Department of Mathematics and Statistics/Faculty of Science
Melbourne Operations Research

Through the power of science MORe delivers greater productivity, efficiency and profitability to organisations.

Operations Research is the science of using advanced analytical methods to improving decision making.

Melbourne Operations Research (MORe) is a leading Operations Research consulting practice, based in the Department of Mathematics and Statistics at the University of Melbourne. MORe specialises in planning, scheduling and logistics.

MORe is committed to developing high quality solutions that deliver real results. For your organisation this means better informed decision making, improved efficiency and greater profitability.

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Latest News

16 June 2009: Try Us Out Online

Do you have complex decisions that you wish to address but are not sure how? Would you like to discover the potential for MORe to add value to your business?

At MORe we are keen to explore your ideas and investigate how we may assist in the challenges facing your organisation. Just fill in our new online form here and we will look over your situation and see how we may assist.

12 June 2009: Conference on Transportation Scheduling and Disruption Handling

The Conference on Transportation Scheduling and Disruption Handling will focus on software algorithms for optimisation of transportation schedules, focusing on the interaction between scheduling and on-the-day operation. Dr Heng-Soon Gan will chair a session presenting approaches to transportation scheduling and disruption handling based on repair techniques and local search...[more]

12 June 2009: Talking Operations Research in Singapore

Dr Heng-Soon Gan recently presented an introduction to Management Science (Operations Research) to the Singapore office of IDP Education...[more]

MORe News and Events

NEW: Conceptualising Optimisation

What exactly is modeling and optimisation? - Try out our new interactive modeling example and find out!

The new 'Conceptualising Optimisation' page shows how optimisation can be applied to industry problems. A simple production planning situation is presented. Users can experiment with different solutions and compare these to the optimised results. The underlying mathematical can then be examined.