Technical Sessions – Monday July 11

FB-21 IFORS 2011 - Melbourne
2 - Integration of Multi-criteria Decision Analysis and Lifecycle
Assessment to Measure Environmental Impacts
of Biomass Production
Tanja Myllyviita, Finnish environment institute, P.O.Box 111,
FI-80101, Joensuu, Finland, tanja.myllyviita@joensuu.fi, Anne
Holma, Riina Antikainen, Katja Lähtinen, Pekka Leskinen
Life-cycle assessment (LCA) evaluates environmental impacts of a product
from processing of raw-material to disposal with environmental impact categories.
MCDA was applied to assess the importance of different LCA impact
categories in biomass production. Experts valuated impact categories with a
MCDA-application. The most important impact categories were natural landuse
and climate change. Also biodiversity was considered important. The results
indicate that MCDA can provide suitable tools for the weighting phase in
LCA, which enables transforming the environmental impacts into one number.
3 - On Elicitation Techniques of Near-consistent Pairwise
Comparison Matrices
Jozsef Temesi, Operations Research and Actuarial Studies,
Corvinus University of Budapest, Fovam ter 8, 1093, Budapest,
Hungary, jozsef.temesi@uni-corvinus.hu
Pairwise comparison matrices (PCMs) are frequently used in various multicriteria
decision-making methods. One of the most important properties of a
PCM is consistency. My presentation will define the near-consistency of the
PCM, and the error-free property of the decision-maker. Based on these definitions
several methods can be generated to obtain the elements of a pairwise
comparison matrix. Different elicitation techniques will be introduced, and —
in case of non-consistent matrices — adjustment methods will be analyzed for
both informed and uninformed decision-makers.
4 - An Analysis of the Major Drivers of Stock Market Prices-
Case Study for the Zimbabwe Stock Exchange (2007-
2008)
Kelvin T Chirenje, Applied Maths, National University of
Science and Technology, 10 Rukumbati Rd, Zengeza 3, +263,
Chitungwiza, Harare, Zimbabwe, kchirenje@gmail.com
This research is aimed at assessing the main factors that influenced the stock
prices on the Zimbabwe Stock Exchange (ZSE) to be continuously bullish from
2007-2008, despite harsh economic conditions which prevailed in the country
and explore the applicability of traditional theorist to the ZSE. Furthermore, the
research will outline why the stock market was performing extraordinarily well
in an economy down the drain with many corporations reporting subnormal
profits and some shutting down
� FB-21
Friday, 13:15-14:45
Meeting Room 218
Stochastic Open Pit Mine Planning and
Supply Chains
Stream: Mining Applications
Invited session
Chair: Gary Froyland, School of Mathematics and Statistics,
University of New South Wales, 2052, Sydney, NSW, Australia,
g.froyland@unsw.edu.au
1 - Open Pit Mine Planning with Uncertain Geology via
Multi-stage Integer Stochastic Programming with Endogenous
Uncertainty
Gary Froyland, School of Mathematics and Statistics, University
of New South Wales, 2052, Sydney, NSW, Australia,
g.froyland@unsw.edu.au, Natashia Boland, Irina Dumitrescu
Geological uncertainty is a major source of financial risk for mining projects.
It is particularly difficult to handle as the timing of the resolution of the uncertainty
is dependent upon earlier decisions made. We model the problem of NPV
optimisation under geological uncertainty as a multi-stage integer stochastic
programming under endogenous uncertainty. Our model allows mining and
processing decisions to flexibly adapt over time, in response to observation of
the geology of the material mined. We also discuss a number of model reductions
to decrease computational effort.
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2 - Dealing with Price Uncertainty in Mine Planning
Andrés Weintraub, University of Chile, Santiago, Chile,
aweintra@dii.uchile.cl, Roger Wets, David Woodruff, Jean-Paul
Watson, Rafael Epstein, Jaime Gacitua
We consider the problem of uncertainty in future copper prices, reflected
through scenarios with probabilites. Non-anticipativity constraints are imposed
on the basic problem constraints. For larger problems solving the problem with
the non-anticipativity constraints is very difficult computationally. We develop
an approach for this problem based on Progressive Hedging, where the problem
is decomposed by scenarios, and convergence to a feasible solution is attained
through penalizing deviations from non-anticipativity. Positive results were
obtained for an open pit mine problem.
3 - Effective Computational Models For Oil Refinery Operations
Juan Kuther, Maths and Stats, Curtin University, GPO BOX
U1987, 6845, Perth, WA, Australia,
juan.kok@postgrad.curtin.edu.au, Louis Caccetta
Petroleum refineries are very complex systems, giving rise to computationally
difficult optimization models. Resolution of these problems is crucial for production
planning and in particular in the evaluation and selection of crudes,
feedstocks, products and processing options. These large-scale production
planning models, which can are formulated as non-convex Mixed Integer Nonlinear
Programming (MINLP) models, are very difficult to solve. Currently
available tools are deficient and usually give rise to inconsistent predictions of
refinery productivity and operation. Recent advances in a number of disciplines
including computer science, mathematical programming, heuristics and complimentary
disciplines like constraint programming and artificial intelligence
motivate this research. These recent advances in technology will be utilised to
formulate effective oil refinery production planning models.
4 - Collaborative Resource Constrained Scheduling : A
Coal Industry Example
Anu Thomas, Industrial Engineering and Operations Research,
IITB Monash Research Academy, IIT Bombay, Powai, 400076,
Mumbai, MH, India, anuthomas@iitb.ac.in, Gaurav Singh,
Mohan Krishnamoorthy, Jayendran Venkateswaran
We present a collaborative resource constrained scheduling problem motivated
by the mining industry. In this model, there are several independent mines
which have delivery jobs to be completed by certain due dates (ship arrival
times). These jobs also require trains (of certain sizes) that are independently
provided by a rail operator having a finite number of trains. We present MIP
formulations, heuristic algorithms and computational results to compare centralised
and decentralised decision making for minimising total weighted tardiness
of all the jobs while maximising train utilisation.