Production planning of multi-stage multi-option seru production systems with sustainable measures

With the concerns over climate change and the escalation in worldwide population, sustainable development attracts more and more attention of academia, policy makers, and businesses in countries. Sustainable manufacturing is an inextricable measure to achieve sustainable development since manufacturing is one of the main energy consumers and greenhouse gas contributors. In the previous researches on production planning of manufacturing systems, environmental factor was rarely considered. This paper investigates the production planning problem under the performance measures of economy and environment with respect to seru production systems, a new manufacturing system praised as Double E (ecology and economy) in Japanese manufacturing industries. We propose a mathematical model with two objectives minimizing carbon dioxide emission and makespan for processing all product types by a seru production system. To solve this mathematical model, we develop an algorithm based on the non-dominated sorting genetic algorithm II. The computation results and analysis of three numeral examples confirm the effectiveness of our proposed algorithm. © 2014 Elsevier Ltd. All rights reserved.

Corpus design for a unit selection TtS system with application to Bulgarian

In this paper we present the process of designing an efficient speech corpus for the first unit selection speech synthesis system for Bulgarian, along with some significant preliminary results regarding the quality of the resulted system. As the initial corpus is a crucial factor for the quality delivered by the Text-to-Speech system, special effort has been given in designing a complete and efficient corpus for use in a unit selection TTS system. The targeted domain of the TTS system and hence that of the corpus is the news reports, and although it is a restricted one, it is characterized by an unlimited vocabulary. The paper focuses on issues regarding the design of an optimal corpus for such a framework and the ideas on which our approach was based on. A novel multi-stage approach is presented, with special attention given to language and speaker dependent issues, as they affect the entire process. The paper concludes with the presentation of our results and the evaluation experiments, which provide clear evidence of the quality level achieved. © 2011 Springer-Verlag.

RANS-URANS in axial compressor, a design methodology

Turbomachinery flows are inherently unsteady. Until now during the design process, unsteadiness has been neglected, with resort merely to steady numerical simulations. Despite the assumption involved, the results obtained with steady simulations have been used with success. One of the questions arising in recent years is can unsteady simulations be used to improve the design of turbomachines? In this work the numerical simulation of a multi-stage axial compressor is carried out. Comparison of Reynolds averaged Navier-Stokes (RANS) and unsteady Reynolds averaged Navier-Stokes (URANS) calculation shows that the unsteadiness affects pressure losses and the prediction of stall limit. The unsteady inflow due to the wake passing mainly modifies the losses and whirl angle near the endwalls. The computational cost of the fully unsteady compared with a steady simulation is about four times in terms of mesh dimension and two orders of magnitude as number of iterations. A mixed RANS-URANS solution has been proposed to give the designer the possibility to simulate an unsteady stage embedded in a steady-state simulation. This method has been applied to the simulation of a four-stage axial compressor rig. The mixed RANS-URANS approach has been developed using sliding and mixing planes as interface conditions. The rotor-stator interaction has been captured physically while reducing the computational time and mesh size.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Towards productivity indicators for performance-based façade design in commercial buildings

Decision-making in the façade design process has a significant influence on several aspects of indoor environment, thereby making it a complex and multi-objective optimisation process. There are two principal barriers in the process of indentifying an optimal façade solution. Firstly, most existing indoor environmental evaluation methods do not account for all the indoor environmental quality (IEQ) aspects relevant to façade design. Secondly, the relationship between the physical properties of a particular façade design option and the resulting economic benefits accrued during its service-life is unknown. In this paper, we introduce the bases for establishing relationships between occupant productivity and the combinatorial effects of four key façade-related IEQ aspects, namely, thermal comfort, aural comfort, visual comfort and air quality, on occupant productivity. The proposed framework's potential is tested against seven existing experimental investigations and its applicability is illustrated by a simple façade design example. The proposed approach ultimately aims to provide a quantitative economic measure of alternative façade design options that would be applicable to early design stage. Aspects of the work that require further experimental validation are identified. © 2012 Elsevier Ltd.