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
Deviatkin, I., Kozlova, M. and Yeomans, J.S. (2021). "Simulation Decomposition for Environmental Sustainability: Enhanced Decision-Making in Carbon Footprint Analysis", Socio-Economic Planning Sciences, 75, 1, 1-10 .
Keywords
Abstract
Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.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.
Keywords
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.Imanirad, R.,Yang, X.S. and Yeomans, J.S. (2016). "Environmental Decision-Making Under Uncertainty Using a Biologically-Inspired Simulation-Optimization Algorithm for Generating Alternative Perspectives", International Journal of Business Innovation and Research, 11(1), 38-59.
Keywords
Abstract
In solving many environmental policy formulation applications, it is generally preferable to formulate several quantifiably good alternatives that provide multiple, disparate approaches to the problem. This is because environmental decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult - if not impossible - to quantify and capture at the time when supporting decision models must be constructed. By generating a set of maximally different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This maximally different solution creation approach is referred to as modelling to generate-alternatives (MGA). This paper provides a biologically-inspired metaheuristic simulation-optimisation MGA method that can efficiently create multiple solution alternatives to environmental problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy of this stochastic MGA approach for environmental policy formulation is demonstrated using a municipal solid waste case study. It is shown that this new computationally efficient algorithmic approach can simultaneously produce the desired number of maximally different solution alternatives in a single computational run of the procedure.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.
Keywords
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.Imanirad, R. and Yeomans, J.S. (2012). "Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning", Applied Mathematics, 3(10A), 1236-1244.
Keywords
Abstract
Public sector decision-making typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization 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 modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.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.
Keywords
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.
Keywords