Figure 1. Illustration of main components and operation process of the ST system.

Table 1 summarises the main characteristics of recent papers on the ST system in terms of the authors, publication year, journals, country of the first author, problem types and solution techniques. As shown in Table 1, some findings are given as follows. First, most research considered the mixture of shovels and trucks, e.g., determining the best matching factor; selection with sizing of trucks and shovels; dispatching a fleet of trucks to one shovel. In comparison, investigation of individual shovel or truck management system is rare relatively. Second, most of studies on the ST system belong to a kind of the planning-type optimisation problems such as the ST allocation/dispatching/assignment/matching problem. In contrast, few studies focused on more complicated scheduling-type problem based on the application of classical machine scheduling theory. Note that planning deals with the optimisation problems of resource capacity, facility design, equipment allocation and personnel deployment without considering timing factors. Scheduling is concerned with the efficient allocation of equipment units to jobs (operations) and sequencing the operations on each equipment unit with timing factors. For example, the parallel-machine scheduling with sequence-dependent set-up times was recently applied to a real-world mine excavators timetabling case [21]. Indeed, the dynamic vehicle routing problem could be applied to the routing optimisation of open-pit truck fleets [50,51]. Third, most solution techniques for the ST problems are mainly based on the formulation of MIP models with the use of exact MIP solvers. More efficient solution approaches, such as metaheuristic algorithms, which can efficiently solve large-scale instances, are relatively occasional. Finally, for scheduling (dispatching and sequencing) a fleet of trucks associated with a shovel, most existing mathematical programming models are relatively basic. To be more applicable in practice, the ST scheduling models should be extended by considering more actual requirements, such as the best matching factor, the selection of trucks/shovels, the layout of haulage roads, the queuing (e.g., waiting/idle times) of trucks in the scheduling process, and maintenance/failure of mining equipment, etc. 

Authors	Year	Country	Problem Types	Solution Techniques
Young and Rogers	2022	USA	Mine haul truck dumping process simulation	A high-fidelity modelling method
Liu et al.	2022	China	Mine excavators timetabling	Mixed integer programming and metaheuristics
de Carvalho and Dimitrakopoulos	2021	Canada	Integrated truck-dispatching and production	Reinforcement learning
Upadhyay et al.	2021	Canada	Production scheduling with shovel allocation	Mixed integer programming
Aguayo et al.	2021	Chile	Productivity and safety of shovel–truck system	Interaction analysis
Elijah et al.	2021	Kenya	Shovel–truck haulage optimisation	Queuing theory
Wang et al.	2021	China	Mine truck fuel consumption analysis	Regression analysis
Bakhtavar and Mahmoudi	2020	Iran	Shovel–truck allocation	Scenario-based robust optimisation
Basiri et al.	2020	Iran	Reliability assessment of shovel–truck system	Statistical methods
Zhang et al.	2020	China	Multi-objective unmanned truck scheduling	Improved genetic algorithms (NSGA-II)
Kansake and Frimpong	2020	USA	Estimate tire dynamic forces on haul roads	An analytical model
Shah and Rehman	2020	Pakistan	Shovel–truck allocation problem	Mixed integer programming
Ozdemir and Kumral	2019	Canada	A two-stage shove-truck dispatching system	A simulation-based optimisation approach
Dabbagh and Bagherpour	2019	Iran	Matching factor of shovel–truck system	Ant colony optimisation
Liu and Chai	2019	China	Routing optimisation of open-pit trucks	Mixed integer programming
Moniri-Morad et al.	2019	Iran	Capacity analysis of shovel–truck system	Discrete event simulation
Sun et al.	2018	China	Prediction of travel times of trucks	Machine learning techniques
Baek and Choi	2017	Korea	Design of a haul road for an open-pit mine	Douglas–Peucker algorithm
Dindarloo and Siami-Irdemoosa	2017	USA	Classification and clustering of shovels failures	Data mining techniques
Patterson, Kozan and Hyland	2017	Australia	Energy efficient shovel–truck scheduling	Mixed integer programming and metaheuristics
Bajany et al.	2017	South Africa	Shove-truck dispatching	Mixed integer programming
Burt et al.	2016	Australia	Mining equipment selection	Mixed integer programming
Chang et al.	2015	China	Open-pit truck scheduling	Mixed integer programming
Dindarloo et al.	2015	USA	Truck and shovel selection and sizing	Stochastic simulation
Rodrigo et al.	2013	France	Dynamic open-pit mine truck allocation	Simulation-and-optimisation framework
Choi and Nieto	2011	Korea	Haulage routing optimisation of mining trucks	Least-cost path algorithm with Google Earth
Souza et al.	2010	Brazil	Dynamic truck allocation in open-pit mining	Hybrid metaheuristic algorithms
Topal and Ramazan	2010	Australia	Mine equipment maintenance scheduling	Mixed integer programming
Choi et al.	2009	Korea	Haulage routing optimisation of mining trucks	Multi-criteria least-cost path analysis
Ercelebi and Bascetin	2009	Türkiye	Shovel–truck dispatching	Linear programming and queuing theory

3. In-Pit Crushing–Conveying (IPCC) System