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. (2017). "Simultaneous Computing of Sets of Maximally Different Alternatives to Optimal Solutions", International Journal of Engineering Research and Applications, 7(9), 21-28.
Abstract
In solving many “real world” engineering optimization applications, it is generally preferable to formulate several quantifiably good alternatives that provide distinct perspectives to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and contain competing design requirements which are very difficult – if not impossible – to capture and quantify at the time that the supporting mathematical programming models are actually constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several, distinct alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess nearoptimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of their decision variables. This solution approach is referred to as modelling to generatealternatives (MGA). This paper provides an efficient computational procedure for simultaneously generating multiple different alternatives to optimal solutions that employs the Firefly Algorithm. The efficacy of this approach will be illustrated using a well-known engineering optimization benchmark problem.Imanirad, R., Yang, X.S. and Yeomans, J.S. (2017). "Simulation-Optimization for Stochastic Modelling to Generate Alternatives", International Journal of Engineering Sciences and Management Research, 4(10), 1-8.
Keywords
Abstract
In solving many practical mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different approaches to the particular problem. This is because decisionmaking 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 that the supporting decision models are constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modelling to generate-alternatives (MGA). This paper provides a biologically-inspired simulationoptimization MGA approach that uses the Firefly Algorithm to efficiently create multiple solution alternatives to stochastic problems that satisfy required system performance criteria and yet remain maximally different in their decision spaces. The efficacy of this stochastic MGA method is demonstrated using a waste facility expansion case study.Yeomans, J.S. (2017). "An Optimization Algorithm that Simultaneously Calculates Maximally Different Alternatives", International Journal of Computational Engineering Research, 7(10), 45-50.
Abstract
When solving “real world” optimization applications, it is generally preferable to formulate several quantifiably good alternatives that provide distinct perspectives to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and contain competing design requirements which are very difficult – if not impossible – to capture and quantify at the time that the supporting decision models are actually constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several, distinct alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess nearoptimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of their decision variables. This solution approach is referred to as modelling to generate-alternatives (MGA). This paper provides an efficient optimization algorithm that simultaneously generates multiple, maximally different alternatives by employing the metaheuristic, Firefly Algorithm. The efficacy of this mathematical programming approach is demonstrated on a commonly-tested engineering optimization benchmark problemImanirad, R., Yang, X.S. and Yeomans, J.S. (2017). "A Stochastic Nature-Inspired Metaheuristic for Modelling-To-Generate-Alternatives", Journal of Scientific and Engineering Research, 4(11), 86-91.
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Abstract
In solving many “real world” decision-making applications, it is generally preferable to formulate several quantifiably good alternatives that provide numerous, distinct approaches to the problem. This is because policy formulation typically involves complex problems that are riddled with incongruent performance objectives and possess incompatible design requirements that can be very difficult – if not impossible – to incorporate at the time supporting decision models are constructed. By generating a set of maximally different solutions, it is believed that some of the dissimilar alternatives will provide unique perspectives that serve to satisfy the unmodelled characteristics. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This paper provides a stochastic biologically-inspired metaheuristic simulation-optimization MGA method that can efficiently create multiple solution alternatives to 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 is demonstrated on 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. (2017). "A Metaheuristic Procedure for Calculating Optimal Osmotic Dehydration Parameters: A Case Study of Mushrooms", Transactions on Machine Learning and Artificial Intelligence, 5(6), 1-10.
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Abstract
The Firefly Algorithm (FA) metaheuristic is employed to determine the optimal parameter settings in a case study of the osmotic dehydration of mushrooms. In the case, the functional form of the dehydration model is established through a response surface technique and the resulting mathematical programming is formulated as a non-linear goal programming model. For optimization purposes, a computationally efficient, FA-driven method is used and the resulting optimal process parameters are shown to be superior to those from previous approaches.Yeomans, J.S. (2017). "A Computational Algorithm for Creating Alternatives to Optimal Solutions", Transactions on Machine Learning and Artificial Intelligence, 5(5), 58-68.
Abstract
In solving many practical mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide distinct perspectives to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and contain competing design requirements which are very difficult – if not impossible – to capture and quantify at the time that the supporting decision models are actually constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several, distinct alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of their decision variables. This solution approach is referred to as modelling to generate-alternatives (MGA). This paper provides an efficient computational procedure for simultaneously generating multiple different alternatives to optimal solutions that employs the Firefly Algorithm. The efficacy of this approach will be illustrated using a well-known engineering optimization benchmark problem.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.Yang, X.S. and Yeomans, J.S. (2014). "Municipal Waste Management Optimization Using A Firefly Algorithm-Driven Simulation-Optimization Approach", International Journal of Process Management and Benchmarking, 4(4), 363-375.
Abstract
Many municipal solid waste management decision-making applications contain considerable elements of stochastic uncertainty. Simulation-optimisation techniques can be adapted to model a wide variety of problem types in which system components are stochastic. The family of optimisation methods referred to as simulation-optimisation incorporate stochastic uncertainties expressed as probability distributions directly into their computational procedures. In this paper, a new simulation-optimisation approach is presented that implements a modified version of the computationally efficient, nature-inspired firefly algorithm (FA). The effectiveness of this stochastic FA-driven simulation-optimisation procedure for optimisation is demonstrated using a municipal solid waste management case study.Imanirad, R., Yang, X.S and Yeomans, J.S. (2014). "A Stochastic Nature-Inspired Approach for Modelling-to-Generate-Alternatives", Journal of Applied Operational Research, 6(1), 30-38.
Keywords
Abstract
In solving many “real world” decision-making applications, it is generally preferable to formulate several quantifiably good alternatives that provide numerous, distinct approaches to the problem. This is because policy formulation typically involves complex problems that are riddled with incongruent performance objectives and possess incompatible design requirements that can be very difficult – if not impossible – to incorporate at the time supporting decision models are constructed. By generating a set of maximally different solutions, it is believed that some of the dissimilar alternatives will provide unique perspectives that serve to satisfy the unmodelled characteristics. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This paper provides a stochastic biologically-inspired metaheuristic simulation-optimization MGA method that can efficiently create multiple solution alternatives to 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 is demonstrated on 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.Imanirad, R., Yang, X.S. and Yeomans, J.S. (2013). "Stochastic Modelling to Generate Alternatives Using the Firefly Algorithm: A Simulation- Optimization Approach", Journal on Computing, 3(1), 23-28.
Keywords