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. (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). "Water Resource Management Using Population-Based, Dual-Criterion Simulation-Optimization Algorithms to Generate Alternatives", Journal of Environmental and Earth Sciences, 5(1), 36- 44.
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When solving complex water resources management (WRM) problems, it is often preferable to construct a number of quantifiably good alternatives that provide multiple, different perspectives. This is because WRM normally involves multifaceted problems that are riddled with incompatible performance objectives and contain inconsistent design requirements which are very difficult to quantify and capture when supporting decisions must be constructed. 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-to-generate-alternatives (MGA). Simulationoptimization approaches are frequently employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper outlines an MGA approach for WRM that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based search 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 effectiveness of this stochastic MGA approach for creating alternatives in “real world”, water policy formulation is demonstrated using a WRM case studyYeomans, 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 procedureYang, 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. and Yeomans, J.S. (2012). "Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning", Applied Mathematics, 3(10A), 1236-1244.
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Public sector decision-making typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization 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 modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.Gunalay, Y., Huang, G. and Yeomans, J.S. (2012). "Modelling to Generate Alternative Policies in Highly Uncertain Environments: An Application to Municipal Solid Waste Management Planning", Journal of Environmental Informatics Letters, 19(2), 58-69.
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Abstract
Public sector decision-making typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are difficult to quantify and capture at the time supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable degrees of stochastic uncertainty. Furthermore, there are frequently numerous stakeholders with incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the construction of a decision support model, which can greatly impact the acceptability of its solutions. While a mathematically optimal solution may be the best solution to the modelled problem, it is frequently not the best solution to the real, underlying problem. Therefore, in public environmental policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the problem. By generating a diverse set of solutions, it is hoped that some of these dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization (SO) modelling can be used to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new approach is very computationally efficient, since, in addition to finding the best solution to the problem, it permits the simultaneous generation of multiple, good solution alternatives in a single computational run of the SO algorithm rather than the multiple implementations required in other modelling-to-generate-alternatives procedures. The efficacy of this approach is specifically demonstrated using a previously studied waste management case from the Municipality of Hamilton-Wentworth, Ontario.Yeomans, J.S. (2012). "Co-Evolutionary Simulation-Driven Optimization for Generating Alternatives in Waste Management Facility Expansion Planning", Journal of Computational Methods in Sciences and Engineering, 12(1/2), 111-127.
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