Multi-objective optimization for pavement maintenance and rehabilitation decision-making: A critical review and future directions
TL;DR: The strategic decision-making goals, decision processes, sources and characterization methods of uncertainty, intelligent algorithm followed across the globe were reviewed, and the latest development of multi-objective decision-aid tools (MODAT) for PMMS was discussed while emphasizing the importance of establishing joint MOO optimization model of multiple facilities and multiple M&R techniques forPMMS to achieve intelligent decision- making.
About: This article is published in Automation in Construction.The article was published on 2021-10-01. It has received 43 citations till now. The article focuses on the topics: Multi-objective optimization.
Citations
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TL;DR: In this article , a systematic review under both scientometric and qualitative analysis is presented to understand the current state and discuss the future research trends of MOO for project improvement. But, the authors do not provide a detailed analysis of these trends.
37Â citations
01 Jan 2003
TL;DR: In this article, a study has been conducted to investigate current practices on decision-making under risk and uncertainty for infrastructure project investments, and it was found that the most influential factors affecting risk and uncertainties resulted from forecasting errors.
Abstract: A study has been conducted to investigate current practices on decision-making under risk
and uncertainty for infrastructure project investments. It was found that many European
countries such as the UK, France, Germany including Australia use scenarios for the
investigation of the effects of risk and uncertainty of project investments. Different alternative
scenarios are mostly considered during the engineering economic cost-benefit analysis stage.
For instance, the World Bank requires an analysis of risks in all project appraisals. Risk in
economic evaluation needs to be addressed by calculating sensitivity of the rate of return for a
number of events.
Risks and uncertainties of project developments arise from various sources of errors including
data, model and forecasting errors. It was found that the most influential factors affecting risk
and uncertainty resulted from forecasting errors. Data errors and model errors have trivial
effects. It was argued by many analysts that scenarios do not forecast what will happen but
scenarios indicate only what can happen from given alternatives. It was suggested that the
probability distributions of end-products of the project appraisal, such as cost-benefit ratios
that take forecasting errors into account, are feasible decision tools for economic evaluation.
Political, social, environmental as well as economic and other related risk issues have been
addressed and included in decision-making frameworks, such as in a multi-criteria decisionmaking
framework. But no suggestion has been made on how to incorporate risk into the
investment decision-making process.
36Â citations
TL;DR: In this article , the authors present and discuss their diverse perceptions and methodologies for these management system elements and further investigate the further investigations required to develop more efficient systems that optimally achieve the environmental, economic, and social objectives.
13Â citations
TL;DR: In this paper, the authors present and discuss their diverse perceptions and methodologies for these management system elements and further investigate the further investigations required to develop more efficient systems that optimally achieve the environmental, economic, and social objectives.
13Â citations
TL;DR: In this paper , the authors summarized the main findings of the past studies and provided some perspective reviews for compromising the economic and environmental appurtenances provoked during the road pavement life cycle.
9Â citations
References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
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TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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01 Feb 2001
TL;DR: A new heuristic algorithm, mimicking the improvisation of music players, has been developed and named Harmony Search (HS), which is illustrated with a traveling salesman problem (TSP), a specific academic optimization problem, and a least-cost pipe network design problem.
Abstract: Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NL...
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18 Sep 2000
TL;DR: Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.
Abstract: Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN3) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.
4,878Â citations