research-article

Benchmarks for the Coal Processing and Blending Problem

Authors:

Sven Schellenberg,

Zbigniew MichalewiczAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 1005 - 1012

https://doi.org/10.1145/2908812.2908945

Published: 20 July 2016 Publication History

Abstract

In this paper we present a challenging problem that many decision makers in coal mining industry face. The coal processing and blending problem (CPBP) builds upon the traditional blending problem known in operations research (OR) by including decision variables around coal processing, novel constraints as well as arbitrary user-defined profit functions which express price bonuses and penalties. The added complexity turns the traditional blending problem into a challenging black-box optimisation problem. We give an informal and mathematical description of this problem and present nine real-world problem instances as benchmark. Finally, we provide preliminary results for solving the problem by using a Genetic Algorithm (GA) and compare the results with those from a commercial Linear Programming (LP) solver. The results show that the GA significantly outperforms the LP solver in many problem instances while being marginally worse in others.

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Cited By

Stimson MReid WNeumann ARatcliffe SNeumann F(2023)Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10254112(1-10)Online publication date: 1-Jul-2023
https://doi.org/10.1109/CEC53210.2023.10254112
Reid WNeumann ARatcliffe SNeumann FKrawiec K(2021)Advanced mine optimisation under uncertainty using evolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3463135(1605-1613)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3463135
Jeuken RForbes MKearney M(2020)Optimal blending strategies for coking coal using chance constraintsJournal of the Operational Research Society10.1080/01605682.2020.1811167(1-14)Online publication date: 3-Sep-2020
https://doi.org/10.1080/01605682.2020.1811167
Show More Cited By

Index Terms

Benchmarks for the Coal Processing and Blending Problem
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
2. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Linear programming
      2. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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ARC Discovery Grant

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GECCO '16

Sponsor:

SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Stimson MReid WNeumann ARatcliffe SNeumann F(2023)Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10254112(1-10)Online publication date: 1-Jul-2023
https://doi.org/10.1109/CEC53210.2023.10254112
Reid WNeumann ARatcliffe SNeumann FKrawiec K(2021)Advanced mine optimisation under uncertainty using evolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3463135(1605-1613)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3463135
Jeuken RForbes MKearney M(2020)Optimal blending strategies for coking coal using chance constraintsJournal of the Operational Research Society10.1080/01605682.2020.1811167(1-14)Online publication date: 3-Sep-2020
https://doi.org/10.1080/01605682.2020.1811167
Chakraborty SMitra A(2019)A hybrid multi-criteria decision-making model for optimal coal blendingJournal of Modelling in Management10.1108/JM2-08-2018-011214:2(339-359)Online publication date: 10-May-2019
https://doi.org/10.1108/JM2-08-2018-0112
Amini SVass CShahabi MNoble A(2019)Optimization of coal blending operations under uncertainty – robust optimization approachInternational Journal of Coal Preparation and Utilization10.1080/19392699.2019.157426242:1(30-50)Online publication date: 7-Feb-2019
https://doi.org/10.1080/19392699.2019.1574262
Leal Gomes Leite JArruda EBahiense LMarujo L(2019)Modeling the integrated mine-to-client supply chain: a surveyInternational Journal of Mining, Reclamation and Environment10.1080/17480930.2019.157969334:4(247-293)Online publication date: 5-Apr-2019
https://doi.org/10.1080/17480930.2019.1579693
Schellenberg SLi XMichalewicz Z(2017)Preliminary Study on Solving Coal Processing and Blending Problems Using Lexicographic OrderingAI 2017: Advances in Artificial Intelligence10.1007/978-3-319-63004-5_18(221-233)Online publication date: 9-Jul-2017
https://doi.org/10.1007/978-3-319-63004-5_18

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