System Skills Project

8/2/2019 System Skills Project

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OPTIMISATION OF CREW AND

RESOURCE ALLOCATION IN READYMIX

CONCRETE DELIVERY

Team: GEM

Michael Smytheman 3185975

Gleb Zinger 3219727

Eric Luu 3207175

Submitted on 9 July 2011


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AbstractTownhouse type developments have returned to the favour of local planning

offices in many suburban areas, and it is thus of high importance to optimise the

foundation of construction - Ready Mix Concrete Pour Process. The objective of

this paper was to study and optimise this particular operation in the construction

project of Little Bay Terraces using computer simulation through AnyLogic. To

achieve this objective, field data was collected on-site through recorded

observations of the cycle and process times, and were then used to determine

the optimum combination of resources of trucks and crew. Whilst being a labour-

intensive process, extensive idle time of both the pump and the spreading crewof up to 50% were observed, as well as redundant truck queues, placing

unnecessary excess costs on the RMC pour process. The scheduling of the truck-

mixers inter-arrival time was not considered a variable for this particular

process. The cost-effectiveness of the RMC pour process could be analysed by

studying the impact of additional resources on the idle time and total time.

Increasing the number of resources to 9 trucks and 2 crews saw an improvement

of 122 minutes (33%) over the initial site observation of 6 trucks and 1 crew.

Adequate utilisation of both the pump and the spreading crew was maintained

and improved upon, resulting in an efficient and effective execution of the

operation and a global cost reduction.


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Contents

OPTIMISATION OF CREW AND RESOURCE ALLOCATION IN READYMIX CONCRETE

DELIVERY...............................................................................................................1

Abstract.................................................................................................................2

Introduction...........................................................................................................4

Literature Review..................................................................................................5

Case Study.............................................................................................................6

Symbolic Modelling................................................................................................7

Data Collection......................................................................................................8Model Development.............................................................................................10

Results Analysis...................................................................................................11

Conclusion...........................................................................................................13

Reference............................................................................................................14

Appendix A: Data Collection................................................................................15

Appendix A: Data Collection


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ReadyMix Concrete Delivery CVEN4104 Major Project

Introduction

Ready mixed concrete (RMC) placing is a major operation in many countries. Thisis particularly true in the city of Sydney where townhouse-type developmentshave regained its prominence in the inner west. Concrete must be batched

remotely and delivered to construction sites by truckmixers and thus theproductivity of RMC placing is of great importance to the productivityimprovement of not just the housing construction industry, but the entire sector.With a more efficient and optimised process, costs incurred would be reducedproportionally, leading to a global cost reduction in the industry.

The aim was to determine the optimum resource allocation of crews andequipment for RMC, and determine the best dispatch rate of trucks from theconcrete mixing site to the work site. Past literature has concentrated ondetermining the optimum inter-arrival rate for trucks being dispatched. This willbe looked at in this investigation, however this investigation will also look at theoptimum number of spreader crews, and how the allocation of crews affects the

overall performance of concrete delivery.

This information is vital to construction engineers. Construction engineers needto know the correct interarrival rate to use, as this needs to match the number ofconcrete trucks arriving at the site with the speed at which the crews are able topump and spread the concrete. If the inter-arrival rate is not accurately matchedto the capacity of the crews this can result in excessive queuing of trucks on site,or idle crews.


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Literature Review The productivity of RMC placing is of great importance to the productivity

improvement of the whole construction industry and as such, there have been anumber of previous researchers who have investigated this particular process.

An appropriate and well-timed supply of RMC to a construction site was

considered to be a major factor affecting productivity of concreting operations

and that the RMC supplier should provide a continuous flow of concrete to ensure

no interruptions to the placing and spreading operation. (Anson et al., 1996,

1998; Wang et al., 1995, 2000, 2001).

This process has since been utilised and simulated via this ideology. (Wang, Teo,

Ofori, 2001). From their simulation results, it was confirmed that the truckmixers

arrival pattern is the most important factor in determining RMC placingproductivity whilst ensuring a relatively high utilisation of the pump equipment.

One of the main assumptions of their simulation was that the placing process

was not considered a variable which was one of the major factors that we have

analysed in our case study.

In addition to the lack of variability of crew performance, their model also

featured a queuing system with an infinite number of trucks which is not

indicative of a real-life process where there would be a re-use of truckmixers.

Their reasoning for this assumption was due to the relatively low placing rate on

the construction site as well as the relatively large number of truckmixers at the

batching plant which was not considered to generate significant errors. This is

obviously a site-dependent issue with its own limitations.

One of the focal points of our case study was the interaction of the pumping

process and the spreader crews on the construction site. The observed high idle

times for the crew as well as the extensive truck queues were suggestive of a

low synchronisation of processes within the system. It was apparent that simply

adjusting the inter-arrival times to match the RMC supply from the batch plant

would not be adequate for this particular operation, and that an optimisation of

the combination of trucks and spreader crews was a necessary endeavour in

order to improve overall productivity as well as cost reduction.


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Case StudyA case study was undertaken at a construction site at the corner of Harvey

Street and Brodie Avenue, Little Bay. The approximately property boundary isshown in the image below, where a new multistorey residential development is

under construction on a land parcel with an area of approximately 10,300m2. It is

part of a new subdivision on the land where the old hospital once stood, and

several construction projects are being undertaken in the area. The building was

designed by Bates Smart Architects and constructed by Brookfield Multiplex.


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Construction of the building commenced in November 2010, and is due to be

completed early 2012. Observations of the construction process were made on

Friday 8th April 2011. At the time the site had been excavated and prepared for

the first major concrete pour of the ground slab. The process of pouring the

concrete was observed and data collected using a logbook.

The total volume of concrete poured was 145m3. Holcim was the company

delivering the concrete from Alexandria, and there were 6 trucks used in the

process. On arrival at the site the trucks queued up until there was a free space

at the concrete pump. The concrete pump had space for two trucks to

simultaneously dump concrete. A crew then operated the pump and pumped the

concrete into the formwork to create the slab.


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Symbolic ModellingA symbolic model was developed using STROBOSCOPE. The model is presented

below.


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Data CollectionData was collected at the site on Friday 8th April from 10.30am to 4.00pm. Data

was recorded in the form of a logbook, and photographs of various activities

were also recorded. The full logbook data is provided in Appendix A.

Cycle and process times were obtained by keeping a log book of the times that

events started and finished at the site, and by recording truck numbers printed

on the back of all of the trucks as shown in the image below.

Dump time for the concrete at the site was obtained by observation andrecording the start and end times of events at the site.


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Access was not granted into Holcims ReadyMix site at Alexandria, so accurate

loading times for concrete into the trucks could not be recorded. Loading time

was obtained by contacting the supplier, Holcim, by phone and getting an

average loading time for the size vehicles being used. This data will be treated

as a deterministic time.

Return trip times for each truck was obtained by keeping a log of the time each

truck left and arrived at the work site. Then the load time obtained from Holcim

was subtracted from this return trip time, and the remaining time divided by 2 to

estimate the haul and return times.

The total number of trucks, capacity of the trucks, and total concrete pour

volume was obtained from the site manager. The total pour volume was 145m3,

using 6 trucks with a 5.6m3 capacity each.


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A summary of the data obtained is provided in the following table.

Cycle # Load Haul Deliver

Retur

n Truck Away Time

1 10 28 17 28 66

2 10 26.5 15 26.5 63

3 10 27 19 27 64

4 10 22.5 15 22.5 55

5 10 25.5 16 25.5 61

6 10 18 13 18 46

7 10 19.5 17 19.5 49

8 10 17.5 22 17.5 45

9 10 18 14 18 46

10 10 17 22 17 44

11 10 19.5 18 19.5 49

12 10 20 21 20 50

13 10 24.5 19 24.5 59

14 10 29 19 29 68

15 10 24.5 20 24.5 59

16 10 23 19 23 56

17 10 19 22 19 48

18 10 20.5 20 20.5 51

19 10 23

20 10 26

21 10 18

The truckmixers inter-arrival time was calculated by dividing the total time by

the number of observed cycles.


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Model DevelopmentAnyLogic version 6.4.1 was used to develop a computer simulation of the

process.

Our initial/simple model was developed on the pretence that matching the

Ready-Mix Concrete supply to the site requirement was the fundamental pattern

to consider. The scheduling of the truckmixers inter-arrival time was perceived

to be one of the main factors to model.

However upon careful observation of the construction site, it became apparent

that the pump crew was idle for 50% of the entire concrete dumping time. The

main problem with the simple model was that it would take the same time to

dump two trucks simultaneously as it would one, whereas in reality it would take

longer since there would be more concrete to spread. This issue is rectified in the

advanced model with the inclusion of the spreading crew which was the actual

time limiting factor. After gathering information that the main reason for the slow

dumping times was due to the speed at which the ground crew could operate,

and in addition to the theoretical minimum pump time of 6 minutes, the

spreading crew element was introduced as a means of rectifying this issue. The

concrete pump was also observed to be rarely idle, and there were frequent

truck queues on site.

A more advanced model was developed based on observations from the site,

which more accurately describe the process at hand. Due to the limitations ofthe system the full process could not be modelled in STROBOSCOPE, however,

the tools made available to us in the AnyLogic software package allowed us to

overcome those limitations.

To rectify the problem of having a continuous process we used the batch object

tool in our AnyLogic model. The concrete would start in entity sizes of 1,000cm3

but would get batched into a 5.6m3 entity for delivery and then un-batched back

to 1,000cm3 while being dumped on site. This would allow the concrete to flow

in small packet volumes through the pump and spread crew. This also required

that we reduce the pumping and spreading time to match the smaller entity

volume, this was simply done by dividing the time distribution by the number of

concrete entities in the batch. Once the spreading process is completed for one

truck load worth of concrete, the small packets get batched once more to a

5.6m3 entity and the truck and available pump port both split off from the

concrete entity. This way the truck has to wait until one truckload has flowed

through the pumping and spreading process before it leaves.

Both the simple and advanced models have been developed and analysed. The

simple model is useful for when considering interarrival rates while not

considering the number of spreader crews, whereas the advanced model is

useful for when determining the optimum ratio of trucks to spreader crews.


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Results Analysis (simple model)Based on the initial simple model of the system, and the data recorded, the

simulation was run to determine the optimum inter-arrival time. The inter-arrivaltime is the rate at which the trucks are dispatched from the concrete mixing

plant. This was done by running the simulation using a set seed value of 1, and

getting the average results for multiple runs. The output of these results is

presented below.

Table 1: Optimum Inter-arrival Rate (1 Pump Truck)Inter-arrival

Rate (minutes)

Overall Process

Time (minutes)

Pump Port

Utilisation

Maximum Queue

Size (trucks)

6 288 100% 9

8 292 83% 3

10 318 78% 1

12 364 64% 0

14 415 61% 0

16 466 53% 0

18 518 50% 0

20 570 43% 0

22 622 40% 0

Based on these results it can be seen that the lower the inter-arrival rate, the

faster the overall process time will be. However there is a trade off in terms of

the queue size at the site, and the utilisation of the two pump ports. Depending

on the costs associated with a longer process time and having more trucks

involved in the process, the best inter-arrival rate is probably 10 minutes. This

rate results in a low overall process time (318 minutes), with a high pump port

utilisation rate and a low number of trucks queued on site.

If the overall process time was needed to be reduced significantly, another pump

truck could be employed, resulting in a total of 4 pump port locations. The

results of this are shown in the following table.

Table 2: Optimum Interarrival Rate (2 Pump Trucks)Interarrival Rate

(minutes)

Overall Process

Time (minutes)

Pump Port

Utilisation

Maximum Queue

Size (trucks)

2 160 100% 10

3 159 88% 4


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4 174 72% 5

5 185 67% 2

6 214 59% 0

7 251 47% 0

8 268 49% 0

9 289 47% 0

10 319 40% 0

It can be seen that if a second pump truck was added to the process this would

significantly improve the overall process time. Depending on costs, the optimum

inter-arrival rate is around 5 minutes. This would result in an overall process time

of 185 minutes, with 67% pump utilisation rate. This is almost half the processtime when only 1 pump truck is used. If the inter-arrival time was decreased to

only 4 minutes the overall process time would decrease even further, however

the maximum queue length would increase to 5 trucks which might be

impractical.

To optimise resources, based on the initial simple model, the best option would

be to employ two pump trucks and dispatch the concrete trucks from the batch

site at a rate of 1 truck every 5 minutes. This would result in a low process time,

reducing costs associated with employing crews, and will result in less queuing

of trucks at the site. Less queues at the site mean lower costs to the concrete

delivery company (Holcim).


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Results Analysis (advanced model)Based on the advanced model of the system, and the data recorded, the

simulation was run to determine the optimum combination of trucks and crews.Our expectations before running the simulation were as follows:

Simply increasing the number of trucks will have little effect on the process

duration as in our real life observations the concrete pump was rarely idle and

there were frequent truck queues on site.

Raising the number of spreader crews in the advanced model would

dramatically reduce the process duration, as we knew that the ground crew

was the limiting factor that was slowing down the concrete pouring process.

The duration of the process could probably be halved since the pump crewwas observed to be idle for around 50% of the entire concrete dumping time

Table 3: Optimum Truck/Crew Ratio (1 Pump Truck)

TrucksCrew

sPump CrewUtilisation

SpreadCrew

Utilisation

Maximum Queue

TotalTime

(minutes)

6 1 37% 91% 1 551

7 1 37% 91% 2 551

8 1 37% 91% 3 551

6 2 57% 74% 0 341

7 2 62% 82% 0 308

8 2 66% 83% 1 302

9 2 66% 83% 2 302

10 2 66% 83% 3 302

6 3 59% 52% 0 326

7 3 62% 54% 1 307

8 3 67% 59% 1 284

9 3 71% 62% 1 269

10 3 72% 63% 2 264


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Note: Utilisation values are lower than what is shown in the table above.

AnyLogic takes the initial load and haul time into account which deflates the

actual utilisation percentages.


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As expected with 1 Crew, increasing the number of trucks had little effect on the

total time. In our particular simulation, it actually resulted in zero improvement

in total time which cited the initial problem of truck queues in our initial

observation.

However with 2 Crews, increasing the number of trucks had a substantial effecton the total time which validates our observation that the ground crews were the

limiting factor in the process. By adding an extra crew and increasing the

number of trucks to 7, an improvement of 243 minutes was observed compared

to the initial setup of 6 trucks and 1 crew. This saw an improvement of 44% in

the total time which was very close to our expectation due to the observed pump

crew idle time of 50%. Adding extra trucks was deemed to be unnecessary as

cost-to-productivity would have reached negative gains.

Total time for 2 crews was limited to 302 minutes with an infinite number of

trucks which allowed us to come to the conclusion that the combination of 7

trucks and 2 Crews resulted in the most optimum synchronization of the two

processes.

By increasing the number of Crews to 3, the total time was further reduced but

not without a few disadvantages. Spread Crew Utilisation hovered between 50%

and 65% which meant that spread crew idle time had become an issue.

Employing a combination of 10 Trucks and 3 Crews saw an improvement of just

44 minutes over 7 Trucks and 2 Crews. Queues at the pump truck also reached a

maximum of 2 trucks which when combined with the additional 3 trucks and

spreader crew was concluded to be an unnecessary cost sacrifice to achieve a

faster total process time.


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Conclusion


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Reference


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Appendix A: Data Collection

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