Framework for Scheduling, Controlling, and Delivery Planning for Scattered Repetitive Infrastructure Rehabilitation Projects
Bibliographic record
Abstract
Public-Sector organizations such as School Boards and Universities are faced with significant challenges to keep their aging facilities operational and reducing the large backlog in infrastructure renewal needs. The cost to bring school and university facilities to acceptableand functional levels in Ontario is estimated to be as high as $15-billion and $2.5-billion, respectively. To address this challenge, Ontario government is investing hundreds of millions of dollars in infrastructure renewal every year. While significant literature efforts focused on determining efficient rehabilitation programs, little efforts addressed the delivery phase of such projects. Existing project management systems exhibit serious drawbacks when applied to infrastructure renewal projects that are mainly scattered and repetitive in nature. Planning such projects involves many challenges related to: the multi-location nature of the work (e.g., multiple schools); the need to synchronize multiple crews among multiple sites; the need to consider site productivity influences and work variations; the lack of timely progress tracking and corrective-action planning; and the ineffectiveness of current project delivery methods to handle this type of projects. Overall, existing systems lack the ability to provide near-optimum scheduling and delivery method to facilitate the execution of scattered repetitive projects. \n \nThis research introduces an efficient framework for enhancing the planning, scheduling, control, and delivery arrangement of scattered repetitive projects. The framework combines the benefits of Line Of Balance (LOB), the Critical Path Segments (CPS), and optimization techniques to develop a schedule optimization model that takes into account various practical options and constraints, including: optional construction methods, variation of work quantities among sites, possible crew assignment strategies, practical productivity factors, activity-specific site execution order, in addition to deadline, resource limits, and crew mobilization constraints. First, a field study was carried out at two large organizations in Ontario, Canada, that manage a large number of facilities, to identify the practical challenges, work constraints, and the requirements for an efficient management system. Accordingly, the proposed framework was designed to address these needs. \n \nTo support decisions during the construction phase, the proposed framework introduces an integrated CPS-LOB scheduling methodology that computes the number of crews to use, the method of construction to utilize, and the order of site execution, given any set of project constraints. For practicality, the scheduling model captures all mid-activity as-built events that enable optimum corrective action planning. This scheduling model is then applied within a Genetic Algorithms optimization procedure that tries millions of combinations of decisions until an optimum schedule is obtained, which meets all constraints at minimum cost. Moreover, the proposed scheduling and control model uses a new legible representation of scattered repetitive schedules to enable better communication of the schedule information among all project parties. To enable the use of the proposed framework in practice, this research introduced an innovative project delivery method called “Modified Construction Manager at Risk (MCMR)” to provide a suitable administrative and contractual environment that suit scattered repetitive projects. As opposed to all existing delivery practices, MCMR allows owner organizations to benefit from repetition and offer real opportunity to achieve significant cost savings. To demonstrate the capabilities and features of the proposed framework, a computer prototype system is developed, and its effectiveness validated using a real-life project. The results of the optimization experiments proved the suitability of the model to handle scattered repetitive projects. The proposed framework offers a powerful decision support features for contractors to make cost-effective decisions, while the proposed MCMR guidelines provide owners with the necessary contractual setup to make this happen. Overall, this framework has the potential to revolutionize the multi-billion-dollar business of infrastructure renewal and provide cost effective decisions that save tax payers’ money on the long run.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".