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Record W2950401736

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

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

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2018
Typedissertation
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsInefficiencyBusinessService (business)Asset managementIntervention (counseling)Psychological interventionPublic infrastructureFinanceCritical infrastructureEnvironmental economicsEconomicsMarketingComputer scienceComputer securityMicroeconomics
DOInot available

Abstract

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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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.266
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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