research-article

An improved merge search algorithm for the constrained pit problem in open-pit mining

Authors:

Andreas T. Ernst,

Yuan SunAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 294 - 302

https://doi.org/10.1145/3321707.3321812

Published: 13 July 2019 Publication History

Abstract

Conventional mixed-integer programming (MIP) solvers can struggle with many large-scale combinatorial problems, as they contain too many variables and constraints. Meta-heuristics can be applied to reduce the size of these problems by removing or aggregating variables or constraints. Merge search algorithms achieve this by generating populations of solutions, either by heuristic construction [4], or by finding neighbours to an initial solution [12]. This paper presents a merge search algorithm that improves the population generation heuristic in [12] and utilises a variable grouping heuristic that exploits the common information across a population to aggregate groups of variables in order to create a reduced subproblem. The algorithm is tested on some well known benchmarks for a complex problem called the constrained pit (CPIT) problem and it is compared to results produced by a merge search algorithm previously used on the same problem and the results published on the minelib [9] website.

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Angus Kenny, Xiaodong Li, and Andreas T. Ernst. 2018. A merge search algorithm and its application to the constrained pit problem in mining. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18). ACM, 8.

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Angus Kenny, Xiaodong Li, Andreas T. Ernst, and Dhananjay Thiruvady. 2017. Towards solving large-scale precedence constrained production scheduling problems in mining. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17). ACM, 1137--1144.

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Dhananjay Thiruvady, Davaatseren Baatar, Andreas T. Ernst, Angus Kenny, Mohan Krishnamoorthy, and Gaurav Singh. 2018. Mixed integer programming based merge search for open-pit block scheduling. Computers & Operations Research (under review) (2018).

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Dhananjay Thiruvady, Christian Blum, and Andreas T Ernst. 2019. Maximising the net present value of project schedules using CMSA and parallel ACO. In International Workshop on Hybrid Metaheuristics. Springer, 16--30.

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Cited By

Pathiranage INeumann A(2024)Multi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612008(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612008
Omidvar MLi XYao X(2022)A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part IIIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313083526:5(823-843)Online publication date: Oct-2022
https://doi.org/10.1109/TEVC.2021.3130835
Rahimi IPicard TMorabito APampalis KAbignano AGandomi A(2022)Comparison of Trajectory and Population-Based Algorithms for Optimizing Constrained Open-Pit Mining Problem2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI56532.2022.10068481(109-112)Online publication date: 26-Nov-2022
https://doi.org/10.1109/ISCMI56532.2022.10068481

Index Terms

An improved merge search algorithm for the constrained pit problem in open-pit mining
1. Applied computing
  1. Operations research
2. Mathematics of computing
  1. Discrete mathematics
    1. Combinatorics
      1. Combinatorial optimization

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cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

July 2019

1545 pages

ISBN:9781450361118

DOI:10.1145/3321707

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 13 July 2019

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GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Cited By

Pathiranage INeumann A(2024)Multi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612008(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612008
Omidvar MLi XYao X(2022)A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part IIIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313083526:5(823-843)Online publication date: Oct-2022
https://doi.org/10.1109/TEVC.2021.3130835
Rahimi IPicard TMorabito APampalis KAbignano AGandomi A(2022)Comparison of Trajectory and Population-Based Algorithms for Optimizing Constrained Open-Pit Mining Problem2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI56532.2022.10068481(109-112)Online publication date: 26-Nov-2022
https://doi.org/10.1109/ISCMI56532.2022.10068481

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