Integrating mining loading and hauling equipment selection and replacement decisions using stochastic linear programming

Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a...

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Bibliographic Details
Published in:International journal of mining, reclamation and environment Vol. 31; no. 1; pp. 52 - 65
Main Authors: Santelices, Gabriel, Pascual, Rodrigo, Lüer-Villagra, Armin, Mac Cawley, Alejandro, Galar, Diego
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 02.01.2017
Taylor & Francis Ltd
Subjects:
Case studies
Computer applications
Cost engineering
Drift och underhållsteknik
equipment replacement
Equipment selection
Financing
Hauling
Life cycle analysis
Life cycle costs
Life cycle engineering
Life cycles
Linear programming
linear stochastic programming
Loaders
Materials handling
Mathematical models
Mining
mining industry
Operation and Maintenance
Operational problems
Probability theory
production assurance
Randomness
Stochastic models
Trucks
ISSN:1748-0930, 1748-0949, 1748-0949
Online Access:Get full text
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Summary:Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a specified production schedule. Previous works have addressed this problem with deterministic approaches, without considering the inter-dependent availability of trucks and loaders. In order to fill this gap, we developed a stochastic model that combines the selection and equipment replacement problems, subject to a stochastic production rate constraint. This is a new idea that will help decision-makers to decide faster and more reliable. The proposed model optimises the fleet by minimising the total life cycle costs. To solve it, we used a linearisation approach that reduces the computational effort. We tested our approach with a benchmark model, using a mining case study. Results indicate that the solutions ensure with a high probability a determined production target, producing good robust solutions compared to the deterministic counterpart.
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ISSN:1748-0930
1748-0949
1748-0949
DOI:10.1080/17480930.2015.1115589