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Benchmarks for the Coal Processing and Blending Problem

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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  • (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
  • (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
  • (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
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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
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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Publication History

Published: 20 July 2016

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Author Tags

  1. benchmark
  2. blending problem
  3. combinatiorial optimisation
  4. evolutionary algorithm

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  • Research-article

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  • ARC Discovery Grant

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GECCO '16
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GECCO '16: Genetic and Evolutionary Computation Conference
July 20 - 24, 2016
Colorado, Denver, USA

Acceptance Rates

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

View all
  • (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
  • (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
  • (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
  • (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
  • (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
  • (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
  • (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

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