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.Yeomans, J.S. (2021). "A Multicriteria, Bat Algorithm Approach for Computing the Range Limited Routing Problem for Electric Trucks", WSEAS Transactions on Circuits and Systems, 20(13), 96-106.
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As a result of increasing urban intensification, civic planners have devoted additional resources to more sustainability-focused 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, the predominant decision-making approaches employed by businesses and municipalities are not necessarily computationally conducive for the optimization and evaluation of urban transportation systems involving electric vehicles. An innovative modelling and planning approach is proposed to enable urban planners to more readily evaluate the contribution of electric vehicles in city logistics and to support the decision-making process. Specifically, this paper provides a multicriteria modelling-to-generate-alternatives (MGA) decision-support procedure that employs the Bat Algorithm (BA) metaheuristic for generating sets of alternatives for electric vehicle planning in urban transshipment problems. The efficacy of this multicriteria, BA-driven MGA approach for creating planning alternatives is demonstrated on an urban transshipment problem involving electric trucks.Yeomans, J.S. (2020). "A Stochastic Multicriteria Algorithm for Generating Waste Management Facility Expansion Alternatives", Advances in Mathematics, 28, 1-27.
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Abstract
While solving waste management (WM) planning problems, it may often be preferable to generate several quantifiably good options that provide multiple, contrasting perspectives. This is because WM planning generally contains complex problems that are riddled with inconsistent performance objectives and contain design requirements that are very difficult to quantify and capture when supporting decision models must be constructed. The generated alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization approaches have frequently been used to solve computationally difficult, stochastic WM problems. This paper outlines a stochastic multicriteria MGA approach for WM planning that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based solution algorithm. This algorithmic approach is computationally efficient because it simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA method is demonstrated on a “real world” waste management facility expansion case.Yeomans, J.S. (2019). "A Stochastic, Dual-Criterion, Simulation-Optimization Algorithm for Generating Alternative", Journal of Computer Science Engineering, 5(6), 1-10.
Abstract
Complex stochastic engineering problems are frequently inundated with incompatible performance requirements and inconsistent performance specifications that can be difficult to identify when supporting decision models must be constructed. Consequently, it is often advantageous to create a set of dissimilar options that afford distinctive approaches to the problem. These alternatives should satisfy the required system performance criteria and yet be maximally different from each other in their decision spaces. The approach for creating such maximally different solution sets is referred to as modelling-to-generate-alternatives (MGA). This paper describes a dual-criterion stochastic MGA procedure that can generate sets of maximally different alternatives for any simulation-optimization approach that employs a population-based search algorithm. This stochastic algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure.Yeomans, J.S. (2019). "A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms", Applied Science and Innovation Research, 3(3), 92-105.
Abstract
While solving difficult stochastic engineering problems, it is often desirable to generate several quantifiably good options that provide contrasting perspectives. These alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process of creating maximally different solution sets has been referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization has frequently been used to solve computationally difficult, stochastic problems. This paper applies an MGA method that can create sets of maximally different alternatives for any simulation-optimization approach that employs a population-based algorithm. This algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA method is demonstrated on a waste management facility expansion case.Yeomans, J.S. (2019). "A Stochastic Multicriteria Algorithm for Generating Waste Management Facility Expansion Alternatives", Journal of Civil Engineering, 9(2), 43-50.
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Abstract
While solving waste management (WM) planning problems, it may often be preferable to generate several quantifiably good options that provide multiple, contrasting perspectives. This is because WM planning generally contains complex problems that are riddled with inconsistent performance objectives and contain design requirements that are very difficult to quantify and capture when supporting decision models must be constructed. The generated alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization approaches have frequently been used to solve computationally difficult, stochastic WM problems. This paper outlines a stochastic multicriteria MGA approach for WM planning that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based solution algorithm. This algorithmic approach is computationally efficient because it simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA method is demonstrated on a “real world” waste management facility expansion case.Yeomans, J.S. (2019). "A Simulation-Optimization Algorithm for Generating Sets of Alternatives Using Population Based Metaheuristic Procedures’, Journal of Software Engineering and Simulation", Journal of Software Engineering and Simulation, 5(2), 1-6.
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Abstract
When solving complex stochastic engineering problems, it can prove preferable to create numerous quantifiably good alternatives that provide multiple, disparate perspectives. These alternatives need to satisfy the required system performance criteria and yet be maximally different from each other in the decision space. The approach for creating maximally different sets of solutions is referred to as modelling-togenerate-alternatives (MGA). Simulation-optimization approaches are frequently employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper outlines an MGA algorithm that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based procedure. This algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedureYeomans, J.S. (2019). "A Population-Based Multi-Criteria Algorithm for Alternative Generation", Transactions on Machine Learning and Artificial Intelligence, 7(4), 1-8.
Abstract
Complex problems are frequently overwhelmed by inconsistent performance requirements and incompatible specifications that can be difficult to identify at the time of problem formulation. Consequently, it is often beneficial to construct a set of different options that provide distinct approaches to the problem. These alternatives need to be close-to-optimal with respect to the specified objective(s), but be maximally different from each other in the solution domain. The approach for creating maximally different solution sets is referred to as modelling-to-generate-alternatives (MGA). This paper introduces a computationally efficient, population-based multicriteria MGA algorithm for generating sets of maximally different alternatives.Yeomans, J.S. (2019). "A Multicriteria Simulation-Optimization Algorithm for Generating Sets of Alternatives Using Population-Based Metaheuristics", WSEAS Transactions on Computers, 18(9), 74-81.
Abstract
Stochastic optimization problems are often overwhelmed with inconsistent performance requirements and incompatible performance specifications that can be difficult to detect during problem formulation. Therefore, it can prove beneficial to create a set of dissimilar options that provide divergent perspectives to the problem. These alternatives should be near-optimal with respect to the specified objective(s), but be maximally different from each other in the decision region. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulationoptimization approaches are commonly employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper provides a new, stochastic, multicriteria MGA approach that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based algorithm.Yeomans, J.S. (2019). "A Bicriterion Approach for Generating Alternatives Using Population-Based Algorithms", WSEAS Transactions on Systems, 18(4), 29-34.