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. (2020). "A Stochastic Multicriteria Algorithm for Generating Waste Management Facility Expansion Alternatives", Advances in Mathematics, 28, 1-27.
Abstract
While solving waste management (WM) planning problems, it may often be preferable to generate several quantifiably good options that provide multiple, contrasting perspectives. This is because WM planning generally contains complex problems that are riddled with inconsistent performance objectives and contain design requirements that are very difficult to quantify and capture when supporting decision models must be constructed. The generated alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization approaches have frequently been used to solve computationally difficult, stochastic WM problems. This paper outlines a stochastic 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 method is demonstrated on a “real world” waste management facility expansion case.Yeomans, J.S. (2019). "A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms", Applied Science and Innovation Research, 3(3), 92-105.
Abstract
While solving difficult stochastic engineering problems, it is often desirable to generate several quantifiably good options that provide contrasting perspectives. These alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process of creating maximally different solution sets has been referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization has frequently been used to solve computationally difficult, stochastic problems. This paper applies an MGA method that can create sets of maximally different alternatives for any simulation-optimization approach that employs a population-based 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 efficacy of this stochastic MGA method is demonstrated on a waste management facility expansion case.Yeomans, J.S. (2019). "A Stochastic Multicriteria Algorithm for Generating Waste Management Facility Expansion Alternatives", Journal of Civil Engineering, 9(2), 43-50.
Abstract
While solving waste management (WM) planning problems, it may often be preferable to generate several quantifiably good options that provide multiple, contrasting perspectives. This is because WM planning generally contains complex problems that are riddled with inconsistent performance objectives and contain design requirements that are very difficult to quantify and capture when supporting decision models must be constructed. The generated alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization approaches have frequently been used to solve computationally difficult, stochastic WM problems. This paper outlines a stochastic 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 method is demonstrated on a “real world” waste management facility expansion case.Yeomans, J.S. (2012). "Waste Management Facility Expansion Planning using Simulation-Optimization with Grey Programming and Penalty Functions", International Journal of Environment & Waste Management, 10(2/3), 269-283.