INCORPORATING CYCLE TIME DEPENDENCY IN TRUCK-SHOVEL

MODELING

Angelina Anani

Kwame Awuah-Offei, PhD

OUTLINE

• Motivation• Objectives• Methodology

– DES Modeling– Correlation Testing

• Results & Discussion• Conclusion

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• 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.

MOTIVATION

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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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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.

METHODOLOGY

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DES Modeling of Truck-Shovel Systems with Bunching

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MODEL DEMO

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

Test for Truck Cycle Time Dependence

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Effect Of No. Of Slow Operators On Loads/Shift

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Effect Of No. Of Slow Operators On Cycle Time

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Effect of slow truck speed on cycle time

Effect of slow truck speed on load/shift

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Truck 1 2 3 4 5

1 1(<0.0001)

       

2 0.837(<0.0001)

1(<0.0001)

     

3 0.607(<0.0001)

0.710(<0.0001)

1    

4 0.311(<0.0001)

0.346(<0.0001)

0.637(<0.0001)

1  

5 0.249(<0.0001)

0.289(<0.0001)

0.541(<0.0001)

0.813(<0.0001)

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Correlations (p-values in parenthesis) of truck cycle times with variable speed

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Truck 1 2 3 4 5

1 1        

2 0.570(<0.0001) 1      

3 0.459(<0.0001)

0.745(<0.0001) 1    

4 0.056(0.12)

-0.009(0.79)

-0.046(0.19) 1  

5 0.795(<0.0001)

0.408(<0.0001)

0.337(<0.0001)

0.124(<0.0001) 1

Correlations (p-values in parenthesis) of truck cycle times with variable speed

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Effect of one slow truck on cycle time

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Effect of two slow trucks on cycle time

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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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Questions

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