Long-term open-pit planning by ant colony optimization
type: CreativeWork
description: The problem of long-term planning of a hard rock open pit mine (discontinuous exploitation operation) is a large combinatorial problem which cannot be solved in a reasonable amount of time through mathematical programming models because of its large size. In this thesis, a new metaheuristic algorithm has been developed based on the Ant Colony Optimization (ACO) and its application in long-term scheduling of a two dimensional hypothetical block model has been analysed. ACO is inspired by the foraging behaviour of ants (i.e. finding the shortest way from the colony to the food source), and has been successfully implemented in several combinatorial optimization problems. In nature, ants transmit a message to other members by laying down a trail with a chemical called pheromones. Instead of travelling in a random manner, the pheromone trail allows the ants to trace the path. Over time, the pheromones layed over longer paths evaporate, whereas those over shorter routes continue to be marched over. In order to simulate the ACO process for long-term planning of a hard rock open-pit mine, various programming variables have been considered for each block as the pheromone trails. The number of these variables is equal to the number of planning periods. In fact these pheromone trails represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. The shape of any given pit (in respect to the slope angles) can be represented by means of a simple array of integer numbers. Each element in this array shows the depth of the pit in an individual column of block model. Extending this concept to a long-term production planning, a mine schedule would be represented by an array that has several mine depths at each column of block model related to different production periods. At the beginning, the values of the pheromone trails are initialized according to a mine schedule generated by Lerchs-Grossmann’s algorithm and the altern…
abstract: The problem of long-term planning of a hard rock open pit mine (discontinuous exploitation operation) is a large combinatorial problem which cannot be solved in a reasonable amount of time through mathematical programming models because of its large size. In this thesis, a new metaheuristic algorithm has been developed based on the Ant Colony Optimization (ACO) and its application in long-term scheduling of a two dimensional hypothetical block model has been analysed. ACO is inspired by the foraging behaviour of ants (i.e. finding the shortest way from the colony to the food source), and has been successfully implemented in several combinatorial optimization problems. In nature, ants transmit a message to other members by laying down a trail with a chemical called pheromones. Instead of travelling in a random manner, the pheromone trail allows the ants to trace the path. Over time, the pheromones layed over longer paths evaporate, whereas those over shorter routes continue to be marched over. In order to simulate the ACO process for long-term planning of a hard rock open-pit mine, various programming variables have been considered for each block as the pheromone trails. The number of these variables is equal to the number of planning periods. In fact these pheromone trails represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. The shape of any given pit (in respect to the slope angles) can be represented by means of a simple array of integer numbers. Each element in this array shows the depth of the pit in an individual column of block model. Extending this concept to a long-term production planning, a mine schedule would be represented by an array that has several mine depths at each column of block model related to different production periods. At the beginning, the values of the pheromone trails are initialized according to a mine schedule generated by Lerchs-Grossmann’s algorithm and the altern…
author: Sattarvand, Javad
language: eng
keywords: info:eu-repo/classification/ddc/620, Tagebau, Planung, Ameisenalgorithmus, Optimierung, Ingenieurwissenschaften, Tagebauplanung, open-pit planning, ant colony optimization, optimization
date published: 2012
date modified: 2026-07-30T19:43:35Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Mainz