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

Yeomans, J.S. (2019). "Waste Management Using Multicriteria Population-Based Simulation-Optimization Algorithms", Journal of Waste Management and Disposal, 2(1), 1-8.

Open Access Download

Abstract When resolving waste management (WM) planning problems, it is often preferable to construct a number of quantifiably good alternatives that provide multiple, disparate perspectives. This is because solid waste planning generally involves complicated problems that are riddled with incompatible performance objectives and contain inconsistent design requirements that are very difficult to quantify and capture when supporting decision models must be constructed. These potential 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). Simulation-optimization approaches have frequently been employed to solve computationally difficult problems containing the significant stochastic uncertainties in waste management. This paper outlines a multicriteria MGA approach for WM planning that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based solution algorithm. This algorithmic approach is computationally efficient because it simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA approach for creating alternatives is demonstrated using a “real world” waste management planning case.