A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

The multi-compartment vehicle routing problem (MC-VRP) consists of designing transportation routes to satisfy the demands of a set of customers for several products that, because of incompatibility constraints, must be loaded in independent vehicle compartments. Despite its wide practical applicability the MC-VRP has not received much attention in the literature, and the few existing methods assume perfect knowledge of the customer demands, regardless of their stochastic nature. This paper extends the MC-VRP by introducing uncertainty on what it is known as the MC-VRP with stochastic demands (MC-VRPSD). The MC-VRPSD is modeled as a stochastic program with recourse and solved by means of a memetic algorithm. The proposed memetic algorithm couples genetic operators and local search procedures proven to be effective on deterministic routing problems with a novel individual evaluation and reparation strategy that accounts for the stochastic nature of the problem. The algorithm was tested on instances of up to 484 customers, and its results were compared to those obtained by a savings-based heuristic and a memetic algorithm (MA/SCS) for the MC-VRP that uses a spare capacity strategy to handle demand fluctuations. In addition to effectively solve the MC-VRPSD, the proposed MA/SCS also improved 14 best known solutions in a 40-problem testbed for the MC-VRP.     

Spoken emotion recognition through optimum-path forest classification using glottal features

A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks – multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.     

Product interface design: A participatory approach based on virtual reality

The usability of the user interface is a key aspect for the success of several industrial products. This assumption has led to the introduction of numerous design methodologies addressed to evaluate the user-friendliness of industrial products. Most of these methodologies follow the participatory design approach to involve the user in the design process. Virtual Reality is a valid tool to support Participatory Design, because it facilitates the collaboration among designers and users.The present study aims to evaluate the feasibility and the efficacy of an innovative Participatory Design approach where Virtual Reality plays a ‘double role’: a tool to evaluate the usability of the virtual product interface, and a communication channel that allows users to be directly involved in the design process as co-designers.In order to achieve these goals, we conducted three experiments: the purpose of the first experiment is to determine the influence of the virtual interface on the usability evaluation by comparing “user–real product” interaction and “user–virtual product” interaction. Subsequently, we tested the effectiveness of our approach with two experiments involving users (directly or through their participation in a focus group) in the redesign of a product user interface. The experiments were conducted with two typologies of consumer appliances: a microwave oven and a washing machine.     

Path dependent stochastic models to detect planned and actual technology use: A case study of OpenOffice

Context

Adopting IT innovation in organizations is a complex decision process driven by technical, social and economic issues. Thus, those organizations that decide to adopt innovation take a decision of uncertain success of implementation, as the actual use of a new technology might not be the one expected. The misalignment between planned and effective use of innovation is called assimilation gap.

Objective

This research aims at defining a quantitative instrument for measuring the assimilation gap and applying it to the case of the adoption of OSS.

Method

In this paper, we use the theory of path dependence and increasing returns of Arthur. In particular, we model the use of software applications (planned or actual) by stochastic processes defined by the daily amounts of files created with the applications. We quantify the assimilation gap by comparing the resulting models by measures of proximity.

Results

We apply and validate our method to a real case study of introduction of OpenOffice. We have found a gap between the planned and the effective use despite well-defined directives to use the new OS technology. These findings suggest a need of strategy re-calibration that takes into account environmental factors and individual attitudes.

Conclusions

The theory of path dependence is a valid instrument to model the assimilation gap provided information on strategy toward innovation and quantitative data on actual use are available.     

An haptic-based immersive environment for shape analysis and modelling

Currently, the design of aesthetic products is a process that requires a set of activities where digital models and physical mockups play a key role. Typically, these are modified (and built) several times before reaching the desired design, increasing the development time and, consequently, the final product cost. In this paper, we present an innovative design environment for computer-aided design (CAD) surface analysis. Our system relies on a direct visuo-haptic display system, which enables users to visualize models using a stereoscopic view, and allows the evaluation of sectional curves using touch. Profile curves are rendered using an haptic device that deforms a plastic strip, thanks to a set of actuators, to reproduce the curvature of the shape co-located with the virtual model. By touching the strip, users are able to evaluate shape characteristics, such as curvature or discontinuities (rendered using sound), and to assess the surface quality. We believe that future computer-aided systems (CAS)/CAD systems based on our approach will contribute in improving the design process at industrial level. Moreover, these will allow companies to reduce the product development time by reducing the number of physical mockups necessary for the product design evaluation and by increasing the quality of the final product, allowing a wider exploration and comparative evaluation of alternatives in the given time.     

Anodic stripping voltammetric determination of copper(II) using a functionalized carbon nanotubes paste electrode modified with crosslinked chitosan

The development and application of a functionalized carbon nanotubes paste electrode (CNPE) modified with crosslinked chitosan for determination of Cu(II) in industrial wastewater, natural water and human urine samples by linear scan anodic stripping voltammetry (LSASV) are described. Different electrodes were constructed using chitosan and chitosan crosslinked with glutaraldehyde (CTS-GA) and epichlorohydrin (CTS-ECH). The best voltammetric response for Cu(II) was obtained with a paste composition of 65% (m/m) of functionalized carbon nanotubes, 15% (m/m) of CTS-ECH, and 20% (m/m) of mineral oil using a solution of 0.05 mol L⁻¹ KNO₃ with pH adjusted to 2.25 with HNO₃, an accumulation potential of −0.3 V vs. Ag/AgCl (3.0 mol L⁻¹ KCl) for 300 s and a scan rate of 100 mV s⁻¹. Under these optimal experimental conditions, the voltammetric response was linearly dependent on the Cu(II) concentration in the range from 7.90 × 10⁻⁸ to 1.60 × 10⁻⁵ mol L⁻¹ with a detection limit of 1.00 × 10⁻⁸ mol L⁻¹. The samples analyses were evaluated using the proposed sensor and a good recovery of Cu(II) was obtained with results in the range from 98.0% to 104%. The analysis of industrial wastewater, natural water and human urine samples obtained using the proposed CNPE modified with CTS-ECH electrode and those obtained using a comparative method are in agreement at the 95% confidence level.