Towards solving large-scale precedence constrained production scheduling problems in mining

Angus Kenny, Xiaodong Li, Andreas T. Ernst, Dhananjay Thiruvady

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

Pit planning and long-term production scheduling are important tasks within the mining industry. This is a great opportunity for optimisation techniques, as the scale of a lot of mining operations means that a small percentage increase in efficiency can translate to millions of dollars in profit. The precedence constrained production scheduling problem (PCPSP) combines both of these aspects of mine optimisation and aims to find a solution which tells a mining company what part of the orebody to mine, and at what time during the life of the mine. This paper presents a GRASP-Mixed Integer Programming hybrid metaheuristic algorithm for solving the PCPSP which consists of two parts: a fast, period-by-period, random construction phase and a local improvement heuristic. It is compared to the current published state-of-the-art results on well known benchmark problems from minelib [5] and is shown to give better quality results in four of the six instances, and within 2% of the LP upper bound in the remaining two. The PCPSP is a good candidate for hybrid metaheuristics as the size of the problems make solving them with mathematical solvers alone intractable.

Original languageEnglish
Title of host publicationProceedings of the 2017 Genetic and Evolutionary Computation Conference
Subtitle of host publication2017 Genetic and Evolutionary Computation Conference, GECCO 2017; Berlin; Germany; 15 July 2017 through 19 July 2017; Code 128633
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1137-1144
Number of pages8
ISBN (Electronic)9781450349208
DOIs
Publication statusPublished - 1 Jul 2017
EventThe Genetic and Evolutionary Computation Conference 2017 - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017
http://gecco-2017.sigevo.org/index.html/HomePage.html

Conference

ConferenceThe Genetic and Evolutionary Computation Conference 2017
Abbreviated titleGECCO 2017
CountryGermany
CityBerlin
Period15/07/1719/07/17
OtherA Recombination of the 26th International Conference on Genetic Algorithms (ICGA) and the 22nd Annual Genetic Programming Conference (GP).

The Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms,genetic programming, ant colony optimization and swarm intelligence, complex systems (artificiallife/robotics/evolvable hardware/generative and developmental systems/artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more.
Internet address

Keywords

  • Applied computing
  • Hybrid algorithms
  • Mine planning
  • Mixed integer programming

Cite this

Kenny, A., Li, X., Ernst, A. T., & Thiruvady, D. (2017). Towards solving large-scale precedence constrained production scheduling problems in mining. In Proceedings of the 2017 Genetic and Evolutionary Computation Conference: 2017 Genetic and Evolutionary Computation Conference, GECCO 2017; Berlin; Germany; 15 July 2017 through 19 July 2017; Code 128633 (pp. 1137-1144). Association for Computing Machinery (ACM). https://doi.org/10.1145/3071178.3071241