Rank Properties in Finite Semigroups II: The Small Rank and the Large Rank

This paper is concerned with the application of two possible definitions of rank to certain well-known semigroups.AMS Subject Classification (2000), 20M10     

Energetic and structural characterization of 2‐R‐3‐methylquinoxaline‐1,4‐dioxides (R = benzoyl or tert‐butoxycarbonyl): experimental and computational studies

The gaseous standard molar enthalpies of formation of two 2‐R‐3‐methylquinoxaline‐1,4‐dioxides (R = benzoyl or tert‐butoxycarbonyl), at T = 298.15 K, were derived using the values for the enthalpies of formation of the compounds in the condensed phase, measured by static bomb combustion calorimetry, and for the enthalpies of sublimation, measured by Knudsen effusion, using a quartz crystal oscillator. The three dimensional structure of 2‐tert‐butoxycarbonyl‐3‐methylquinoxaline‐1,4‐dioxide has been obtained by X‐ray crystallography showing that the two N? O bond lengths in this compound are identical. The experimentally determined geometry in the crystal is similar to that obtained in the gas‐phase after computations performed at the B3LYP/6‐311 + G(2d,2p) level of theory. The experimental and computational results reported allow to extend the discussion about the influence of the molecular structure on the dissociation enthalpy of the N? O bonds for quinoxaline 1,4‐dioxide derivatives. As found previously, similar N? O bond lengths in quinoxaline‐1,4‐dioxide compounds are not linked with N? O bonds having the same strength. Copyright © 2007 John Wiley & Sons, Ltd.     

A hybrid heuristic for a multi-objective real-life car sequencing problem with painting and assembly line constraints

We address a multi-objective version of the car sequencing problem, which consists in sequencing a given set of cars to be produced in a single day, minimizing the number of violations of assembly constraints and the number of paint color changes in the production line. We propose a set of heuristics for approximately solving this problem, based on the paradigms of the VNS and ILS metaheuristics, to which further intensification and diversification strategies have been added. Computational results on real-life test instances are reported. The work presented in this paper obtained the second prize in the ROADEF challenge 2005 sponsored by Renault.     

Consecutive customer losses in oscillating GIX/M//n systems with state dependent services rates

We address the probability that k or more Consecutive Customer Losses take place during a busy period of a queue, the so-called k-CCL probability, for oscillating GI ^X /M//n systems with state dependent services rates, also denoted as GI ^X /M(m)−M(m)//n systems, in which the service rates oscillate between two forms according to the evolution of the number of customers in the system. We derive an efficient algorithm to compute k-CCL probabilities in these systems starting with an arbitrary number of customers in the system that involves solving a linear system of equations. The results derived are illustrated for specific sets of parameters.     

Consecutive customer losses in regular and oscillating <Emphasis Type="Italic">M</Emphasis><Superscript><Emphasis Type="Italic">X</Emphasis></Superscript>/<Emphasis Type="Italic">G</Emphasis>/1/<Emphasis Type="Italic">n</Emphasis> systems

We derive fast recursions to compute the probability that k or more consecutive customer losses take place during a busy period of a queue, the so called k-CCL probability, for regular and oscillating M ^X /G/1/n systems.     

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.     

Global convergence of slanting filter methods for nonlinear programming

In this paper, we present a general algorithm for nonlinear programming which uses a slanting filter criterion for accepting the new iterates. Independently of how these iterates are computed, we prove that all accumulation points of the sequence generated by the algorithm are feasible. Computing the new iterates by the inexact restoration method, we prove stationarity of all accumulation points of the sequence.