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.
Yeomans, J.S. (2019). "Water Resource Management Using Population-Based, Dual-Criterion Simulation-Optimization Algorithms to Generate Alternatives", Journal of Environmental and Earth Sciences, 5(1), 36- 44.
AbstractWhen solving complex water resources management (WRM) problems, it is often preferable to construct a number of quantifiably good alternatives that provide multiple, different perspectives. This is because WRM normally involves multifaceted problems that are riddled with incompatible performance objectives and contain inconsistent design requirements which are very difficult to quantify and capture when supporting decisions must be constructed. These alternatives need to satisfy the required system performance criteria and yet be maximally different from each other in the decision space. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulationoptimization approaches are frequently employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper outlines an MGA approach for WRM that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based search algorithm. This algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The effectiveness of this stochastic MGA approach for creating alternatives in “real world”, water policy formulation is demonstrated using a WRM case study
Cao, T. and Yeomans, J.S. (2016). "Creating Alternatives for Stochastic Water Resources Management Decision-Making Using a Firefly Algorithm-Driven Simulation-Optimization Approach", The Global Environmental Engineers, 3(2), 49-62.