Joint stochastic short-term production scheduling and fleet management optimization for mining complexes

被引:38
作者
Both, Christian [1 ]
Dimitrakopoulos, Roussos [1 ]
机构
[1] McGill Univ, Dept Min & Mat Engn, COSMO Stochast Mine Planning Lab, FDA Bldg,3450 Univ St, Montreal, PQ H3A 0E8, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Short-term mine planning; Production scheduling; Fleet management; Stochastic mixed integer programming; Metaheuristics; OPEN-PIT; RISK; UNCERTAINTY; MINE; SIMULATION; MODEL;
D O I
10.1007/s11081-020-09495-x
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.
引用
收藏
页码:1717 / 1743
页数:27
相关论文
共 52 条
[1]   Mining fleet management systems: a review of models and algorithms [J].
Afrapoli, Ali Moradi ;
Askari-Nasab, Hooman .
INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT, 2019, 33 (01) :42-60
[2]  
Alarie S., 2002, INT J SURFACE MINING, V16, DOI [10.1076/ijsm.16.1.59.3408, DOI 10.1076/IJSM.16.1.59.3408]
[3]  
[Anonymous], 2002, THESIS
[4]  
[Anonymous], 2013, IBM ILOG CPLEX optimization studio
[5]   Predicting equipment requirements using SIMAN simulation - a case study [J].
Awuah-Offei, K ;
Temeng, VA ;
Al-Hassan, S .
TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY SECTION A-MINING TECHNOLOGY, 2003, 112 :180-184
[6]   Development of a scenario-based robust model for the optimal truck-shovel allocation in open-pit mining [J].
Bakhtavar, E. ;
Mahmoudi, H. .
COMPUTERS & OPERATIONS RESEARCH, 2020, 115
[7]  
Birge JR, 2011, SPRINGER SER OPER RE, P3, DOI 10.1007/978-1-4614-0237-4
[8]   Short-term scheduling of an open-pit mine with multiple objectives [J].
Blom, Michelle ;
Pearce, Adrian R. ;
Stuckey, Peter J. .
ENGINEERING OPTIMIZATION, 2017, 49 (05) :777-795
[9]   A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks Over Multiple Time Periods [J].
Blom, Michelle L. ;
Pearce, Adrian R. ;
Stuckey, Peter J. .
MANAGEMENT SCIENCE, 2016, 62 (10) :3059-3084
[10]   Attitudes toward elderly workers and perceptions of integrated age management practices [J].
Blome, Mikael Widell ;
Borell, Jonas ;
Hakansson, Carita ;
Nilsson, Kerstin .
INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS, 2020, 26 (01) :112-120