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. (2022). "Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics", Sustainability, 14(3), 1655.
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This issue contains applied computational analytics papers that either create new methods or provide innovative applications of existing methods to assist with sustainability analysis and environmental decision-making applications. In practice, environmental analytics is an integra-tion of science, methods, and techniques that involves a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to assess re-al-world, sustainability, and environmental problems. The contributions to this issue all inves-tigate novel approaches of computational analytics – modelling, computational solution proce-dures, optimization, simulation, and technologies—as applied to sustainability analysis. The papers emphasize both the practical relevance and the methodological contributions of the work to environmental decision-making. Areas of application encompass a wide spectrum of environ-mental decision-making and sustainability, from waste, water, energy, climate change, industrial ecology, resource recovery, to recycling.Babier, A., Chan, T., Diamant, A., Mahmood, R. and McNiven, A. (2020). "The Importance of Evaluating the Complete Knowledge-Based Automated Planning Pipeline", European Journal of Medical Physics, 72, 73-79 .
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We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models. The first optimization model, inverse planning (IP), estimates weights for dose-objectives from a predicted dose distribution and generates new plans using conventional inverse planning. The second optimization model, dose mimicking (DM), minimizes the sum of one-sided quadratic penalties between the predictions and the generated plans using several dose-objectives. Altogether, four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and benchmarked against the corresponding clinical plans using clinical criteria; the error of both prediction methods was also evaluated. The best performing plans were GAN-IP plans, which satisfied the same criteria as their corresponding clinical plans (78%) more often than any other KBP pipeline. However, GAN did not necessarily provide the best prediction for the second-stage optimization models. Specifically, both the RF-IP and RF-DM plans satisfied the same criteria as the clinical plans 25% and 15% more often than GAN-DM plans (the worst performing plans), respectively. GAN predictions also had a higher mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that state-of-the-art prediction methods when paired with different optimization algorithms, produce treatment plans with considerable variation in quality.Chan, T., Diamant, A. and Mahmood, R. (2020). "Sampling from the Complement of a Polyhedron: An MCMC Algorithm for Data Augmentation", Operations Research Letters, 48(6), 744-751.
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We present an MCMC algorithm for sampling from the complement of a polyhedron. Our approach is based on the Shake-and-bake algorithm for sampling from the boundary of a set and provably covers the complement. We use this algorithm for data augmentation in a machine learning task of classifying a hidden feasible set in a data-driven optimization pipeline. Numerical results on simulated and MIPLIB instances demonstrate that our algorithm, along with a supervised learning technique, outperforms conventional unsupervised baselines.Babier, A., Chan, T., Diamant, A., Mahmood, R. and McNiven, A. (2020). "Knowledge-Based Automated Planning with 3-D Generative Adversarial Neural Networks", Medical Physics Journal , 47(2), 297-306.
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Gunalay, Y. and Yeomans, J.S. (2018). "A Simultaneous, Simulation-Optimization Modelling-to-Generate-Alternatives Approach for Stochastic Water Resources Management Decision-Making", International Journal of Advancement in Engineering Technology, Management and Applied Science, 3(1), 57-73.
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Environmental policy formulation can prove complicated when the various system components contain considerable degrees of stochastic uncertainty. In addition, there are invariably unmodelled issues, not 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 for the modelled problem, it is frequently not the best solution for the real problem. Consequently, it is generally preferable to create several good alternatives that provide different approaches and perspectives to the same problem. This study shows how a computationally efficient simulation-optimization (SO) 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 SO 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 operational and strategic planning applications containing significant sources of uncertainty.Huang, H., Milevsky, M. and Salisbury, T. (2017). "Retirement Spending and Biological Age", Journal of Economic Dynamics and Control, 84, 58-76.
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We solve a lifecycle model in which the consumer’s chronological age does not move in lockstep with calendar time. Instead, biological age increases at a stochastic non-linear rate in time like a broken clock that might occasionally move backwards. In other words, biological age could actually decline. Our paper is inspired by the growing body of medical literature that has identified biomarkers which indicate how people age at different rates. This offers better estimates of expected remaining lifetime and future mortality rates. It isn’t farfetched to argue that in the not-too-distant future personal age will be more closely associated with biological vs. calendar age. Thus, after introducing our stochastic mortality model we derive optimal consumption rates in a classic (Yaari, 1965) framework adjusted to our proper clock time. In addition to the normative implications of having access to biological age, our positive objective is to partially explain the cross-sectional heterogeneity in retirement spending rates at any given chronological age. In sum, we argue that neither biological nor chronological age alone is a sufficient statistic for making economic decisions. Rather, both ages are required to behave rationally.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.
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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.V. Dhingra and Debjit Roy (2015). "Modeling Emergency Evacuation with Time and Resource Constraints: A Case Study from Gujarat", Socio-Economic Planning Sciences, 51, 23-33.