Dragline operation modelling and task assignment based on mixed-integer linear programming

被引:1
|
作者
Liu, Haoquan [1 ]
Kearney, Michael P. [1 ]
Austin, Kevin J. [1 ]
机构
[1] Univ Queensland, Sch Mech & Min Engn, Mansergh Shaw Bldg, Brisbane, Qld 4072, Australia
关键词
Mixed-integer linear programming; Optimization; Mining robotics; Excavation planning; PRODUCTIVITY; AUTOMATION;
D O I
10.1007/s11081-018-9386-5
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Draglines are among the largest earthmoving machines in surface mining, where they are used to remove the waste material (overburden) sitting above a target mineral or coal deposit. The effectiveness of their operation is highly dependent on decisions made by their operators, including the sequence of positions at which they operate and the bulk material movement (what to dig and where to dump) during excavation at each position. In this paper, we formulate a mixed-integer linear program that captures the operational constraints imposed in dragline excavation to determine the optimal material movement for a prescribed sequence of dragline positions. Through a simulation study, we show that the dragline productivity can be improved by more effective assignment of material movement tasks to individual dragline positions. We find that the improvement made compared to using a greedy'digging and dumping strategy depends on the dragline positioning sequence and the terrain profile in the specific environment. The solutions in terms of what to dig and where to dump provide insights into the optimal digging and dumping patterns for different types of dragline positioning sequences, and contribute towards solving the overall dragline operation planning problem that includes the planning of the dragline positioning sequence.
引用
收藏
页码:1005 / 1036
页数:32
相关论文
共 50 条
  • [41] A Mixed-Integer Linear Programming Model for a Selective Vehicle Routing Problem
    Posada, Andrea
    Carlos Rivera, Juan
    Palacio, Juan D.
    APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2018, PT II, 2018, 916 : 108 - 119
  • [42] Mixed-integer programming in motion planning
    Ioan, Daniel
    Prodan, Ionela
    Olaru, Sorin
    Stoican, Florin
    Niculescu, Silviu-Iulian
    ANNUAL REVIEWS IN CONTROL, 2021, 51 : 65 - 87
  • [43] Prediction of folding type of proteins using mixed-integer linear programming
    Türkay, M
    Üney, F
    Yilmaz, Ö
    European Symposium on Computer-Aided Process Engineering-15, 20A and 20B, 2005, 20a-20b : 523 - 528
  • [44] Test Assembly for Cognitive Diagnosis Using Mixed-Integer Linear Programming
    Wang, Wenyi
    Zheng, Juanjuan
    Song, Lihong
    Tu, Yukun
    Gao, Peng
    FRONTIERS IN PSYCHOLOGY, 2021, 12
  • [45] Optimization of air vehicles operations using mixed-integer linear programming
    Schumacher, C.
    Chandler, P. R.
    Pachter, M.
    Pachter, L. S.
    JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2007, 58 (04) : 516 - 527
  • [46] Robust intersample crossing of target sets with mixed-integer linear programming
    Afonso, Rubens J. M.
    Galvao, Roberto K. H.
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2021, 31 (06) : 2411 - 2433
  • [47] A new cross decomposition method for stochastic mixed-integer linear programming
    Ogbe, Emmanuel
    Li, Xiang
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2017, 256 (02) : 487 - 499
  • [48] On the approximation of real rational functions via mixed-integer linear programming
    Papamarkos, N
    APPLIED MATHEMATICS AND COMPUTATION, 2000, 112 (01) : 113 - 124
  • [49] A Mixed-Integer Linear Programming Approach to Deploying Base Stations and Repeaters
    Fong, Silas L.
    Bucheli, Juan
    Sampath, Ashwin
    Bedewy, Ahmed M.
    Mare, Michael Di
    Shental, Ori
    Islam, Muhammad Nazmul
    IEEE COMMUNICATIONS LETTERS, 2023, 27 (12) : 3414 - 3418
  • [50] A multi-agent learning framework for mixed-integer linear programming
    Jing, Yuchen
    Liang, Binyan
    Li, Siyuan
    Liu, Feifan
    Zhao, Wei
    Liu, Peng
    INFOR, 2024, 62 (04) : 588 - 598