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64 C h a p t e r 7 The Resource Allocation Logic Framework is fundamentally based on needs. Optimization applies to the adjustments of needed allocations to match available funds. A needs-based determination of allocation resources means that it is necessary to connect preservation investments directly to expected performance/condition results and to enable correction of expected performance/condition outcomes commensurate with any positive or negative allocation adjustments. Connecting investments to results requires reasonably reliable data or estimates on AAG- specific inventories, average performance/condition, average deterioration, and average pres- ervation unit costs. When available funding exceeds preservation needs, optimization is used to distribute allo- cation for best results. When available funding is short of the aggregated preservation needs, the optimization is used to minimize the negative effects of the shortfall on program assets and activities. This usually results in extension of the time required to reach some or all stated performance/condition goals or targets. Some additional conclusions are highlighted as follows: 1. Based on literature review and interviews, each state DOT has unique practices, definitions, account structure, and taxonomies for the allocation of funds to preservation. There is no one-size-fits-all solution. 2. Inventory, performance/condition, deterioration, and preservation unit cost data availability for NBP assets is very scarce among DOTs, making it challenging to apply a complete analyti- cal approach for allocating resources, without significant estimating and judgment. 3. Agencies track performance metrics and asset inventory in unique ways, so the framework is flexible for a wide range of definitions of both asset-activity groupings and performance standards. 4. Good historical expenditure data is needed to estimate unit costs and deterioration rates. 5. In a severely constrained situation, optimization is still useful to minimize damage to the net asset condition. 6. Deterioration is a very strong driver of preservation need. Where deterioration-based pres- ervation need exceeds funding allocations for any particular AAG, performance improve- ment is not possible; rather performance can be expected to regress. In these cases, optimized allocation of available funds would seek to minimize this regression across AAGs. 7. The nonlinear aspects of asset deterioration can be reasonably represented as first-order (straight line) equations when aggregated as average rates across entire AAG inventories. 8. The allocation framework does not explicitly incorporate life-cycle cost analysis (LCCA). LCCA is important for determination of preservation strategies, which drives unit costs for Conclusion
Conclusion 65 preservation projects. The rate at which asset inventories need to be addressed in an established year-to-year program also drives the deteriorate rates in the logic model. The resource alloca- tion logic focuses on making the best use of available resources in a particular allocation cycle. The Excel-based demonstration model was developed to validate the logic framework using real or plausible estimates of required data based on two DOT case examples and a composite of data collected from other DOTs. It is not market software. While the model is very flexible and applies straightforward logic, adaptation to other cases requires meeting significant data16 requirements, as well as familiarity with Excel modeling, and application of linear programming solutions. Several suggestions are included for activities to support and enhance adoption of the Resource Allocation Logic Framework including the following: 1. Average Deterioration Rates. Research is suggested to support improved methods for deter- mining or estimating average deterioration rates for various AAGs. This is a very important factor in the allocation logic framework. Ideally, a high-level average rate is needed for each designated AAG and can be based on specific preservation strategies for that AAG or, as in the case for reflective signs, may be simply based on estimated reflectivity declines under various conditions. As an example, the preservation strategies for pavement may include a portion of the inventory expected to need light overlay treatment each year, and another portion that can be expected to receive full surface rehabilitation each year. This can be addressed in the framework as two separate pavement-related AAGs, each with appropriate inventory, unit cost, performance/condition information, and deterioration rates reflecting the expected time cycles for each grouping. Levels of stress (e.g., vehicle/truck traffic, climate) may also be used to calibrate deterioration estimates. 2. Asset Inventory and Condition Management. The actual availability of reasonably reliable asset inventory data (for NBP assets) appeared considerably less common via the interview process than suggested by literature. The process of collection and management of full actual inventory and condition data is an expensive proposition, in relation to the total preservation budgets for NBP assets. Compilation of practical NBP inventory and condition assessment and management approaches (in use by DOTs) would be useful and helpful to potential users of the allocation logic framework, both for enhanced resource allocation and for other asset management purposes. The inventory and condition assessment approaches could include sampling, mapping analysis, and aerial surveys. 3. Objective Functions for Optimizing Resource Allocation. Section 4.2.2 lists a number of example objective functions that may be appropriate for the linear optimization, depend- ing on the priorities and particular preservation concerns of DOTs using the logic frame- work. Using any of these objective functions requires some dexterity with the Excel model computation formulae. It makes sense that preferred optimization strategies (and therefore objective functions) would vary considerably across multiple DOTs. Prospective users of the allocation logic framework would benefit from the exercise of developing the right object functions and collecting ideas and preferences from multiple peer agencies. Collection of this intellectual capital would likely be best done via a focus-group/brainstorming approach with multiple practitioners. A simplified version of the logic model (a few AAGs and example data) will be useful in collecting and checking proposed objective functions. 4. Added Case Applications for Logic Framework. Additional case applications would sup- port, build, and communicate confidence among prospective adopters of the Resource Allo- cation Logic Framework. This will provide experience and lessons learned across a wider 16Actual or estimated.
66 resource allocation Logic Framework for highway asset preservation variety of DOTsâ allocation processes and characteristics, which range over a wide spectrum from basic âseat-of-the-pantsâ methods based on history and internal negotiations to much more sophisticated performance-based approaches supported by up-to-date management systems and analytic support. 5. Methodology to Facilitate Initial Adoption of the Resource Allocation Logic Framework. Preservation resource allocation processes are well-established in most cases, and the research team assumes that transition to a new approach will require familiarization, validation, and calibration before deployment as a core component of agenciesâ preservation resource allo- cation. The team envisions that an adopting DOT will want to populate a full version of the framework with the actual AAG taxonomy and real-time estimates or facts on inventory, unit costs, performance status, deterioration, and so forth. It will likely run and calibrate the allocation results in parallel with the normal allocation process. Through this approach over one or two allocation cycles, agencies should be able to validate the logic, and increase dependence on it. 6. Development and Sponsorship of Practitioner Training. Training support is likely to be needed at both strategic and modeler/operator levels for effective assessment, adoption, and implementation of the Resource Allocation Logic Framework. Training would be useful for (1) strategic input estimation and development of objective functions and (2) populating and manipulating the Excel model and Solver for various objective functions. The research team believes that the combination of this and the other suggestions on this list will be important to building acceptance and deployment of the allocation framework. 7. Application of the Framework to Other Resource Allocation Areas. Since the model can be adapted to virtually any taxonomy and is fundamentally based on performance-based investment needs, the model can support any application with a quantifiable inventory, per- formance ratings that can be normalized, and known average cost factors to link investment to results. It may be useful to explore application of the logic more broadly to maintenance and infrastructure improvement programs.
