Is Truck Queuing Productive? - SMP2014 - D Kostyuk… · Is Truck Queuing Productive? Study of...

Is Truck Queuing Productive?Study of truck & shovel operations productivity using simulation platform MineDES

Dmitry KostyukSpecialist Scientist, Group Resource and Business Optimisation25 November 2014

Contents

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014 Slide 2

Simulation Modeling in Mine Planning

Truck and shovel operations simulation platform MineDES

• Overview

• Advantages

• Benchmarking and validation

Case Study

Conclusions

Mine Planning Challenges


• Mines will continue to increase in depth, scale and complexity.

• There is a strong demand for mine planners to:

–Produce achievable, optimized plans for these mines–Appropriately size equipment fleets–Design efficient, effective mine access systems–Accurately estimate mining system productivity

• Getting it wrong can negatively impact project NPV.

Conventional Mine Planning


• Analytical methods – formula-based approach

• High-level of abstraction – BIG PICTURE point of view

• Normally doesn’t account for the impact on overall mining system productivity such factors as equipment interactions, parameters variability, randomness, uncertainty etc…

• Fails to describe systems with dynamic behavior featuring:

– non-linear behavior– non-intuitive influences between variables– time and causal dependencies– uncertainty, randomness and large number of parameters

Mine Planning using Simulation Modeling


Simulation Modeling• Method of solving problems that

can’t be calculated analytically• Cheap and risk free experiments

(“what if ?” studies)• Efficient for analyzing systems with

dynamic behavior

Analytical Modeling vs. Simulation Modeling


Arrivals: on average trucks / hour

Loading time exponentially distributed:

1/ – mean loading time

Shovel utilization:

Average waiting time: Average queue length:

Loading time arbitrary distributed:


Shovel utilization:

Average waiting time: ,where is coefficient of variation of loading time.

Single loaderPoisson stream (independent arrivals)

Case #1: Case #2:

Queuing theory

Analytical Modeling vs. Simulation Modeling


Arrivals: on average trucks / hour

Multiple (K) loaders

Loading time exponentially distributed:

1/ – mean loading timeShovel utilization:

Average waiting time: ,

where P! ,

and !∑

!

Loading time arbitrary distributed:


Poisson stream (independent arrivals)

ANALYTICAL SOLUTION DOES NOT EXIST STARTING FROM HERE AND FOR ANY FURTHER COMPLICATION OF THE PROCESS!!!

Case #3: Case #4:

Queuing theory

Advantages of Simulation Modeling


• Enabling system analysis, and to find solutions where other methods fail

• Once appropriate level of abstraction is selected, development of a simulation model is a more straightforward process than analytical modeling – less intellectual efforts, scalable, incremental and modular

• The structure of a simulation model naturally reflects the structure of the real system – it is visual, easy to verify and communicate to other people

• Any state of the model is measurable and any entity, which is not below abstraction level is tractable – sensitivity analysis, statistical analysis

• Ability to play and animate the system

• Simulation models are a lot more convincing than Excel spreadsheets or Power Point slides

Contents




• Overview

• Advantages


Case Study

Conclusions

MineDES - What is it?


• Truck & shovel operations simulation tool that can be used to estimate mining movement and processing capability in the following dimensions:

– productivity statistics– bottleneck processes and infrastructure– truck queuing statistics– the impact of different crew and maintenance schedules– the impact of unscheduled random equipment and road sector downtime – the influence of road maintenance vehicles and light vehicles on congestion – the capacity of particular pit ramps, and the whole road network, to support planned material

movements.

• Primary design concept was to focus application on addressing strategic mine planning questions, but secondly to be applicable in the short term planning environment.

MineDES Features


• Truck dynamics are calculated using full rim-pull curve data, taking into account truck payload as well as road gradient and quality

• A fast, purpose-built simulation engine, and the modelling of trucks as agents to ensure realistic and intelligent behavior

• Truck dispatch is predicated upon attempting to achieve a user-defined mining rate at each mining face

• Realistic and flexible modelling of traffic rules at complex intersections

• Modelling of payload and loading/dumping time variability

• Flexibility in assigning legal digging and dumping destinations to different truck sets within each truck fleet

• The optional application of a wide range of scheduled and unscheduled downtime for all mobile and static material processing infrastructure.

• Integrated 3D visualization engine

Advantages of truck & shovel simulator MineDES


• In-house software development project with more than 4 years development history.

• Developed from scratch and doesn’t use any commercial simulation engines.

• Designed to address both long- and short-term mine planning problems

• Intuitive, flexible and fit for purpose. Development of a simulation scenario is straightforward process, which does not require programming skills and a lot of intellectual effort.

• Benchmarked against other industry standard software products and tested in real operations environment

MineDES Benchmarking and Validation


600 m

0

5

10

15

20

25

30

35

40

45

50

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

MineDES

TALPAC

Truck dynamics model benchmarking against industry standard software

Truc

k sp

eed

(km

/h)

Distance (km)

Testing against real truck data.


113.6

113.8

114

114.2

114.4

114.6

114.8

115

115.2

115.4

17 17.2 17.4 17.6 17.8 18 18.2

Y (k

m)

X (km)

GPS real truck data plot:

05

10152025303540

0 5 10 15 20

00.5

11.5

22.5

33.5

44.5

0 5 10 15 20

Truck ground speed profile. GPS vs. MineDES

Truck travel distance profile. GPS vs. MineDES

MineDES model - design view: Experiment results:

Truc

k sp

eed

(km

/h)

Dis

tanc

e (k

m)

Travel time (min)

Contents




• Overview

• Advantages


Case Study

Conclusions

Case Study. Optimal fleet size.


Truck Loading Time Distribution Graph [sec]:

Truck Payload Distribution Graph [tonnes]:

Case Study. Optimal fleet size.


• A minimum 8 trucks are required to maximize productivity.

• Adding more trucks to the fleet simply increases overall queuing time with no additional aggregate material movement - in fact, adding extra trucks above 8 can lead to an insignificant decrease in productivity (< 1%) due to increased traffic congestion.

Case Study. Optimal fleet size


The simulation experiment has shown that even in a simple case where non-deterministic behaviour is quite limited and the road network is simple (point-to-point), in an optimized configuration, we should expect to see trucks queuing at shovels to a not insubstantial extent. The simple financial model shows that this queuing is protective of productivity and operational value.

Conclusions


• A new truck-shovel simulation tool called MineDES has been introduced

• A simulation case study was undertaken using MineDES to address the question of whether having queuing trucks could be a feature of an optimally productive mining operation.

• Our experiments have shown that in the case where non-deterministic factors are present in the system, typical of all real mining operations to a greater or lesser extent, then some degree of truck queuing will be observed in the most productive of configurations.

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