other factors, appropriate definitions of multivariate quantiles and related notions, which are able to correctly describe the intrinsic characteristics of the concepts of aging that should be generalized, and which provide useful tools in the applications. A new multivariate version of the well-known New Better than Used in Expectation univariate aging notion is provided, by means of the concepts of the upper corrected orthant and multivariate excess-wealth function. Some of its properties are described, with particular attention paid to those that can be useful in the analysis of real data sets. Finally, through an example it is illustrated how the new multivariate aging notion influences the final results in the analysis of data on tumor growth from the Comprehensive Cohort Study performed by the German Breast Cancer Study Group.     

Online computation with advice

We consider a model for online computation in which the online algorithm receives, together with each request, some information regarding the future, referred to as advice. The advice is a function, defined by the online algorithm, of the whole request sequence. The advice provided to the online algorithm may allow an improvement in its performance, compared to the classical model of complete lack of information regarding the future. We are interested in the impact of such advice on the competitive ratio, and in particular, in the relation between the size b of the advice, measured in terms of bits of information per request, and the (improved) competitive ratio. Since b=0 corresponds to the classical online model, and b=⌈log∣A∣⌉, where A is the algorithm’s action space, corresponds to the optimal (offline) one, our model spans a spectrum of settings ranging from classical online algorithms to offline ones.In this paper we propose the above model and illustrate its applicability by considering two of the most extensively studied online problems, namely, metrical task systems (MTS) and the k-server problem. For MTS we establish tight (up to constant factors) upper and lower bounds on the competitive ratio of deterministic and randomized online algorithms with advice for any choice of 1≤b≤Θ(logn), where n is the number of states in the system: we prove that any randomized online algorithm for MTS has competitive ratio Ω(log(n)/b) and we present a deterministic online algorithm for MTS with competitive ratio O(log(n)/b). For the k-server problem we construct a deterministic online algorithm for general metric spaces with competitive ratio k^O(1/b) for any choice of Θ(1)≤b≤logk.     

A simple and efficient method for variable ranking according to their usefulness for learning

The selection of a subset of input variables is often based on the previous construction of a ranking to order the variables according to a given criterion of relevancy. The objective is then to linearize the search, estimating the quality of subsets containing the topmost ranked variables. An algorithm devised to rank input variables according to their usefulness in the context of a learning task is presented. This algorithm is the result of a combination of simple and classical techniques, like correlation and orthogonalization, which allow the construction of a fast algorithm that also deals explicitly with redundancy. Additionally, the proposed ranker is endowed with a simple polynomial expansion of the input variables to cope with nonlinear problems. The comparison with some state-of-the-art rankers showed that this combination of simple components is able to yield high-quality rankings of input variables. The experimental validation is made on a wide range of artificial data sets and the quality of the rankings is assessed using a ROC-inspired setting, to avoid biased estimations due to any particular learning algorithm.