Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
25 C h a p t e r 3 The primary objective of the Resource Allocation Logic Framework developed as a part of this project is to assist state DOTs in choosing the right mix of preservation investments given policy, funding, organizational, and other legislative and programmatic constraints. It is desirable to have a Resource Allocation Logic Framework that can assist state DOTs in adapting preserva- tion investment strategies to accommodate legislative mandates and DOT goals, objectives, and priorities. Also, the Resource Allocation Logic Framework will be useful in assisting state DOTs and other key decisionmakers in evaluating and testing the impact of preservation funding allo- cation decisions on overall system performance. To satisfy these needs, funding should be linked to preservation needs and expected results and vice versa. Resource allocation decisions are typically made based on a variety of policy, organizational, programmatic, and performance-based constraints and variables that present a challenge to state DOTs in meeting both their required and desired highway preservation needs. Figure 3-1 depicts the overall decisionmaking context for highway preservation resource allocation. The linkage of investments to preservation needs and results across all assets has historically been a difficult and unmet challenge for highway agencies. This inability to link investments to needs and results has also been the bane of ROI models intended to support broader, overarching asset management decisionmaking. Partial progress has been made in addressing maintenance and preservation needs for bridge and pavement assets thanks to well-established systems that support planning for those assets. But other roadside and highway network assets do not benefit from similar planning systems and tend to be treated arbitrarily or neglected, even though they are important to the health, safety, and operational efficiency of the highway network. Based on the literature review, technical experience, and input from panel members, the research team defined the highway preservation resource allocation challenge in the following sections. 3.1 Problem Statement A crucial step in formulating an optimization model is constructing the objective function. In a constrained2 resource allocation process, decisionmakers typically have competing objec- tives to achieve, recognizing that not all objectives can be fully met in a given allocation time cycle. These objectives include reducing traffic accidents and fatalities related to an aging infra- structure, maximizing the lifespan of a particular asset type, and improving usersâ travel experi- ence and quality of life. Objectives may in some cases be asset type specific. For example, DOTs Resource Allocation Solution Context and Requirements 2 Examples of constraints include funding limits, mandated requirements, and practical limits to the extent that allocations can be adjusted in each allocation time cycle without severe operational impact.
26 resource allocation Logic Framework for highway asset preservation may be specifically mandated to maximize bridge or pavement condition ratings, which are typically the most reported and most visible indicators of system health. Legislators, agency directors, transportation commissions, and the public expect these objectives to be achieved within specified timeframes or as soon as realistically possible.3 To ensure that investments in preservation and maintenance are strategic and meet state goals and objectives, it is necessary to prioritize asset- or program-specific objectives to influence the allocation results. Given these realities, the objective of the allocation logic model is to effectively balance the pres- ervation of the entire asset network and to progress toward specified goals, objectives, and expected measures in a desired time period. The object of this research is to develop a high-level framework to optimally allocate resources over major asset and activity groupings based on preservation needs and adjust them optimally based on user-selected priorities and goals. The framework will not be granular enough to sup- port specific project planning and selection decisions, and it is not so intended. 3.2 Resource Allocation Solution Requirements On defining the problem statement, the research team investigated best approaches to devel- oping a Resource Allocation Logic Framework that most state DOTs can use to allocate resources across different asset types or preservation activities. The research team found that although there is some commonality in the terms used to describe asset types, there are many variations in the taxonomy that states employ to allocate for, account for, and execute preservation activities. The research team used the information gathered on the state of the practice in resource allo- cation (through a literature review and phone interviews, as detailed in Chapter 2) to develop a Resource Allocation Logic Framework. The Resource Allocation Logic Framework is not meant to put the resource allocation challenges in a âblack boxâ; rather, it is meant to supply an ana- lytical approach that will make resource allocation decisions faster, more logically supportable, and transparent to users. Resource Allocation Decisions for Highway Preservation Policy Organizational Programmatic Performance Allocation of Preservation Resources to Various Groups of Asset Types and/or Preservation Activities Figure 3-1. Decisionmaking context for allocating preservation funds. 3 This is an important issue for the allocation framework, because in contrast with the relatively short cycles of typical allocation processes, average highway system performance ratings improve or deteriorate slowly over time.
resource allocation Solution Context and requirements 27 The following key features are among those necessary to make the Resource Allocation Logic Framework usable: ⢠Flexibility. The logic framework should be flexible and accommodate different allocation pro- cesses and organization structures (e.g., statewide versus district-based allocation), funding con- straints, desired and current performance standards, and preservation program taxonomies. ⢠Practicality. The logic framework should be able to link performance expectations with investments for preservation and rely primarily on available, accessible, and reliable data and estimation. ⢠User-Friendliness. The logic framework should depend on optimization and analysis tools and techniques commonly available to state DOTs and allow state DOT staff to easily change and adjust mathematical formulae. It should also be easy to learn and use. In addition to these key features, the Resource Allocation Logic Framework needs to consider the following: ⢠Organizational units or members of management who will be making the resource allocation decisions for highway preservation needs ⢠Appropriate and pertinent strategic objectives or relative importance of preserving specific asset types or executing specific preservation activities to agency goals ⢠Constraints and committed projects ⢠Target performance or condition ratings for each asset type or activity ⢠Target timelines to reach the desired performance targets ⢠Total funds needed to meet the performance targets (by asset types) ⢠Legislative mandates and investment guidance that establish funding priorities or set aside specific investments ⢠Credible estimates of preservation needs to support resource allocation decisions The successful application of operation research techniques hinges on whether users can prac- tically implement the framework immediately or in the reasonable future. It is critical to clearly specify the data or to estimate support needed for the logic framework: ⢠What data is needed to execute the allocation logic? ⢠Is the data readily available in most state DOTs? ⢠What is the required data quality? ⢠What tools or software packages may be needed to support the analysis process? 3.2.1 Output Expectations for the Allocation Framework The Resource Allocation Logic Framework should enable users and decisionmakers to identify the optimal investment mix in highway preservation and maintenance to ensure the following: ⢠Statewide goals and objectives effectively influence the allocations ⢠Highway assets/activities are funded to progress toward achieving expected performance/ condition ratings within funding constraints ⢠Difference is minimized between targeted time to achieve rating targets and expected actual time to achieve rating targets ⢠End-of-allocation-cycle rating results are predicted 3.3 Key Considerations The Resource Allocation Logic Framework should be for general resource allocation solutions, that is, there should be no restricted order (top-down or bottom-up) on how it can be applied. Our literature research shows that agencies often use either the region-centric or asset-centric models
28 resource allocation Logic Framework for highway asset preservation to allocate resources, usually in a top-down or bottom-up process. This model is intended to support both approaches. Agencies would be able to use this model repetitively for resource allocation solutions at different organizational or program levels. Although the general form of the objective function should remain the same, agencies are likely to impose different constraints to best suit their needs at each level. The second key point here is that the logic model should support computational solutions in any direction. That is, public agencies should be able to solve for allocations needed based on performance objectives and constraints; conversely, agencies should be able to solve for perfor- mance objectives and timelines that are achievable given funding constraints and adjustments. There may also be the need to test sensitivities of specific input factors, assumptions, and priori- ties, or to experiment with alternative performance/condition targets. 3.3.1 Linking Allocation to Preservation Needs and Targets The allocation solution is intended to be driven fundamentally by existing preservation investment needs. For any identified grouping of assets or activities,4 preservation needs over an allocation cycle are a function of the expected average deterioration in performance or condition of an asset/activity grouping (AAG), plus the needs associated with any targeted improvement in the performance or condition of the grouping. The following are examples: ⢠If an average of number of units of an AAG inventory is expected to deteriorate below a minimum performance or condition standard during an allocation cycle, that will determine for that cycle how many units of that AAG will need restoration to the as-new (or near-new) standards set for preservation projects ⢠If the average performance/condition rating of an AAG is above or below a targeted performance/condition rating for that AAG, that will help determine how many fewer or additional units of that AAG will need restoration to the as-new (or almost-as-new) standards for completed preservation projects to meet the target ⢠Given the number of units in an AAG that require preservation projects, application of the average AAG unit cost for preservation projects will provide a reasonable indication of the preservation investment need for that AAG Linking investment to needs and results targets requires that for each AAG, actual data or cred- ible estimates are needed, including inventory, average deterioration rate, average performance/ condition rating, and average unit costs. These will all be treated more extensively in subsequent chapters, as will the need to accommodate AAGs for which such data is not presently available. 3.3.2 Optimized Resource Allocation The research and experiences of interviewees clearly indicate that optimizing preservation allocation across asset groupings is a difficult challenge, particularly when or if preservation needs cannot be determined and quantified in normalized terms (e.g., ratings and dollar invest- ments needed). In most cases, program priorities, level of urgency, and tolerable rate of change (e.g., dislocation5 issues) cannot be factored into the allocation framework in a quantifiable way. The results are familiar. Initial allocations are typically estimated based on various asset group-specific technical models, engineering judgments, field assessments, and so forth. These 4 At a known average level of performance or condition. 5Dislocation refers to the effects of rapid and significant changes in period-to-period allocations that cannot be managed effectively in a practical or cost impact sense for reasons of (as examples) contractual obligations, service commitments and work-in-process, limits of staffing flexibility, lead times for project initiation, and so forth.
resource allocation Solution Context and requirements 29 are usually combined and adjusted based on top-down programmatic decisions and funding decisions and readjusted based on overall funding limitsâoften late in the allocation process. The result tends to be driven by arbitrary decisions and tradeoffs. Investment result expectations are indeterminate or, at best, approximations that are difficult to relate later to actual outcomes. Using a linear programming approach, the solution can begin with computation of quanti- fied needs specific to AAGs that reflect and are derived from performance and condition data, adjusted to available resources in a way that is modulated by AAG ranking, performance targets, and measures of urgency. The computational solver cycles repeatedly until needs and perfor- mance goals are met to the greatest extent possible within funding limits. Developing solutions in this way permits a reasonably accurate depiction of allocation needs for AAG inventories across a state or district jurisdiction. Realistically, use of more precise second-order asset deterioration equations and cost curves would not significantly improve confidence in allocation results, because the computation results are aggregate averages, as are the less precise (somewhat subjective) performance/condition ratings that significantly drive the computations. A second major feature of the linear programming approach is the ability to compute and simply state performance expectations at the end of each allocation cycle based on the final allocations supported by available funding. This is especially important when funding shortfalls preclude the performance expectations imposed at the outset of the allocation process. 3.3.3 Scope of the Allocation Framework The research team proposed an allocation framework that can leverage straightforward linear programming techniques to link allocations to results based on estimated or actual average val- ues for inventory, deterioration rate, performance/condition rating, and unit costs. It is assumed for this framework that mandated programs and selected major one-off preservation or rehabili- tation projects would be funded as objects of specific plans and programs, with commitments that often extend well beyond typical allocation cycles. Funds for these projects will need to be treated as outside the amounts that would be termed âavailableâ for allocation of preservation resources for the broader ârank and fileâ inventory of highway system assets. Within the allocation framework, average deterioration rates and average unit costs for pres- ervation may vary considerably for particular asset types within an asset group. For example, deterioration and unit rehabilitation cost differs considerably between flexible and rigid pave- ments. The allocation framework and computational model will need to permit these cases to be treated as separate and distinct AAGs.
