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

Kozlova, M. and Yeomans, J.S. (2019). "Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage", Journal of Environmental Informatics Letters, 1(1), 20-26.

Open Access Download

Abstract Environmental decision-making commonly involves multifaceted problems that demonstrate considerable uncertainty. Monte Carlo simulation approaches have been employed in a variety of environmental planning venues to address these uncertain aspects. Simulation-based outputs are frequently presented in the form of probability distributions. Recently an approach referred to as simulation decomposition (SD) has been introduced that extends the analysis of Monte Carlo results by enhancing the explanatory power of the cause-effect relationships between the multi-variable combinations of inputs and the simulated outputs. SD constructs sub-distributions of the simulation output by pre-classifying some of the uncertain input variables into states, clustering the various combinations of these different states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since the contribution of subdivided scenarios to the overall output is easily portrayed visually, SD can highlight and disclose previously unidentified connections between the multi-variable combinations of inputs on the outputs. An SD approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used for environmental analyses that employ simulation models. This study illustrates the efficacy of SD in environmental analysis using a carbon capture and storage project from China.

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.

View Paper

Abstract Simulation-Optimisation (SO) techniques, which incorporate inherent system uncertainties using probability distributions, have been used for optimal waste management planning. While SO can be applied to numerous stochastic problems, its solution times vary considerably from one implementation to the next. In this study, SO has been concurrently combined with both penalty functions and a Grey Programming (GP) technique in order to efficiently generate sets of numerous, good policy alternatives – an approach referred to as modelling-to-generate-alternatives. The efficacy of this approach is illustrated on a planning problem for expanding a landfill and municipal waste management facilities.

Gunalay, Y., Huang, G. and Yeomans, J.S. (2012). "Modelling to Generate Alternative Policies in Highly Uncertain Environments: An Application to Municipal Solid Waste Management Planning", Journal of Environmental Informatics Letters, 19(2), 58-69.

Open Access Download

Abstract Public sector decision-making typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are difficult to quantify and capture at the time supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable degrees of stochastic uncertainty. Furthermore, there are frequently numerous stakeholders with incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the construction of a decision support model, which can greatly impact the acceptability of its solutions. While a mathematically optimal solution may be the best solution to the modelled problem, it is frequently not the best solution to the real, underlying problem. Therefore, in public environmental policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the problem. By generating a diverse set of solutions, it is hoped that some of these dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization (SO) modelling can be used to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new approach is very computationally efficient, since, in addition to finding the best solution to the problem, it permits the simultaneous generation of multiple, good solution alternatives in a single computational run of the SO algorithm rather than the multiple implementations required in other modelling-to-generate-alternatives procedures. The efficacy of this approach is specifically demonstrated using a previously studied waste management case from the Municipality of Hamilton-Wentworth, Ontario.

Yeomans, J.S. (2012). "Co-Evolutionary Simulation-Driven Optimization for Generating Alternatives in Waste Management Facility Expansion Planning", Journal of Computational Methods in Sciences and Engineering, 12(1/2), 111-127.

View Paper

Abstract Public environmental policy formulation can prove complicated when the various system components contain considerable elements of stochastic uncertainty. Invariably, there are unmodelled issues, not captured or 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 to the modelled problem, it is frequently not the best solution for the underlying real problem. Consequently, it is generally preferable to create several good alternatives that provide very different approaches and perspectives to the same problem. This study shows how a computationally efficient simulation-driven optimization (SDO) 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 SDO 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 application areas containing significant sources of uncertainty.