启发式
整数规划
调度(生产过程)
计算机科学
数学优化
预处理器
生产计划
运筹学
生产(经济)
数学
人工智能
经济
宏观经济学
作者
Orlando Rivera Letelier,Daniel Espinoza,Marcos Goycoolea,Eduardo Moreno,Gonzalo Muñoz
出处
期刊:Operations Research
[Institute for Operations Research and the Management Sciences]
日期:2020-08-27
卷期号:68 (5): 1425-1444
被引量:35
标识
DOI:10.1287/opre.2019.1965
摘要
Production scheduling is a large-scale optimization problem that must be solved on a yearly basis by every open pit mining project throughout the world. Surprisingly, however, this problem has only recently started to receive much attention from the operations research community. In this article, O. Rivera, D. Espinoza, M. Goycoolea, E. Moreno, and G. Muñoz propose an integer programming methodology for tackling this problem that combines new classes of preprocessing schemes, cutting planes, heuristics, and branching mechanisms. This methodology is shown to compute near-optimal solutions on a number of real-world planning problems whose complexity is beyond the capabilities of preexisting approaches.
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