1. INCORPORATING CYCLE TIME
DEPENDENCY IN TRUCK-SHOVEL
MODELING
Angelina Anani
Kwame Awuah-Offei, PhD

2. OUTLINE
• Motivation
• Objectives
• Methodology
– DES Modeling
– Correlation Testing
• Results & Discussion
• Conclusion
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3. MOTIVATION
• Discrete event simulation (DES) Models assume truck cycle times
are independent and identically distributed (iid) random variables
• Removal of analyst from the data collection exercise, makes it
difficult to appreciate the effect of bunching on the iid assumption.
• Identifying bunching in raw VIMS data
• How is bunching modeled once identified
• Error surrounding uncertainty estimation
Truck bunching(clumping) refers to a group of two or more trucks along the same
route with evenly spaced schedules, running in the same location at the same time.
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4. OBJECTIVES
• Account for truck bunching due to slow trucks using
Arena® a DES simulation software based on the SIMAN
simulation language.
• Present a methodology to test for cycle time dependence
(i.e. whether truck cycle time data is iid or not).
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5. METHODOLOGY
Simulation in Arena
• The modeled system consists of a single shovel loading five
trucks
• Truck operators are modeled as entities and trucks as
transporters;
• The shovel and crusher are modeled as resources
• Use of an Arena® guided transporter
• AttrSpeedFactor defined to adjust the truck speed.
• Run for 30 replications of 10 hours each.
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6. DES Modeling of Truck-Shovel Systems
with Bunching
MODEL DEMO
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7. Test for Truck Cycle Time Dependence
Truck speeds, load/shift, cycle time, loading times, and
dumping times are sampled from simulation.
Pearson’s correlation
– Variable speed factor of trucks
– Variable number of slow trucks
– Speed of slow truck versus cycle times
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8. Effect Of No. Of Slow Operators On
Loads/Shift
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9. Effect Of No. Of Slow Operators On
Cycle Time
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10. Effect of slow truck speed on cycle time
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11. Effect of slow truck speed on load/shift
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12. Correlations (p-values in parenthesis) of
truck cycle times with variable speed
Truck 1 2 3 4 5
1 1
(<0.0001)
2 0.837 1
(<0.0001) (<0.0001)
3 0.607 0.710 1
(<0.0001) (<0.0001)
4 0.311 0.346 0.637 1
(<0.0001) (<0.0001) (<0.0001)
5 0.249 0.289 0.541 0.813 1
(<0.0001) (<0.0001) (<0.0001) (<0.0001)
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13. Correlations (p-values in parenthesis) of
truck cycle times with variable speed
Truck 1 2 3 4 5
1 1
0.570
2 1
(<0.0001)
0.459 0.745
3 1
(<0.0001) (<0.0001)
0.056 -0.009 -0.046
4 1
(0.12) (0.79) (0.19)
0.795 0.408 0.337 0.124
5 1
(<0.0001) (<0.0001) (<0.0001) (<0.0001)
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14. Effect of one slow truck on cycle time
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15. Effect of two slow trucks on cycle time
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16. CONCLUSION
• A DES model that accounts for bunching due to a slow
truck(s) is built using Arena®.
• Simple correlation tests between the cycle times of the trucks
can be used to identify bunching due to a slow truck(s).
• When truck bunching occurs, the iid assumption, inherent in
statistical goodness-of-fit tests, is not valid.
• Assuming iid in modeling, over-estimates productivity and the
uncertainty surrounding it.
• Identify the causes of bunching for system under study.
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17. Questions
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