Optimisation and Tradeoff Analysis Tool for Asset Management Planning and Programming
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This presentation introduces key concepts around cross-asset optimization and trade-off analysis, the framework and tool developed by the author for applying cross-asset optimization and trade-off analysis in infrastructure asset management planning and programming, and discuss various ways and potential benefits of implementing optimization and trade-off analysis in asset management decision making
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Optimisation and Tradeoff Analysis Tool for Asset Management Planning and Programming
- 1. Multi Criteria Cross Asset Optimization and Trade-Off Analysis for Asset Management Planning and Programming Dr Wei Liu Principal Asset Management Engineer GHD
- 2. Global Infrastructure Stock and Gap To bridge the gap, more effective planning of capital and maintenance investment plays a key part in a holistic solution
- 3. “Come 2045, an expected 1.2 million more people will live in New Zealand, leading to an amplified demand for efficient infrastructure services. Now more than ever, the New Zealand government must work with various stakeholders to ensure sound infrastructure planning, which is vital to both the economic growth and well- being of cities. Planning occurs in silos won't deliver the right results…“ Hon Bill English
- 4. Challenges in Asset Management Capital and Maintenance Planning • Diverse projects and requirements • Highly constrained budgets • Align project portfolio with agency’ goals (e.g. KPIs) • Tools and processes to consistently and objectively prioritise and optimise project planning are not in place.
- 5. How We Address the Challenges? Project Pool Transport Water Building Other Assets … Multi- Criteria Analysis Cross- Asset Optimisation & Trade-off Cpex/Opex LTP
- 6. Analysis Inputs – Number of Years for Planning – Project pool • Business Area, Category and sub-category • Costs (can be staged in multi years) • Impacts (e.g. KPI contributions) – Multi-Criteria Analysis • Business KPIs and their relative importance • Rules for project scoring and ranking – Cross Asset Optimisation And Trade-off Analysis • Objective Function • Constraints (budgets and/or performances)
- 7. Outcomes from Analysis – Project (overall or by business unit) ranking list (most to least valuable) – Optimal project mix to deliver on agreed KPI’s – Optimised multi-year forward work programme – Optimal fund allocations among business unit and asset category – Programme cost (total, and per annum), by business unit – KPI delivery tracking/forecasting
- 8. Example: Multi-Criteria Project Assessment Evaluate and rank projects across investment areas with rationale and consistency Ranking of Project by Overall Scores
- 9. Example: LoS per $ invested analysis understand payback for each $ invested 0 20 40 60 80 100 120 0 50 100 150 NetworkPerformance (NAASRA) Cost ($M) 0 0.5 1 1.5 2 2.5 3 3.5 4 0 20 40 60 80 100 BridgeCondition Cost ($M) 0 2 4 6 8 10 12 0 10 20 30 40 50 FSCrashRate Cost ($M) 0 20 40 60 80 100 0 100 200 300 400 500 600 AverageSpeed Cost ($M)
- 10. Example: Optimised road maintenance programme (ONRC - levels of service delivery) Optimised Optimised
- 11. Example: Testing Different Optimisation Strategies Maximise BCR Budget constraint Maximise BCR Budget + performance constraints Min Difference Performance and Targets Max Budget Utilisation Minimise Cost
- 12. Example: Trade-off Analysis for two Projects/Portfolios
- 13. Summary • Multi-Criteria Cross Asset Optimisation and Trade-off Analysis Framework and Tool: – Consistent and objective project scoring, ranking and prioritisation process – Flexible optimisation configurations – Ability to set up and run different objective function – Ability to accommodate various number and type of constraints, including budget constraints and performance constraints in one year or multiple years. – Suitable for all level of analysis, from strategic budget analysis to operational project analysis – Capable to run cross-sector, cross-asset, or cross-class fund allocation analysis
- 14. Summary • Future development Plan: – Risked Based Analysis (e.g. considering budget uncertainties) – Multi Objective Optimisation – Web App Development
- 16. Optimisation Module in dTIMS
- 17. Optimisation Engine for Our Tool
Editor's Notes
- Hi everyone, hope you have enjoyed this conference so far. Now, it is my great pleasure to introduce the multi criteria cross asset optimization and trade-off analysis tool we have developed for asset management planning and programming. Hope you find some useful message from my presentation. If you have any question or comment, I will be glad to discuss with you at the end of the presentation or afterwards.
- Infrastructure is the backbone of any country. According to recently worldwide survey, there are already a super big stock of infrastructure globally. However, to meet the requirements, the gap between the demand and supply of infrastructure is also very large estimated as 1 trillion US$ per annual. To bridge the gap, more effective planning of capital and maintenance investment plays a key part in a holistic solution. In other words, we need to stretch our dollars to “do more for less”
- Similar to most of other countries, as Hon Bill English stated in the recent published 30 year Infrastructure Plan, “Come 2045, an expected 1.2 million more people will live in New Zealand, leading to an amplified demand for efficient infrastructure services. Now more than ever, the New Zealand government must work with various stakeholders to ensure sound infrastructure planning, which is vital to both the economic growth and well-being of cities. Planning occurs in silos won't deliver the right results if.“
- We are facing a number challenges in infrastructure capital and maintenance planning, including we need to deal with diverse range of projects and requirements; we often have highly constrained budgets; we need to align project portfolio with agency’s goals; and quite often tools and processes for making optimised project planning are not readily available or are not easy to use.
- So how we address these challenges? Our solution is a new framework and an analysis tool for the proposed framework. For the new framework, first we tear down the silo to build a big pool from projects in various infrastructure groups, here for example, including transport, water, building and other assets. Secondly, projects are scored and ranked through multi-criteria analysis, then optimisation and trade-off analysis are carried out to develop the optimal Cpex and Opex programme. Two key components of this proposed new analysis framework are the multi criteria analysis and advanced cross-asset optimisation and tradeoff analysis. Multi-Criteria Analysis allow us to evaluate projects consistently across all areas while cross-asset optimisation support us to make integrated and informed decision makings. To accompany with the proposed new framework, we have an analysis tool using Microsoft Excel with 6 Modules including Module 1 for Project Information Input, Module 2 for Weighting of relative importance of performance measures, Module 3 for Scaling for comparing performance measures on the same playing level; Module 4 for scoring projects from weighting and scaling of performance measures; Module 5 for tradeoff analysis to compare alternative budget and performance tradeoffs and Module 6 for selecting optimal project and fund allocaitons
- You must wonder what are inputs to run a multi-criteria cross asset optimisation analysis. While, it may include firstly number of years for planning, a project pool from business area, asset category and sub-category, project costs which can be staged in multi-years, and impacts such contributions of the project into business KPIs, Key inputs for multi-criteria analysis include business KPIs and their relative importance and rule for project scoring and ranking. Key inputs for cross asset optimisation and trade-off analysis include object function and constraints for budgets and/or performances
- Then what we can get from the analysis. First, a project ranking list from most to least valuable overall or by business unit, 2nd optimal project mix to deliver on agreed KPIs or optimised multi-year forward work programme, then fund allocations among business unit and asset category, programme cost, KPI delivery tracking/forecasting and much more
- Now, let us look at a few examples to see what the analysis tool can do for us. First of all, we can evaluate and rank projects across investment areas with rationale and consistence. We have built a tool to determine the weights of KPIs through pair-wise comparisons and to calculate the over score and ranking of each project
- You can use the tool to run high-level investment analysis to get the cost-Level-of-service charts shown in this slide, from this charts, you can determine the budget you need to meet certain level of service, or to determine the level of service target for a given budget amount. You can also get the optimal allocation of fund into each business area for any level of total budgets.
- Now let’s have a look on how the tool help us to optimise road maintenance programme for ONRC – level of service delivery. ONRC stands for the “One Road Network Classification”, which is the new road classifications system for balancing the investment of road maintenance nationally. The main objective of ONRC is to achieve similar level of services for the same class of road across the country. In the past 20 years, road controlling authorities including NZTA and some local governments have been using dTIMS software to develop long-term road maintenance programme. However, the optimisation module in dTIMS can only specify budget constraints, as a results, road maintenance programme from dTIMS can only predict the future network performance but cannot guarantee that the programme can meet the level of service requirements by ONRC. In comparison, our optimisation tool can be used to develop road maintenance programme that meet the budget and performance requirements at the same time. Additional it also gives you the optimal budget allocations among ONRC
- Next, in our tool, we can test the impacts of different optimisation strategies by specifying different type of objective functions and constraints. For example, in this slides, we have run analyses for 5 strategies, including the first one and most commonly used in other asset management system is to have the programme BCR with budget constraints only, to compare with this strategy, we can run analysis by adding performance constraints and using different objective function including minimize the differences of programme performance and level of service targets, maximize budget utilisation, or minimise programme cost. Different strategy will develop different programme and deliver different performance. However, with performance constraints specified, all the strategies except the first one will meet the level of service targets.
- Lastly, let’s quickly look at two simple trade-off analysis function in our tool, the first is for trade-off between two projects, which gives you detailed comparison of two projects and the second is the trade-off for two portolios, which can give you a comparison of two group of project portfolios.
- The proposed Multi-Criteria Cross Asset Optimisation and Trade-off Analysis Framework and Tool offers consistent and objective project scoring, ranking and prioritisation process; it has flexible optimisation configurations to suit different objectives of your asset management planning; it is suitable for all level of analysis, from strategic budget analysis to operational project analysis, and it is capable to run cross-sector, cross-asset, or cross-class fund allocation analysis.
- Look into future, we have plans to further develop our tools, including to add function for risk-based analysis such as for considering budget uncertainties, to make the tool capable to run multiple objective optimisation analysis, and to develop web application for running the tool over a cloud computing platform.
- With this, thank you for your time and attention. Wish you have a great day and enjoy the rest of the conference.