Robust solutions of the Mild Slope Equations using a discontinuous DRBEM

2022 ◽  
Vol 136 ◽  
pp. 158-171
Author(s):  
Roohollah Naserzadeh ◽  
Harry.B. Bingham
Keyword(s):  
Author(s):  
Barbara Gray ◽  
Jill Purdy

Multistakeholder partnerships (MSPs) are formed to tackle knotty societal problems, promote innovation, provide public services, expand governance capabilities, set standards for a field, or resolve conflicts that impede progress on critical issues. Partnerships are viewed as collaboration among four types of stakeholders: businesses, governments, nongovernmental organizations (NGOs), and civic society. The objective of collaboration is to create a richer, more comprehensive appreciation of the iss/problem than any of the partners could construct alone by viewing it from the perspectives of all the stakeholders and designing robust solutions. Such partnerships are necessary because few organizations contain sufficient knowledge and resources to fully analyze issues and take action on them unilaterally. Five essential components of a rigorous definition of collaboration are presented: interdependence among partners, emergence of shared norms, wrestling with differences, respect for different competencies, and assuming joint responsibility for outcomes. Several examples of MSPs are provided.


2013 ◽  
Vol 44 (2) ◽  
pp. 131-156 ◽  
Author(s):  
Laura Climent ◽  
Richard J. Wallace ◽  
Miguel A. Salido ◽  
Federico Barber

2017 ◽  
Vol 60 (3) ◽  
pp. 382-400 ◽  
Author(s):  
Rémi Boutteau ◽  
Peter Sturm ◽  
Pascal Vasseur ◽  
Cédric Demonceaux

2007 ◽  
Vol 17 (03) ◽  
pp. 299-309
Author(s):  
GRZEGORZ DRZADZEWSKI ◽  
MARK WINEBERG

The common definition for robust solutions considers a solution robust if it remains optimal when the parameters defining the fitness function are perturbed. A second definition that can be found in the literature: robustness occurs when a solution can be varied spatially without a significant drop in fitness. We propose an alternative operational definition for spatial robustness: both the solution and the neighbourhood around the solution has fitness above a given threshold. With this new definition, we created a set of functions with useful properties to allow for the testing of solution robustness. The performance of a Genetic Algorithm (GA) is then evaluated based on its ability to identify multiple robust solutions based on the above robustness definition. Different neighbourhood evaluation schemes are identified from the literature and compared, with the minimum neighbour technique proving to be the most effective.


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