A new model for automated pushback selection

被引：10
|
作者：
Jelvez, Enrique [1 ,2 ]
Morales, Nelson [1 ,2 ]
Askari-Nasab, Hooman [3 ]
机构：
[1] Univ Chile, Adv Min Technol Ctr, Av Tupper 2007, Santiago 8370451, Chile
[2] Univ Chile, Dept Min Engn, Delphos Mine Planning Lab, Santiago, Chile
[3] Univ Alberta, Sch Min & Petr Engn, Dept Civil & Environm Engn, Min Optimizat Lab, Edmonton, AB, Canada
来源：
COMPUTERS & OPERATIONS RESEARCH | 2020年 / 115卷
关键词：
Long-term open-pit mine planning; Nested pits; Pushback selection; Mixed integer programming; DESIGN; UNCERTAINTY; PSEUDOFLOW; ALGORITHMS;
D O I：
10.1016/j.cor.2018.04.015
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The design of pushbacks is essential to long-term open pit mine scheduling because it partitions the pit space into individual units, controlling ore and waste production. In this paper, a new model is proposed for the pushback selection procedure, which consists of characterizing the potential pushbacks based on the comprehensive family of nested pits and selecting those ones that meet a set of criteria, for instance, bounded ore and waste. An advantage of this method is the possibility to automate the pushback selection methodology, applying well-defined criteria for the selection and reducing the time employed in the planning task. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页数：9
相关论文
共 50 条
  • [31] Automated model selection (AMS) on finite mixtures: A theoretical analysis
    Ma, Jinwen
    2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, 2006, : 4139 - 4145
  • [32] Model Selection for Production System via Automated Online Experiments
    Dai, Zhenwen
    Ravichandran, Praveen
    Fazelnia, Ghazal
    Carterette, Ben
    Lalmas-Roelleke, Mounia
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [33] Fuzzy Clustering with Automated Model Selection: Entropy Penalty Approach
    Li, Tao
    Ma, Jinwen
    PROCEEDINGS OF 2018 14TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP), 2018, : 571 - 576
  • [34] A Fast Automated Model Selection Approach Based on Collaborative Knowledge
    Sun, Zhenyuan
    Chen, Zixuan
    He, Zhenying
    Jing, Yinan
    Wang, X. Sean
    DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2020), PT I, 2020, 12112 : 655 - 662
  • [35] BYY Harmony Learning on Weibull Mixture with Automated Model Selection
    Ren, Zhijie
    Ma, Jinwen
    ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT I, PROCEEDINGS, 2008, 5263 : 589 - 599
  • [36] TextBrew: Automated Model Selection and Hyperparameter Optimization for Text Classification
    Desai, Rushil
    Shah, Aditya
    Kothari, Shourya
    Surve, Aishwarya
    Shekokar, Narendra
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (09) : 748 - 754
  • [37] Model Selection for Continuous Operation of Automated Vulnerability Assessment System
    Jung, Soohyun
    Furumoto, Keisuke
    Takahashi, Takeshi
    Shiraishi, Yoshiaki
    PROCEEDINGS OF THE 2023 WORKSHOP ON RECENT ADVANCES IN RESILIENT AND TRUSTWORTHY ML SYSTEMS IN AUTONOMOUS NETWORKS, ARTMAN 2023, 2023, : 11 - 15
  • [38] Automated model selection for multivariate anomaly detection in manufacturing systems
    Engbers, Hendrik
    Freitag, Michael
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024,
  • [39] An Automated Approach to Salt Selection for New Unique Trazodone Salts
    Emily C. Ware
    D. Robert Lu
    Pharmaceutical Research, 2004, 21 : 177 - 184
  • [40] An automated approach to salt selection for new unique trazodone salts
    Ware, EC
    Lu, DR
    PHARMACEUTICAL RESEARCH, 2004, 21 (01) : 177 - 184
12345