Now showing 1 - 10 of 22
  • Publication
    Engaging Business Students in Quantitative Skills Development
    (Australian Business Education Research Association, 2015-06) ;
    In this paper the complex problems of developing quantitative and analytical skills in undergraduate first year, first semester business students are addressed. An action research project detailing how first year business students perceive the relevance of data analysis and inferential statistics in light of the economic downturn and the challenges society faces is discussed. Students¿ attitudes were evaluated via an online survey consisting of both quantitative and qualitative responses. While two thirds of respondents do acknowledge the relevance of such a course for future business roles, it is shown that more work must be done to distinguish between why data analysis is relevant and how data analysis is performed. Also discussed are findings related to student learning, their intellectual development, and their motivation and expectations upon enrolling on the Data Analysis for Decision Makers (DADM) module. The challenges in teaching such a mandatory module to Business students are discussed and a pedagogical framework for promoting deeper student engagement through active learning, regular continuous assessment and technology are also examined.
  • Publication
    Smart Grid Topology Designs
    (, 2019-06-12) ;
    This paper addresses supports for evolving design demands of electricity low voltage networks in urban areas. Innovations in how electricity is generated and supplied are required to support transformation of energy systems in response to climate change. We describe a MIP model to support grid upgrade decisions in the context of an energy community in an existing urban setting. We evaluate the MIP model on an adaption of an IEEE radial network benchmark instance augmented with geographic data. We present interesting computational results which suggest additional arcs to be added. Our results highlight potential research opportunities for the network optimisation community to facilitate the desired energy systems transformation challenge.
  • Publication
    Motivational Assessment Strategies in Business Analytics
    (Association for Information Systems, 2016-12-11) ;
    This teaching case describes an assessment component which was developed as part of a core undergraduate module in data analysis. The data analysis module is taken by all undergraduate business students at the School of Business in University College Dublin. We give a detailed description of the team project assessment component. This component was designed to address two key problems in business analytics education: how to engage business students in quantitative analysis and how to foster decision-making based on data analysis. We present promising results with analysis and some recommendations.
  • Publication
    A Decomposition Algorithm for the Ring Spur Assignment Problem
    This paper describes the ring spur assignment problem (RSAP), a new problem arising in the design of next generation networks. The RSAP complements the sonet ring assignment problem (SRAP). We describe the RSAP, positioning it in relation to problems previously addressed in the literature. We decompose the problem into two IP problems and describe a branch-and-cut decomposition heuristic algorithm suitable for solving problem instances in a reasonable time. We present promising computational results.
      498Scopus© Citations 5
  • Publication
    A Branch and Cut Algorithm for the Ring Spur Assignment Problem
    The Ring Spur Assignment Problem (RSAP) arises in the design of Next Generation Telecommunications Networks (NGNs) and has applications in location-allocation problems. The aim is to identify a minimum cost set of interconnected ring spurs. We seek to connect all nodes of the network either on a set of bounded disjoint local rings or by a single spur edge connected to a node on a local ring. Local rings are interconnected by a special ring called the tertiary ring. We show that the problem is NP-Hard and present an Integer Programming formulation with additional valid inequalities. We implement a branch-and-cut algorithm and present our conclusions with computational results.
      512Scopus© Citations 9
  • Publication
    Smart Meter Tariff Design to Minimise Wholesale Risk
    (Elsevier, 2016-06) ;
    Smart metering in electricity markets offers an opportunity to explore more diversetariff structures. In this article a Genetic Algorithm (GA) is used to design Time ofUse tariffs that minimise the wholesale risk to the supplier in residential markets.Residential demand and the System Marginal Price of Ireland's Single ElectricityMarket are simulated to estimate the wholesale risk associated with each tariff.
      378Scopus© Citations 1
  • Publication
    A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study
    Biopharmaceutical manufacturers are required to collect extensive observational data sets in order to meet regulatory and process quality monitoring requirements. These datasets contain information that may improve the performance of the production process. Analytics provides a means of extracting this information while Six Sigma provides a means for the insights to be incorporated into production practices. We present a novel framework which combines Six Sigma and Business Analytics. This approach mines large volumes of inline and offline biopharmaceutical production data, allowing the entire production process to be analysed and modeled. The recommendations of the model are represented as manufacturing rules which give actionable insights to improve the performance of the process. The integrated approach delivers promising results from synthetic experiments as well as being applied in practice to a cell culture process.
      526Scopus© Citations 19
  • Publication
    Integer-Programming Ensemble of Temporal-Relations Classifiers
    The extraction of temporal events from text and the classification of temporal relations among both temporal events and time expressions are major challenges for the interface of data mining and natural language processing. We present an ensemble method, which reconciles the outputs of multiple heterogenous classifiers of temporal expressions. We use integer programming, a constrained optimisation technique, to improve on the best result of any individual classifier by choosing consistent temporal relations from among those recommended by multiple classifiers. Our ensemble method is conceptually simple and empirically powerful. It allows us to encode knowledge about the structure of valid temporal expressions as a set of constraints. It obtains new state-of-the-art results on two recent natural language processing challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and SemEval-2016 Task 12 (Clinical TempEval), with F1 scores of 0.3915 and 0.595 respectively.
      507Scopus© Citations 1
  • Publication
    Household Classification Using Smart Meter Data
    This article describes a project conducted in conjunction with the Central Statistics Office of Ireland in response to a planned national rollout of smart electricity metering in Ireland. We investigate how this new data source might be used for the purpose of official statistics production. This study specifically looks at the question of determining household composition from electricity smart meter data using both Neural Networks (a supervised machine learning approach) and Elastic Net Logistic regression. An overview of both classification techniques is given. Results for both approaches are presented with analysis. We find that the smart meter data alone is limited in its capability to distinguish between household categories but that it does provide some useful insights.
      580Scopus© Citations 45
  • Publication
    A Genetic Algorithm for a Green Vehicle Routing Problem
    We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO 2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution ap- proach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.