Optimization Strategies for Resource-Constrained Project Scheduling Problems in Underground Mining

Alessandro Hill (), Andrea J. Brickey (), Italo Cipriano (), Marcos Goycoolea () and Alexandra Newman ()
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Alessandro Hill: Department of Industrial and Manufacturing Engineering, California Polytechnic State University, San Luis Obispo, California 93407
Andrea J. Brickey: Mining Engineering and Management, South Dakota School of Mines and Technology, Rapid City, South Dakota 57701
Italo Cipriano: Alicanto Labs, Universidad Adolfo Ibáñez, Santiago 7941169, Chile
Marcos Goycoolea: School of Business, Universidad Adolfo Ibáñez, Santiago 7941169, Chile
Alexandra Newman: Department of Mechanical Engineering, Colorado School of Mines, Golden, Colorado 80401

INFORMS Journal on Computing, 2022, vol. 34, issue 6, 3042-3058

Abstract: Effective computational methods are important for practitioners and researchers working in strategic underground mine planning. We consider a class of problems that can be modeled as a resource-constrained project scheduling problem with optional activities; the objective maximizes net present value. We provide a computational review of math programming and constraint programming techniques for this problem, describe and implement novel problem-size reductions, and introduce an aggregated linear program that guides a list scheduling algorithm running over unaggregated instances. Practical, large-scale planning problems cannot be processed using standard optimization approaches. However, our strategies allow us to solve them to within about 5% of optimality in several hours, even for the most difficult instances.

Keywords: underground mine planning; resource-constrained project scheduling; constraint programming; mathematical programming (search for similar items in EconPapers)
Date: 2022
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