Simulation-based high-level synthesis of Nyquist-rate data converters using MATLAB/SIMULINK

This paper presents a toolbox for the simulation, optimization and high-level synthesis of Nyquist-rate Analog-to-Digital (A/D) and Digital-to-Analog (D/A) Converters in MATLAB®. The embedded simulator uses SIMULINK® C-coded S-functions to model all required subcircuits including their main error mechanisms. This approach allows to drastically speed up the simulation CPU-time up to 2 orders of magnitude as compared with previous approaches - based on the use of SIMULINK® elementary blocks. Moreover, S-functions are more suitable for implementing a more detailed description of the circuit. For all subcircuits, the accuracy of the behavioral models has been verified by electrical simulation using HSPICE. For synthesis purposes, the simulator is used for performance evaluation and combined with an hybrid optimizer for design parameter selection. The optimizer combines adaptive statistical optimization algorithm inspired in simulated annealing with a design-oriented formulation of the cost function. It has been integrated in the MATLAB/SIMULINK® platform by using the MATLAB® engine library, so that the optimization core runs in background while MATLAB® acts as a computation engine. The implementation on the MATLAB® platform brings numerous advantages in terms of signal processing, high flexibility for tool expansion and simulation with other electronic subsystems. Additionally, the presented toolbox comprises a friendly graphical user interface to allow the designer to browse through all steps of the simulation, synthesis and post-processing of results. In order to illustrate the capabilities of the toolbox, a 0.13)im CMOS 12bit@80MS/s analog front-end for broadband power line communications, made up of a pipeline ADC and a current steering DAC, is synthesized and high-level sized. Different experiments show the effectiveness of the proposed methodology.Ministerio de Ciencia y Tecnología TIC2003-02355RAICONI

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Study of the development and verification of an integrated code for UAV design

L'objectiu d'aquest estudi és desenvolupar una eina de disseny d'aeronaus utilitzant algoritmes d'optimització per a facilitar el procés. Es pretén incorporar el codi d'estudi i simulació de les actuacions d'un UAV desenvolupat per l'equip Trencalòs Team en un software de disseny aerodinàmic ja existent, ja sigui XFLR5 o AVL. Les funcions objectiu incorporades seran les que l'equip considera per a la participació en el concurs internacional Air Cargo Challenge, amb la intenció de desenvolupar una eina de treball per a Trencalòs que permeti fer un disseny òptim dins del marc de la competició. El treball es dividirà en tres etapes: 1. Incorporació del codi desenvolupat per Trencalòs al software de disseny aerodinàmic2. Fer ús dels algoritmes d'optimització de funcions objectiu per a facilitar el procés de disseny3. Verificació els resultats obtinguts.

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work proposes an alternative treatment of the imprecision (demands) by using fuzzy concepts. In this study, we introduce a new approach to the design problem based on a multiobjective genetic algorithm, taking into account simultaneously maximization of the net present value NPV ~ and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg’s wheel, systematically leading to a higher number of non-dominated solutions

Life cycle assessment (LCA) applied to the process industry: a review

Purpose : Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations. Method : This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. Results : The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions. Conclusions : The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry

Multi-disciplinary robust design of variable speed wind turbines

This paper addresses the preliminary robust multi-disciplinary design of small wind turbines. The turbine to be designed is assumed to be connected to the grid by means of power electronic converters. The main input parameter is the yearly wind distribution at the selected site, and it is represented by means of a Weibull distribution. The objective function is the electrical energy delivered yearly to the grid. Aerodynamic and electrical characteristics are fully coupled and modelled by means of low- and medium-fidelity models. Uncertainty affecting the blade geometry is considered, and a multi-objective hybrid evolutionary algorithm code is used to maximise the mean value of the yearly energy production and minimise its variance