Framework for Scheduling, Controlling, and Delivery Planning for Scattered Repetitive Infrastructure Rehabilitation Projects
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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