Search results for "optimization"
showing 10 items of 2824 documents
Solving a class of fuzzy linear programs by using semi-infinite programming techniques
2004
This paper deals with a class of Fuzzy Linear Programming problems characterized by the fact that the coefficients in the constraints are modeled as LR-fuzzy numbers with different shapes. Solving such problems is usually more complicated than finding a solution when all the fuzzy coefficients have the same shape. We propose a primal semi-infinite algorithm as a valuable tool for solving this class of Fuzzy Linear programs and, we illustrate it by means of several examples.
Branch-and-Cut
2010
This chapter focuses on the approach for solving the LOP to optimality which can currently be seen as the most successful one. It is a branch-and-bound algorithm, where the upper bounds are computed using linear programming relax- ations.
A Stochastic Soft Constraints Fuzzy Model for a Portfolio Selection Problem
2006
The financial market behavior is affected by several non-probabilistic factors such as vagueness and ambiguity. In this paper we develop a multistage stochastic soft constraints fuzzy program with recourse in order to capture both uncertainty and imprecision as well as to solve a portfolio management problem. The results we obtained confirm the studies carried out in literature addressed to integrate stochastic and possibilistic programming.
Lower and upper bounds for the mixed capacitated arc routing problem
2006
This paper presents a linear formulation, valid inequalities, and a lower bounding procedure for the mixed capacitated arc routing problem (MCARP). Moreover, three constructive heuristics and a memetic algorithm are described. Lower and upper bounds have been compared on two sets of randomly generated instances. Computational results show that the average gaps between lower and upper bounds are 0.51% and 0.33%, respectively.
Constrained Clusterwise Linear Regression
2005
In market segmentation, Conjoint Analysis is often used to estimate the importance of a product attributes at the level of each single customer, clustering, successively, the customers whose behavior can be considered similar. The preference model parameter estimation is made considering data (usually opinions) of a single customer at a time, but these data are usually very few as each customer is called to express his opinion about a small number of different products (in order to simplify his/her work). In the present paper a Constrained Clusterwise Linear Regression algorithm is presented, that allows simultaneously to estimate parameters and to cluster customers, using, for the estimati…
Hydropower Optimization Using Deep Learning
2019
This paper demonstrates how deep learning can be used to find optimal reservoir operating policies in hydropower river systems. The method that we propose is based on the implicit stochastic optimization (ISO) framework, using direct policy search methods combined with deep neural networks (DNN). The findings from a real-world two-reservoir hydropower system in southern Norway suggest that DNNs can learn how to map input (price, inflow, starting reservoir levels) to the optimal production pattern directly. Due to the speed of evaluating the DNN, this approach is from an operational standpoint computationally inexpensive and may potentially address the long-standing problem of high dimension…
A Quick Simulation Technique for a Fluid Information Storage Problem
2001
Summary In this paper we present an application of Importance Sampling (IS) for quick simulation of buffer overflow probability in a statistical multiplexer loaded with a number of independent Markov modulated fluid sources. Runtime improvement is deducible from NMCσ2(p) and NISσ2(p*) that characterize the trade-offs between sample size and variance of the estimators of buffer overflow probability experienced in Monte Carlo (MC) and Importance Sampling simulations. By assuming that the same precision is achieved for the two kinds of simulations if σ2(p)=σ2(p*), an approximate closed form expression for the ratio NIS/NMC is derived, and it is minimized with respect to the load of the multipl…
Unbiased Branches: An Open Problem
2007
The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 in…
Self-stabilizing Balls & Bins in Batches
2016
A fundamental problem in distributed computing is the distribution of requests to a set of uniform servers without a centralized controller. Classically, such problems are modelled as static balls into bins processes, where m balls (tasks) are to be distributed to n bins (servers). In a seminal work, [Azar et al.; JoC'99] proposed the sequential strategy Greedy[d] for n = m. When thrown, a ball queries the load of d random bins and is allocated to a least loaded of these. [Azar et al.; JoC'99] showed that d=2 yields an exponential improvement compared to d=1. [Berenbrink et al.; JoC'06] extended this to m ⇒ n, showing that the maximal load difference is independent of m for d=2 (in contrast…
A parsimonious model for generating arbitrage-free scenario trees
2016
Simulation models of economic, financial and business risk factors are widely used to assess risks and support decision-making. Extensive literature on scenario generation methods aims at describing some underlying stochastic processes with the least number of scenarios to overcome the ‘curse of dimensionality’. There is, however, an important requirement that is usually overlooked when one departs from the application domain of security pricing: the no-arbitrage condition. We formulate a moment matching model to generate multi-factor scenario trees for stochastic optimization satisfying no-arbitrage restrictions with a minimal number of scenarios and without any distributional assumptions.…