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.     

Time and temperature induced phase transformation in L‐isoleucine hydrochloride monohydrated crystal

Semi organic crystals have been intensively studied aiming applications in nonlinear optical (NLO). Such applicability requires crystals with both high quality and thermal stability, which make the full characterization of this material a topic issue. In this paper, single crystals of L‐isoleucine hydrochloride monohydrated (L‐Ile.H2O.HCl) was synthesized by slow evaporation technique and characterized by thermal analysis and X‐ray diffraction (XRD) measurements. XRD results at 298 K showed that the sample crystallize with the orthorhombic structure, and the lattice parameters obtained by Rietveld refinement were a = 5.873(3) Å, b = 24.814(4) Å, and c = 6.873(5) Å. Thermal analysis measurements shows four decomposition stages between 328 ‐480 K which were associated with loss of water by desolvation, loss crystallization water and decomposition of L‐Ile.HCl. XRD measurements as a function of temperature up to 428 K show an irreversible phase transformation. This transformation was obtained after 32 hours keeping the L‐Ile.HCl.H2O sample at 413 K. The analysis shows that phase transformation occurs due to water and chlorine losses without destroy the amino acid carbon chain and in the end of transformation only the precursor amino acid remains. L‐Ile.HCl.H2O present low thermal stability and the phase transformation is time and temperature dependent.     

Prediction of SAMPL2 aqueous solvation free energies and tautomeric ratios using the SM8, SM8AD,and SMD solvation models

We applied the solvation models SM8, SM8AD, and SMD in combination with the Minnesota M06-2X density functional to predict vacuum-water transfer free energies (Task 1) and tautomeric ratios in aqueous solution (Task 2) for the SAMPL2 test set. The bulk-electrostatic contribution to the free energy of solvation is treated as follows: SM8 employs the generalized Born model with the Coulomb field approximation, SM8AD employs the generalized Born approximation with asymmetric descreening, and SMD solves the nonhomogeneous Poisson equation. The non-bulk-electrostatic contribution arising from short-range interactions between the solute and solvent molecules in the first solvation shell is treated as a sum of terms that are products of geometry-dependent atomic surface tensions and solvent-accessible surface areas of the individual atoms of the solute. On average, three models tested in the present work perform similarly. In particular, we achieved mean unsigned errors of 1.3 (SM8), 2.0 (SM8AD), and 2.6 kcal/mol (SMD) for the aqueous free energies of 30 out of 31 compounds with known reference data involved in Task 1 and mean unsigned errors of 2.7 (SM8), 1.8 (SM8AD), and 2.4 kcal/mol (SMD) in the free energy differences (tautomeric ratios) for 21 tautomeric pairs in aqueous solution involved in Task 2.