Supply Chain, Logistics and Mining at BAM 2016

in Blog / by dnsadmin

The Biarri Applied Mathematics Conference is on again and registrations are open for 2016. This year BAM will be covering a range of presentations across supply chain, logistics and mining optimisation. With our supply chains becoming more and more complex don’t miss out on exploring how optimisation can be used.

Extending the MIP toolbox to crack the Liner Shipping Fleet Repositioning Problem

Robin Pearce will be delving into the Liner Shipping Fleet Repositioning Problem (LSFRP) which involves repositioning ships between service routes while maximising profit. This presentation will demonstrate how this problem can become quite large, with multiple ships, thousands of potential cargo transfers and tens of thousands of arcs. A straightforward MIP implementation can solve small scale problems, however the problem quickly becomes intractable..

In this presentation Robin will show us how he has managed to reduce solve times from hours down to a few minutes.

Robin Pearce is a mathematics student at the University of Queensland. After studying a Bachelor of Science with Honours in Applied Mathematics at UQ, he spent two years as a Graduate Fellow with CSIRO working on three-dimensional microstructure modelling. He is now a PhD student with Michael Forbes, once again at UQ. His main topics of interest are the use of lazy constraints and disaggregated Benders decomposition for solving large and difficult integer and mixed-integer programs.

Optimal facility location and equipment selection for whey re-use

Whey is a by-product of cheese making that is a potentially important source of nutrients, but which currently goes to disposal in many parts of the world. In this presentation, Rasul Esmaeilbeigi will analyse the efficiency of investment in whey-processing with the aim of releasing the productive potential of currently unexploited whey supply chains. Rasul will describe a decision support model for production and distribution of products derived from whey that extends a globally inclusive facility location problem. The basic tenet of the model is that equipment selection during the initial stages of facility planning is critical, as capital costs in the early stages of supply chain design go into purchases of new machines and site conditioning. The model selects the optimal combination of whey processing equipment, facility locations and transportation routes subject to budget, equipment availability and final product requirements.

Rasul is currently a PhD. candidate in the school of mathematical and physical sciences at the University of Newcastle. he holds a master’s degree (2014) and a bachelor’s degree (2012) in Industrial Engineering. Rasul has expertise in the field of Mathematical Programming and Combinatorial Optimization and also general knowledge and experience of programming languages for solving large scale optimisation problems.

Multiple Yard Crane Scheduling with Variable Crane Handling Time and Uncertain Yard Truck Arrival Time

Container yard performance heavily depends on the efficient operations of yard cranes. Yong Wu will discuss the multiple yard crane scheduling problem with variable crane handling time and uncertain yard truck arrival time. Here the variable crane handling time refers to the variable time of handling each individual container, while the uncertain yard truck arrival time relates to the actual arrival time of trucks that are dispatched to either pick up or drop off containers. While there is a rich body of literature addresses the multiple yard crane scheduling problem in a deterministic operational context, there is a paucity of research incorporating these uncertain factors.

Dr Yong Wu is a Senior Lecturer at the Department of International Business and Asian Studies within the Griffith Business School. Yong holds a PhD in Operations Research and an MEng in Mechanical Engineering and has worked for The Logistics Institute – Asia Pacific, a joint venture between National University of Singapore and Georgia Institute of Technology (2005-2008), and the Institute for Logistics and Supply Chain Management, Victoria University, Australia (2008-2010). He teaches in the area of logistics and supply chain management and his research interests are in logistics and supply chain management, operations research and engineering optimisation.

Machine learning methods for mineral processing

Machine learning emerged as a subject area in the late 1950s; yet to date there has been little application of machine learning to mineral processing.

There are of course many ways that machine learning can be applied. Stephen Gay will pursue a probabilistic framework, strongly related to the new subbranch of mathematics called information theory.

The approach is to use far less samples than conventional methods and to infer many of the missing variables – indeed to infer the missing variables at a great level of depth (distribution of multimineral particles at each stream). By inferring this information we have a ‘snapshot’ of unit models for each series of plant data. Machine learning algorithms are then applied to parameterise the models according to operational parameters.

Dr. Stephen Gay originally graduated from University of Queensland [BSc (hons/Applied Maths)]. His domain areas have largely been in physical oceanography, mining (PhD), image analysis and geometric probability. The main area of mining is the development of software for optimising mineral processing plants. He received most of his grounding in mathematical modelling for mineral processing at the Julius Kruttschnitt Mineral Research Centre (JKMRC) – and in 2008 development his own independent consulting and contracting business which has since evolved into a startup Company: MIDAS Tech Intl. In 2014 he patented a method that enables the estimation of detailed mineral processing data from simple measurements – and has largely been focusing on getting interest in this new method from Mining Companies and Universities.

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Extending the MIP toolbox to crack the Liner Shipping Fleet Repositioning Problem

Optimal facility location and equipment selection for whey re-use

Multiple Yard Crane Scheduling with Variable Crane Handling Time and Uncertain Yard Truck Arrival Time

Machine learning methods for mineral processing

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