About the lab

The Maori word “orua” means “to coincide” and we think about Operations Research and Analytics as engineering, mathematics, statistics, computer science and management science coinciding to improve whatever system we are currently modelling. O(perations) R(esearch) U(nion) A(nalytics) is a research group at The University of Auckland that investigates Operations Research and Analytics methods and utilises them to improve a wide variety of systems.

ORUA was founded by Dr Michael O'Sullivan and A/Prof Cameron Walker who currently co-direct the lab. More information on the lab can be found on
* the lab website https://orua.auckland.ac.nz/; and
* the lab Twiki https://twiki.esc.auckland.ac.nz/bin/view/ORUA/WebHome

Featured research (12)

In this paper, we present a new model averaging technique that can be applied in medical research. The dataset is first partitioned by the values of its categorical explanatory variables. Then for each partition, a model average is determined by minimising some form of squared errors, which could be the leave-one-out cross-validation errors. From our asymptotic optimality study and the results of simulations, we demonstrate under several high-level assumptions and modelling conditions that this model averaging procedure may outperform jackknife model averaging, which is a well-established technique. We also present an example where a cross-validation procedure does not work (that is, a zero-valued cross-validation error is obtained) when determining the weights for model averaging.
Cloud service providers use load balancing algorithms in order to avoid Service Level Agreement Violations (SLAVs) and wasted energy consumption due to host over- and under-utilization, respectively. Load balancing algorithms migrate VMs between hosts in order to balance host loads. Any Virtual Machines (VMs) that are migrated experience performance degradation which results in lower Quality of Service (QoS) and can possibly result in SLAVs. Hence, an optimal load balancing method should reduce the number of over- and under-utilized hosts with a minimal number of VM migrations. One of the metrics used previously in the literature for evaluating load balancing stated that it equally considered SLAVs caused by both over-utilized hosts and migrations. However, in this paper, we show that, in fact, this metric values keeping the number of migrations low at the expense of an increased number of over-utilized hosts. This disparity is demonstrated by simulation of Google, PlanetLab and Azure data sets in CloudSim. This metric may suit public cloud providers which are focused on minimizing SLAVs and keeping energy costs low, but does not consider the QoS of customer VMs. We propose an alternative metric that considers QoS for the VMs. This alternative metric considers not only performance loss during migration, but also performance degradation due to host over-utilization. Private cloud providers, e.g., IT services within large organizations, often value the performance of their “customer” VMs, i.e., the QoS their organization receives, as well as traditional cloud provider costs, i.e., energy and SLAV costs. Hence, our alternative metric would be more appropriate in these scenarios. We compare and contrast load balancing methods using both the existing, biased metric and our new alternative metric.
Simulation modelling has been utilised as a decision-support tool for different production systems. However, simulation uptake in construction is lagging due to several challenges such as the extensive data requirements and modelling efforts in a simulation study. This paper aims at addressing this gap by introducing a framework that integrates the practices of simulation modelling with the Last Planner® System (LPS), which is a well-established production planning and control method. The framework focuses on exploiting the outputs of implementing the LPS to support building a valid simulation model. A case study is conducted to test the applicability of the proposed framework. Based on the findings of the study, it can be concluded that the LPS provides a promising avenue for integration with simulation modelling due to the shared processes and requirements between both methods, which may lead to minimised data requirements and modelling efforts for simulation, thus improving simulation uptake in the construction industry.

Lab head

Michael O'Sullivan
Department
  • Department of Engineering Science
About Michael O'Sullivan
  • My main research interests are the application of Operations Research and Analytics to complex systems, particularly the formulation of optimisation and simulation models and the development of advanced techniques for solving these models. I am focusing on: improving health delivery services; modelling to inform government policy; building intelligent IT clouds; conceptual modelling for simulation; developing innovative optimisation software; and simulating construction projects.

Members (16)

Vicente Gonzalez
  • University of Alberta
Cameron G Walker
  • University of Auckland
Jonathan P Christiansen
  • Waitemata District Health Board
Ken Walsh
  • George Mason University
Siegfried Voessner
  • Graz University of Technology
Abraham Zhang
  • University of Essex
Subeh Chowdhury
  • University of Auckland
Nikolaus Furian
  • Graz University of Technology
Tava Lennon Olsen
Tava Lennon Olsen
  • Not confirmed yet
K.W. Soh
K.W. Soh
  • Not confirmed yet
K.W. Soh
K.W. Soh
  • Not confirmed yet
Delwyn Armstrong
Delwyn Armstrong
  • Not confirmed yet
Jonathan Wallace
Jonathan Wallace
  • Not confirmed yet
Kanokporn Pongjetanapong
Kanokporn Pongjetanapong
  • Not confirmed yet
Philip Santner
Philip Santner
  • Not confirmed yet