Publications Database

Welcome to the new Schulich Peer-Reviewed Publication Database!

The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:

  • Faculty Member’s Name;
  • Area of Expertise;
  • Whether the Publication is Open-Access (free for public download);
  • Journal Name; and
  • Date Range.

At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.

If you have any questions or input, please don’t hesitate to get in touch.

 

Search Results

Gunalay, Y. and Yeomans, J.S. (2018). "A Simultaneous, Simulation-Optimization Modelling-to-Generate-Alternatives Approach for Stochastic Water Resources Management Decision-Making", International Journal of Advancement in Engineering Technology, Management and Applied Science, 3(1), 57-73.

Keywords
Abstract Environmental policy formulation can prove complicated when the various system components contain considerable degrees of stochastic uncertainty. In addition, there are invariably unmodelled issues, not apparent at the time a model is constructed, that can greatly impact the acceptability of its solutions. While a mathematically optimal solution may be the best solution for the modelled problem, it is frequently not the best solution for the real problem. Consequently, it is generally preferable to create several good alternatives that provide different approaches and perspectives to the same problem. This study shows how a computationally efficient simulation-optimization (SO) approach that combines evolutionary optimization with simulation can be used to generate multiple policy alternatives that satisfy required system criteria and are maximally different in decision space. The efficacy of this stochastic modelling-to-generate-alternatives approach is demonstrated on a waste management planning case. Since SO techniques can be adapted to model a wide variety of problem types in which system components are stochastic, the practicality of this approach can be extended into many other operational and strategic planning applications containing significant sources of uncertainty.