Many-objective portfolio optimization approach for stormwater management project selection encouraging decision maker buy-in

Matteo, Michael Di; Maier, Holger R.; Dandy, Graeme C.

doi:10.1016/j.envsoft.2018.09.008

Cited by 43 publications

(23 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that solutions are not "prescriptive", as outlined above, is also likely to increase trust in the overall optimization process. However, the degree to which the solutions developed with the aid of simulation-optimization approaches are trusted is likely to be a function of the degree to which decision-makers are familiar with and trust the simulation model, especially of the optimal solutions do not correspond with expectations (Di Matteo et al, 2018).…”

mentioning

confidence: 99%

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Maier

Razavi

Kapelan

et al. 2019

Environmental Modelling & Software

212

View full text Add to dashboard Cite

Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best tradeoffs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single-and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.

show abstract

mentioning

confidence: 99%

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Maier

Razavi

Kapelan

et al. 2019

Environmental Modelling & Software

212

View full text Add to dashboard Cite

show abstract

“…(Inselberg, 2009) to visualize Pareto-optimal solutions. Visualizing performance objectives can assist stakeholders to compare with the scheme before the optimization and select key tradeoff schemes with a clearer perspective (Matteo et al, 2019;Maier et al, 2014). Moreover, decision-makers can eliminate redundant schemes based on the preferred objectives or concerns and filter the optimal subsets those probably adopted by the experienced practitioners.…”

Section: Visual Analytics Of Pareto-frontmentioning

confidence: 99%

“…However, these studies rarely consider many-objective optimization in the basin-scale water management with conjunctive use of SW and GW. The management model including the typical single objective or bi-objective formulation probably results in the decision bias (i.e., cognitive myopia or short-sightedness) due to the sub-optimal solution only considering the fewer preference criteria (Kasprzyk et al, 2012(Kasprzyk et al, , 2015Woodruff et al, 2013;Matteo et al, 2019). Therefore, for water management with the strong interactions between SW and GW in the basin-scale water cycle, the optimal water use practice calls for decision-maker to consider multiple conflicting management objectives.…”

Section: Introductionmentioning

confidence: 99%

Basin-scale multi-objective simulation-optimization modeling for conjunctive use of surface water and groundwater in northwest China

Song¹,

Yang²,

Sun³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. In the arid inland basin of China, the long-term unregulated agriculture irrigation from surface water diversion and groundwater abstraction has caused unsustainability of water resources and degradation of ecosystems. This requires integrated management and conjunctive use of surface water (SW) and groundwater (GW) at basin scale to achieve scientific decision supports for water resources allocation in China. This study developed a novel multi-objective simulation-optimization (S-O) framework for sustainably conjunctive use of SW and GW in Yanqi Basin (YB), a typical arid region with intensive agricultural irrigation in northwest China. The S-O model integrates the new epsilon multi-objective memetic algorithm (ε-MOMA) with the MODFLOW-NWT based simulation model for examining the hydraulic interactions between SW and GW. Four conjunctive management objectives, involving maximizations of total water supply rate, groundwater storage change and surface runoff inflow to Bosten Lake, and minimization of total water delivery cost, were considered to explore the tradeoffs between socioeconomic development and environmental demands. The combined multi-objective SW and GW management model can achieve the tradeoffs in high-order objective spaces by considering groundwater abstraction in the irrigation districts and surface water diversion from the river, so as to avoid the prevalence of decision bias caused by the low-dimensional optimization formulation. Decision-makers are then able to identify their desired water use schemes with preferred objectives in the post-optimization and achieve maximal socioeconomic and ecological benefits. Furthermore, three representative runoff scenarios under changing climatic conditions were specified to quantify the influence of decreasing runoff in Kaidu River on the YB water management. Results show that runoff reduction would be of great negative impact on the total water supply, surface runoff inflow to the lake and regional groundwater storage in the aquifer. Therefore, the integrated SW and GW management is of critical importance for the protection of the fragile hydro-ecosystem under changing climatic conditions.

show abstract

“…(2) multi/many-objective optimization algorithms based on decomposition strategy and (3) indicator-based evolutionary algorithms. The application of many-objective optimization can be demonstrated in stormwater management project selection, encouraging decision-maker buy-in [96]. Additionally, the application can also be seen in mixed-model disassembly line balancing along with multi-robotic workstation [97].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of social group optimization with a few recent optimization algorithms

Naik

Satapathy

2020

Complex Intell. Syst.

View full text Add to dashboard Cite

From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

show abstract

Many-objective portfolio optimization approach for stormwater management project selection encouraging decision maker buy-in

Cited by 43 publications

References 46 publications

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Basin-scale multi-objective simulation-optimization modeling for conjunctive use of surface water and groundwater in northwest China

A comparative study of social group optimization with a few recent optimization algorithms

Contact Info

Product

Resources

About