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Enregistrement W2950401736

Coordination and Multi-Objective Optimization Framework for Managing Municipal Infrastructure Under Performance-Based Contracts

2018· dissertation· en· W2950401736 sur OpenAlexaboutno aff
Soliman Abusamra

Notice bibliographique

RevueSpectrum Research Repository (Concordia University) · 2018
Typedissertation
Langueen
DomaineEngineering
ThématiqueInfrastructure Maintenance and Monitoring
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInefficiencyBusinessService (business)Asset managementIntervention (counseling)Psychological interventionPublic infrastructureFinanceCritical infrastructureEnvironmental economicsEconomicsMarketingComputer scienceComputer securityMicroeconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

One-third of Canada’s municipal infrastructure is in fair, poor and failing condition states. Aging infrastructure systems are placing tremendous pressure on governments through steeply growing budget deficits and an urgent need for replacement. Municipalities are experiencing high inefficiency and financial burden imposed by their under-performing infrastructure, which in return increases the risk of service disruption and leaves decision-makers with no choice but undertake immediate interventions. The estimate of Canada’s infrastructure deficit is ranging between $110 billion to $270 billion. The massive number of infrastructure intervention activities occurring in cities leads to detrimental social, environmental, and economic impacts on the community. Thus, coordinating the interventions of the co-located assets (i.e. roads, water, and sewer) is progressively becoming of importance to cope with those tough challenges. It will decrease the number of service disruptions and reduce the rehabilitation costs by integrating the joint activities shared among the co-located assets. Numerous attempts have been made by previous scholars to enhance the infrastructure performance within the limited budgets. Yet, most of their efforts were geared towards short-term intervention planning for a single asset, without accounting for the potential coordination savings (i.e. cost, disruption time, consumed space, amount of service disruption, and end users’ inconvenience). 
\nIn the lights of those issues, this research proposes a coordination and multi-objective optimization framework for managing the municipal infrastructure under performance-based contracts. The framework proposes an integrated contractual and asset management solution to aid decision-makers in both the pre-contract and post-contract phases. In the pre-contract phase, the system will find a near-optimal set of key performance indicators thresholds’ as well as their associated penalties and incentives that meet the end users’ expectations without having an escalated contingency at the contractual price. In the post-contract phase, it will provide a near-optimum coordinated interventions’ schedule/plan for the municipal infrastructure. To build the framework, the research went through three main phases: (1) literature review that thoroughly studied and analyzed the municipal contractual practices, optimization, and integrated asset management; (2) contractual scheme, coordination, and multi-objective optimization asset management system where a novel contractual scheme was introduced and a coordination and optimization-based asset management system was developed; and (3) system integration and model implementation where the contractual scheme was integrated with the coordination and optimization-based asset management system to aid decision-makers in taking informed pre-contract and post-contract decisions. The coordination and optimization systems were built to quantify and evaluate the potential savings of coordinating the maintenance, whether partially or fully, as opposed to the conventional approach. It revolves through three core models: (1) central database that contains detailed asset inventory for the infrastructure systems, (2) multi-dimensional performance assessment computational models that assess the potential coordination savings for the three coordination scenarios based on eight indicators (time, space, cost, risk, resilience preparedness, condition, efficiency, and effectiveness); and (3) two multi-objective optimization models: (a) multi-objective hierarchical goal optimization that relies on a set of meta-heuristic rules and genetic algorithms optimization engine; (b) multi-objective linear programming optimization that reaches an exact solution using MOSEK software. 
\nTo demonstrate the system’s functionality, it was applied to the roads’, water and sewer networks of two case studies namely: (1) city of Montreal; and (2) town of Kindersley. Both displayed huge savings in favor of the coordinated approach as opposed to the conventional one. For the city of Montreal, the system was developed on sophisticated spreadsheets combined with a genetic algorithms’-based optimization engine (Evolver) and was applied to both pre-contract and post-contract phases. The pre-contract optimization was able to obtain a near-optimal set of key performance indicators’ thresholds and their associated penalties and incentives. The post-contract optimization displayed an overall improvement of 15% across 25 years planning horizon as a result of coordinating the interventions as opposed to the conventional scenario. The 15% improvement was broken down to 12%, 16%, 18%, 30%, 26%, 10%, 10% for the time, space, cost, efficiency, effectiveness, condition, and risk respectively. For the town of Kindersley, the system was developed on REMSOFT software integrated with MOSEK optimization engine. The results displayed an overall improvement of 29% across 25 years planning horizon because of coordinating the interventions as opposed to conventional ones. The 29% improvement was broken down to 72%, 63%, 48%, 67%, 9%, 1%, 14%, and 5% for the time, space, cost, efficiency, effectiveness, condition, resilience preparedness and risk respectively. Furthermore, the coordinated intervention program resulted in 67% fewer interventions as opposed to the conventional approach, saving an overall of 374 interventions across the 25 years, equivalent to 15 interventions annually, which drastically reduces the public disruption. In conclusion, this research proposes an integrated coordination, optimization, and contractual solution for the municipalities and maintenance contractors to enhance their expenditures’ utilization, minimize the service disruptions, and improve their assets’ performance under tough budgetary constraints.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,098
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,266
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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