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

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.

Abstract 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.

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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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.

Narayanan, M., and M. Lévesque (2014). "Venture Capital Deals: Belief and Ownership", IEEE Transactions on Engineering Management, 61(4), 570-582.

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Abstract We use a principal-agent model to examine how venture capitalists can determine the ownership division when fund-seeking entrepreneurs possess private information on their disutility of effort. This situation is especially applicable to early-stage first-time entrepreneurs seeking funding, since no history exists on their potential performance. The venture capitalist must thus consider this private information by forming a belief on the entrepreneur's effort level toward the proposed investment opportunity. Formal modeling enables us to describe how the deal process unfolds and to build a simulation. We then identify a unique investor's belief and resulting ownership sharing that maximizes the return to the entrepreneur, one that maximizes the return to the venture capitalist, as well as one that maximizes the deal welfare. We also conjecture an ordering relationship between these critical beliefs and between their resulting ownership allocations. Furthermore, we identify conditions under which the venture capitalist should choose to revise the investment offer if rejected by the entrepreneur. This paper thus moves us closer to a comprehensive theory of venture investment decisions.