Search results for " optimization"

Large‐scale set partitioning problems: Some real‐world instances hide a beneficial structure

2006

In this paper we consider large‐scale set partitioning problems. Our main purpose is to show that real‐world set partitioning problems originating from the container‐trucking industry are easier to tackle in respect to general ones. We show such different behavior through computational experiments: in particular, we have applied both a heuristic algorithm and some exact solution approaches to real‐world instances as well as to benchmark instances from Beasley OR‐library. Moreover, in order to gain an insight into the structure of the real‐world instances, we have performed and evaluated various instance perturbations. Didelės matematinės aibės dalijimo problemų sprendimas, nagrinėjant reali…

Black-Box solvers in combinatorial optimization

2015

Black box optimizers have a long tradition in the field of operations research. These procedures treat the objective function evaluation as a black box and therefore do not take advantage of its specific structure. Black-box optimization refers to the process in which there is a complete separation between the evaluation of the objective function —and perhaps other functions used to enforce constraints— and the solution procedure. The challenge of optimizing black boxes is to develop methods that can produce outcomes of reasonable quality without taking advantage of problem structure and employing a computational effort that is adequate for the context.

A multi-objective approach to facility layout problem by genetic search algorithm and Electre method

2006

Abstract Classical approaches to layout design problem tend to maximise the efficiency of layout, measured by the handling cost related to the interdepartmental flow and to the distance among the departments. However, the actual problem involves several conflicting objectives hence requiring a multi-objective formulation. Multi-objective approaches, recently proposed, in most cases lead to the maximisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and of quantifying the weights. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the P…

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms

2014

The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link. The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts…

A Unified Approach to Portfolio Optimization with Linear Transaction Costs

2004

In this paper we study the continuous time optimal portfolio selection problem for an investor with a finite horizon who maximizes expected utility of terminal wealth and faces transaction costs in the capital market. It is well known that, depending on a particular structure of transaction costs, such a problem is formulated and solved within either stochastic singular control or stochastic impulse control framework. In this paper we propose a unified framework, which generalizes the contemporary approaches and is capable to deal with any problem where transaction costs are a linear/piecewise-linear function of the volume of trade. We also discuss some methods for solving numerically the p…

An efficient framework for the elasto-plastic reliability assessment of uncertain wind excited systems

2016

Abstract In this paper a method to efficiently evaluate the reliability of elastic-perfectly plastic structures is proposed. The method is based on combining dynamic shakedown theory with Subset Simulation. In particular, focus is on describing the shakedown behavior of uncertain elasto-plastic systems driven by stochastic wind loads. The ability of the structure to shakedown is assumed as a limit state separating plastic collapse from a safe, if not elastic, state of the structure. The limit state is therefore evaluated in terms of a probabilistic load multiplier estimated through solving a series of linear programming problems posed in terms of the responses of the underlying linear elast…

Up-to-Date Supply Chain Management: The Coordinated (S, R) Order-Up-to

2011

This paper presents the mathematical derivation of a new generation of the most largely used periodic review policy in supply chain: the coordinated (S, R) replenishment rule. We first derive the classical order-up-to model and then we modify it to generate the coordinated decision policy equations. We run a numerical simulation on a serial supply chain model to show differences in the two policies. We conclude on the managerial implications related to coordinated replenishment.

2014

For locating inaccurate problem of the discrete localization criterion proposed by Demigny, a new criterion expression of “good localization” is proposed. Firstly, a discrete expression of good detection and good localization criterion of two dimension edge detection operator is employed, and then an experiment to measure optimal parameters of two dimension Canny's edge detection operator is introduced after. Moreover, a detailed performance comparison and analysis of two dimension optimal filter obtained via utilizing tensor product for one dimension optimal filter are provided which can prove that least square support vector regression (LS-SVR) is a smoothness filter and give the construc…

Multi-dimensional Function Approximation and Regression Estimation

2002

In this communication, we generalize the Support Vector Machines (SVM) for regression estimation and function approximation to multi-dimensional problems. We propose a multi-dimensional Support Vector Regressor (MSVR) that uses a cost function with a hyperspherical insensitive zone, capable of obtaining better predictions than using an SVM independently for each dimension. The resolution of the MSVR is achieved by an iterative procedure over the Karush-Kuhn-Tucker conditions. The proposed algorithm is illustrated by computers experiments.

Training label cleaning with ant colony optimization for classification of remote sensing imagery

2015

This paper presents an original approach for improving performances of the supervised classifiers in remote sensing imagery by proposing a technique to refine a given training set using Ant Colony Optimization (ACO). The new method called ACO-Training Label Cleaning (ACO-TLC) applies ACO model for selection of the significant training samples from a given set of labeled vectors in order to optimize the quality of a supervised classifier. This means to retain the most informative samples and to remove the uncertain or misclassified training samples, which lead to classification errors. As a result of the selection process, we can obtain a purified training set. The proposed model is implemen…