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, Julian Scott (2023). "A Computational Comparison of Three Nature-Inspired, Population-Based Metaheuristic Algorithms for Modelling-to-Generate-Alternatives", International Journal of Operations Research & Information Systems, 14(1),19.
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Abstract
In “real life” decision-making situations, inevitably, there are numerous unmodelled components, not incorporated into the underlying mathematical programming models, that hold substantial influence on the overall acceptability of the solutions calculated. Under such circumstances, it is frequently beneficial to produce a set of dissimilar–yet “good”–alternatives that contribute very different perspectives to the original problems. The approach for creating maximally different solutions is known as modelling-to-generate alternatives (MGA). Recently, a data structure that permits MGA using any population-based solution procedure has been formulated that can efficiently construct sets of maximally different solution alternatives. This new approach permits the production of an overall best solution together with n locally optimal, maximally different alternatives in a single computational run. The efficacy of this novel computational approach is tested on four benchmark optimization problems.Gunalay, Y. and Yeomans, J.S. (2020). "An Algorithm for Computing Solutions to the Range Limited Routing Problem Using Electrical Trucks", WSEAS Transactions on Computers, 19(7), 47-53 .
Abstract
Increasing urban intensification has caused civic planners to devote additional resources to more appropriate logistics planning. Electric vehicles have proved to be both a lower cost alternative and more environmentally friendly than the more ubiquitous internal combustion engine vehicles. However, prevailing decision-making formulations employed by municipalities and businesses are not necessarily computationally conducive for the evaluation and optimization of urban transportation systems using electric vehicles. An innovative computational approach, the range limited routing problem, is introduced that enables urban planners to more readily evaluate the contributions of electric vehicles to the city logistics decision-making process. While there is no generalized solution technique for solving this new formulation, this paper employs the Firefly Algorithm (FA) metaheuristic to solve the range limited routing problem using electric trucks.Yeomans, J.S. (2018). "Computationally Testing the Efficacy of a Modelling-to-Generate-Alternatives Procedure for Simultaneously Creating Solutions", Journal of Computer Engineering, 20(1), 38-45.
Abstract
“Real world” applications tend to contain complex performance specifications riddled with contradictory performance elements. This state arises because policymaking naturally involves multifaceted problems that are riddled with competing performance objectives and contain incompatible design requirements which are very problematic – if not impossible – to capture at the time that the requisite decision models are constructed. There are invariably unmodelled components, not readily apparent during model formulation, which could greatly impact the suitability of the model’s solutions. Consequently, it proves preferable to generate a number of dissimilar alternatives that provide multiple, distinct perspectives to the problem. These different options should all possess close-to-optimal measures with respect to the specified objective(s), but be maximally different from each other in the decision space. These maximally different solution construction approaches have been referred to as modelling-to-generate-alternatives (MGA). This study provides a procedure that simultaneously generates multiple, maximally different alternatives by employing the metaheuristic, Firefly Algorithm. The efficacy of this efficient algorithmic optimization approach is demonstrated on a commonly-tested engineering benchmark problem.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.
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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 problemCao, T. and Yeomans, J.S. (2017). "An Evolutionary Firefly Algorithm, Goal Programming Optimization Approach for Setting the Osmotic Dehydration Parameters of Papaya", Journal of Software Engineering and Applications, 10(2), 128-142.
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Abstract
An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediateto-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.Imanirad, 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 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.Cao, T. and Yeomans, J.S. (2016). "The Calculation of Optimal Osmotic Dehydration Process Parameters for Mushrooms: A Firefly Algorithm", International Journal of Environmental and Agriculture Research, 2(12), 52-58.
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