Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Agent-based Physical Asset MaintenanceSimulation Modeling

Michael Thistlethwaite

02/09/10

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Introduction

Physical assets such as houses, motorways/roads, water pipes andelectrical networks need maintenance because the condition of aphysical asset deteriorates with time and usage. The risk of anasset failure (e.g., flooding) or not being able to provide therequired service quality (due to weak water pressure) increases asthe assets condition decreases. The cost of a repair/replacementprocess, including the liability incurred due to an asset failure, canbe very high. Therefore, a good maintenance stratergy is needed.

During the project I have created a model, using Repast Simphony,to investigate this problem. The main area I looked at was how aProactive maintenance stratergy compared to a Reactive stratergy

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Repast Simphony

The programme that I made the models on is Repast Simphony.It’s an ABMS tool-kit software, Its aim is to simplify modelcreation and use. Some of its main features are:

• Fluid model component development using any mixture ofJava, Groovy, and flowcharts in each project;

• A pure Java point-and-click model execution environmentthat includes built-in results logging and graphing tools as wellas automated connections to a variety of optional externaltools including the R statistics environment;

• An extremely flexible hierarchically nested deinition of spaceincluding the ability to do point-and-click and modeling andvisualization of 2D environments and 3D environments; and

• An automated Monte Carlo simulation framework whichsupports multiple modes of model results optimisation.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Parameters

In making the model I introduced the following parameters:

• Work

• Fail Probability

• Check Probability

• Countdown

• Breakage Days

• Total Breakage Days

I also made the following models:

• Proactive Model

• Reactive Model

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Model Score

Figure: An example Model Score.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Agent

Figure: An example Agent.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Run Environment

Figure: Screen upon running the model.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Creating the Display

Figure: Setting up the display.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Randomly switching offThe first thing I needed to do was create agents that randomlyturned off if they were on and turned on if they were off.

Figure: 100 Agents Randomly turning off.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Linking agentsThe next step was to create a network so that the agents werelinked together, such that if an agent was off then subsequentagents would also be off.

Figure: Linear network of agents.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

2D GridThe next thing I investigated was making a 2D grid of agents.

Figure: Pros and Cons of a 2D grid.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

BranchingSince there were problems with a 2D grid I had to find another wayto make a model, so I tried branching other paths off the initialpath.

Figure: Branching Paths.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Final ModelUsing the properties that I had developed in the Early Models Iwas able to create a final model (of a water pipe system).

Figure: The Water Pipe System.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Part of the Flowchart for the Proactive Model

Figure: Flowchart for an agent in the Water Pipe system.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

CounterWith the model working I needed a way to log the results, so I hadto introduce some new properties:

• Breakage Days (BD) - included in the agents; and• Total Breakage Days (TBD) - New agent.

Figure: Water pipe system with the green counting agent on the right.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Analysis and Results

• Each agent (except TBD) is modelled as a water pipe.

• There are 53 agents in the model.

• Each tick corresponds to one day.

• Each run lasts for 100 days.

• In collecting the values for the TBD 5 runs were taken undereach condition (Fail Probability = constant and CheckProbability = constant) and the mean value was used whenplotting the results.

• The TBD adds to itself at the start of the tick, this isbecause, in a real life situation, the pipes would be inspectedat the start of the day.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Varying the Fail ProbabilityInitially the Reactive model was run (without the CheckParameter). The Fail Probability was varied from 0.1 to 1.

Figure: Graph to show the TBD against Fail Probability for the Reactive

model.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Introducing the Check ProbabilityThen the Proactive model was run with the check parameter set at0.5. Again the Fail Probability was varied from 0.1 to 1.

Figure: Graph to show the TBD against Fail Probability with Check

Probability = 0.5.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Varying the Check ParameterFinally the Proactive model was run again, with Fail Probability setat 0.1, and the Check Probability varying from 0 to 0.9.

Figure: Graph to show the TBD against Check Probability for the Reactive

model with Fail Probability = 0.1.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

Conclusions

• As the Fail Probability increases, the TBD increases.

• As the Check Probability increases, the TBD decreases.

• There is a limit to the maximum TBD, this limit isapproximately 4000 days for my model.

There is still further investigation possibilities with this project, forexample:

• The type of Probability used.

• Implementing costs of maintaining and fixing the pipes, aswell as losses due to pipes being off.

• Refine the model by:• Setting the pipes nearer the centre to have a higher

maintenance priority; or• The 2D grid could be made to work.

Background Creating the Models Early Models Final Model Analysis and Results Conclusions

AcknowledgementsI would like to thank Stephan Onggo for his help and guidance

throughout the project.

Any Questions?