Microeconomic optimization and what-if analysis for facilities renewal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to propose a microeconomic-based approach to support fund-allocation decisions for a large number of assets. Under the prevailing financial constraints and rapid deterioration of facilities, arriving at optimum fund allocation for capital renewal projects has become very challenging. Due to the complexity of modeling multi-year life cycle cost analysis, existing fund-allocation methods have serious drawbacks when handling a large portfolio of assets, and their results are difficult to justify. Design/methodology/approach – This paper adopts well-established theories from microeconomics and proposes a new microeconomic-based decision support framework that has two novel components: a heuristic procedure to optimize and justify fund-allocation decisions by balancing the funding among the different asset categories; and a visual what-if analysis approach inspired by the economic indifference maps. Findings – Applying the proposed framework on a real case study of 800 building components proved that optimum decisions can be achieved through an equilibrium state at which fair and equitable allocations are made such that the utility per dollar is balanced for all asset categories. The visual what-if analysis approach presented a powerful graphical tool to visualize decisions, along with their costs and benefits, and facilitate sensitivity analysis under changes in budget levels. Originality/value – This paper, using the proposed microeconomic framework, sheds a new light on how fund-allocation optimization problems can be simplified, from an economic perspective, to arrive at accurate and justifiable decisions for a large portfolio of facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it