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Prof. Dr. Julian Scott Yeomans
Schulich School of Business, York University, Toronto, ON M3J 1P3, Canada

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0 Machine Learning
0 Visual Analytics
0 Waste Management
0 environmental informatics
0 Simulation–optimization

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Waste Management
Simulation–optimization
Visual Analytics

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Journal article
Published: 07 April 2020 in Socio-Economic Planning Sciences
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Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.

ACS Style

Ivan Deviatkin; Mariia Kozlova; Julian Scott Yeomans. Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis. Socio-Economic Planning Sciences 2020, 75, 100837 .

AMA Style

Ivan Deviatkin, Mariia Kozlova, Julian Scott Yeomans. Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis. Socio-Economic Planning Sciences. 2020; 75 ():100837.

Chicago/Turabian Style

Ivan Deviatkin; Mariia Kozlova; Julian Scott Yeomans. 2020. "Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis." Socio-Economic Planning Sciences 75, no. : 100837.

Journal article
Published: 01 January 2020 in Journal of Environmental Informatics Letters
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ACS Style

J. S. Yeomans. Computational Analytics for Supporting Environmental Decision-Making and Analysis: An Introduction. Journal of Environmental Informatics Letters 2020, 4, 48-49 .

AMA Style

J. S. Yeomans. Computational Analytics for Supporting Environmental Decision-Making and Analysis: An Introduction. Journal of Environmental Informatics Letters. 2020; 4 (2):48-49.

Chicago/Turabian Style

J. S. Yeomans. 2020. "Computational Analytics for Supporting Environmental Decision-Making and Analysis: An Introduction." Journal of Environmental Informatics Letters 4, no. 2: 48-49.

Journal article
Published: 01 January 2020 in Journal of Environmental Informatics Letters
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Various components within environmental decision-making problems often contain considerable uncertainty. Monte Carlo simulation approaches have frequently been used to incorporate a wide array of this uncertainty into environmental planning. Simulated outputs summarizing these uncertainties are commonly portrayed in the form of probability distributions. Visualization of the disparate uncertainties within these distributions is a key aspect for effective decision support in Monte Carlo analysis. This study contrasts the performance and benefits of two visual analytics tools – overlay charts and simulation decomposition. Overlay charts enable the display of multiple sources of uncertainty overlaid on top of each other in a single graphical representation and come as a standard feature in numerous commercial Monte Carlo software packages. Conversely, simulation decomposition combines user -defined sub-distributions of the simulation uncertainties and collectively displays them in a combined graphical output figure. This paper contrasts the efficacy of overlay charts versus simulation decomposition for the visual analysis uncertainty into the environmental decision-making process.

ACS Style

M. Kozlova; J. S. Yeomans. Visual Analytics in Environmental Decision-Making: A Comparison of Overlay Charts versus Simulation Decomposition. Journal of Environmental Informatics Letters 2020, 4, 93-100 .

AMA Style

M. Kozlova, J. S. Yeomans. Visual Analytics in Environmental Decision-Making: A Comparison of Overlay Charts versus Simulation Decomposition. Journal of Environmental Informatics Letters. 2020; 4 (2):93-100.

Chicago/Turabian Style

M. Kozlova; J. S. Yeomans. 2020. "Visual Analytics in Environmental Decision-Making: A Comparison of Overlay Charts versus Simulation Decomposition." Journal of Environmental Informatics Letters 4, no. 2: 93-100.

Journal article
Published: 01 January 2019 in Journal of Environmental Informatics Letters
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Environmental decision-making commonly involves multifaceted problems that demonstrate considerable uncertainty. Monte Carlo simulation approaches have been employed in a variety of environmental planning venues to address these uncertain aspects. Simulation-based outputs are frequently presented in the form of probability distributions. Recently an approach referred to as simulation decomposition (SD) has been introduced that extends the analysis of Monte Carlo results by enhancing the explanatory power of the cause-effect relationships between the multi-variable combinations of inputs and the simulated outputs. SD constructs sub-distributions of the simulation output by pre-classifying some of the uncertain input variables into states, clustering the various combinations of these different states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since the contribution of subdivided scenarios to the overall output is easily portrayed visually, SD can highlight and disclose previously unidentified connections between the multi-variable combinations of inputs on the outputs. An SD approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used for environmental analyses that employ simulation models. This study illustrates the efficacy of SD in environmental analysis using a carbon capture and storage project from China.

ACS Style

M. Kozlova; J. S. Yeomans. Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage. Journal of Environmental Informatics Letters 2019, 1, 20-26 .

AMA Style

M. Kozlova, J. S. Yeomans. Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage. Journal of Environmental Informatics Letters. 2019; 1 (1):20-26.

Chicago/Turabian Style

M. Kozlova; J. S. Yeomans. 2019. "Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage." Journal of Environmental Informatics Letters 1, no. 1: 20-26.

Book chapter
Published: 04 September 2016 in Springer Texts in Business and Economics
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In solving complex water resources management (WRM) problems, it can prove preferable to create numerous quantifiably good alternatives that provide multiple, disparate perspectives. This is because WRM normally involves complex problems that are riddled with irreconcilable performance objectives and possess contradictory design requirements which are very difficult to quantify and capture when supporting decisions must be constructed. By producing a set of options that are maximally different from each other in terms of their decision variable structures, it is hoped that some of these dissimilar solutions may convey very different perspectives that may serve to address these unmodelled objectives. In environmental planning, this maximally different option production procedure is referred to as modelling-to-generate-alternatives (MGA). Furthermore, many WRM decision-making problems contain considerable elements of stochastic uncertainty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA that can be used to 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. This algorithmic approach is both computationally efficient and simultaneously produces a 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”, environmental policy formulation is demonstrated using a WRM case study.

ACS Style

Julian Scott Yeomans. Water Resources Management Decision-Making Under Stochastic Uncertainty Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. Springer Texts in Business and Economics 2016, 207 -229.

AMA Style

Julian Scott Yeomans. Water Resources Management Decision-Making Under Stochastic Uncertainty Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. Springer Texts in Business and Economics. 2016; ():207-229.

Chicago/Turabian Style

Julian Scott Yeomans. 2016. "Water Resources Management Decision-Making Under Stochastic Uncertainty Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives." Springer Texts in Business and Economics , no. : 207-229.

Book chapter
Published: 13 February 2016 in Springer Texts in Business and Economics
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In solving municipal solid waste (MSW) planning problems, it is generally preferable to formulate several quantifiably good alternatives that provide multiple, disparate perspectives. This is because MSW 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). Furthermore, many MSW decision-making problems contain considerable elements of stochastic uncertainty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA 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. 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. The efficacy of this stochastic MGA approach for “real world,” environmental policy formulation is demonstrated using an MSW case study.

ACS Style

Raha Imanirad; Xin-She Yang; Julian Scott Yeomans. Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. Springer Texts in Business and Economics 2016, 299 -323.

AMA Style

Raha Imanirad, Xin-She Yang, Julian Scott Yeomans. Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. Springer Texts in Business and Economics. 2016; ():299-323.

Chicago/Turabian Style

Raha Imanirad; Xin-She Yang; Julian Scott Yeomans. 2016. "Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives." Springer Texts in Business and Economics , no. : 299-323.

Journal article
Published: 01 January 2016 in International Journal of Business Innovation and Research
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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.

ACS Style

Raha Imanirad; Xin She Yang; Julian Scott Yeomans. Environmental decision-making under uncertainty using a biologically-inspired simulation-optimisation algorithm for generating alternative perspectives. International Journal of Business Innovation and Research 2016, 11, 38 .

AMA Style

Raha Imanirad, Xin She Yang, Julian Scott Yeomans. Environmental decision-making under uncertainty using a biologically-inspired simulation-optimisation algorithm for generating alternative perspectives. International Journal of Business Innovation and Research. 2016; 11 (1):38.

Chicago/Turabian Style

Raha Imanirad; Xin She Yang; Julian Scott Yeomans. 2016. "Environmental decision-making under uncertainty using a biologically-inspired simulation-optimisation algorithm for generating alternative perspectives." International Journal of Business Innovation and Research 11, no. 1: 38.

Book chapter
Published: 28 December 2014 in Artificial Intelligence: Foundations, Theory, and Algorithms
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This study employs the Firefly Algorithm (FA) to determine the optimal parameter settings needed in the osmotic dehydration process of fruits and vegetables. Two case studies are considered. For both cases, the functional form of the osmotic dehydration model is established using response surface techniques with the resulting optimization formulations being non-linear goal programming models. For optimization purposes, a computationally efficient, FA-driven method is employed and the resulting solutions are shown to be superior to those from previous approaches for 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 nature-inspired metaheuristic approach over a range of the two key parameters that most influence its running time.

ACS Style

Raha Imanirad; Julian Scott Yeomans. Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce. Artificial Intelligence: Foundations, Theory, and Algorithms 2014, 585, 49 -69.

AMA Style

Raha Imanirad, Julian Scott Yeomans. Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce. Artificial Intelligence: Foundations, Theory, and Algorithms. 2014; 585 ():49-69.

Chicago/Turabian Style

Raha Imanirad; Julian Scott Yeomans. 2014. "Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce." Artificial Intelligence: Foundations, Theory, and Algorithms 585, no. : 49-69.

Journal article
Published: 01 January 2014 in International Journal of Process Management and Benchmarking
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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.

ACS Style

Julian Scott Yeomans; Xin She Yang. Municipal waste management optimisation using a firefly algorithm-driven simulation-optimisation approach. International Journal of Process Management and Benchmarking 2014, 4, 363 .

AMA Style

Julian Scott Yeomans, Xin She Yang. Municipal waste management optimisation using a firefly algorithm-driven simulation-optimisation approach. International Journal of Process Management and Benchmarking. 2014; 4 (4):363.

Chicago/Turabian Style

Julian Scott Yeomans; Xin She Yang. 2014. "Municipal waste management optimisation using a firefly algorithm-driven simulation-optimisation approach." International Journal of Process Management and Benchmarking 4, no. 4: 363.

Journal article
Published: 01 April 2013 in International Journal of Decision Support System Technology
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Real world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate numerous alternatives that provide dissimilar approaches to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the Firefly Algorithm can concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new approach is computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures.

ACS Style

Raha Imanirad; Xin-She Yang; Julian Scott Yeomans. A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm. International Journal of Decision Support System Technology 2013, 5, 33 -45.

AMA Style

Raha Imanirad, Xin-She Yang, Julian Scott Yeomans. A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm. International Journal of Decision Support System Technology. 2013; 5 (2):33-45.

Chicago/Turabian Style

Raha Imanirad; Xin-She Yang; Julian Scott Yeomans. 2013. "A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm." International Journal of Decision Support System Technology 5, no. 2: 33-45.

Journal article
Published: 28 May 2012 in Journal of Computational Methods in Sciences and Engineering
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ACS Style

Julian Scott Yeomans. Co-evolutionary simulation-driven optimization for generating alternatives in waste management facility expansion planning. Journal of Computational Methods in Sciences and Engineering 2012, 12, 111 -127.

AMA Style

Julian Scott Yeomans. Co-evolutionary simulation-driven optimization for generating alternatives in waste management facility expansion planning. Journal of Computational Methods in Sciences and Engineering. 2012; 12 (1-2):111-127.

Chicago/Turabian Style

Julian Scott Yeomans. 2012. "Co-evolutionary simulation-driven optimization for generating alternatives in waste management facility expansion planning." Journal of Computational Methods in Sciences and Engineering 12, no. 1-2: 111-127.

Journal article
Published: 01 January 2012 in Applied Mathematics
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Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives 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 com- bines 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.

ACS Style

Julian Scott Yeomans; Raha Imanirad. Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning. Applied Mathematics 2012, 03, 1236 -1244.

AMA Style

Julian Scott Yeomans, Raha Imanirad. Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning. Applied Mathematics. 2012; 03 (10):1236-1244.

Chicago/Turabian Style

Julian Scott Yeomans; Raha Imanirad. 2012. "Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning." Applied Mathematics 03, no. 10: 1236-1244.

Journal article
Published: 01 January 2012 in Environmental Systems Research
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The Toronto District School Board (TDSB) oversees the largest school district in Canada and has been spent more than one third of its annual maintenance budget on energy and waste. This has directed attention toward system-wide reductions to both energy consumption patterns and waste generation rates. In this paper, a decision support system (DSS) that can process unit-incompatible measures is used for rating, ranking, and benchmarking the schools within the TDSB.

ACS Style

Julian S Yeomans. A decision support system for benchmarking the energy and waste performance of schools in Toronto. Environmental Systems Research 2012, 1, 5 .

AMA Style

Julian S Yeomans. A decision support system for benchmarking the energy and waste performance of schools in Toronto. Environmental Systems Research. 2012; 1 (1):5.

Chicago/Turabian Style

Julian S Yeomans. 2012. "A decision support system for benchmarking the energy and waste performance of schools in Toronto." Environmental Systems Research 1, no. 1: 5.

Journal article
Published: 21 January 2011 in Central European Journal of Operations Research
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In public policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the particular situation. This is because public sector 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 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. Furthermore, public environmental policy formulation problems often contain considerable stochastic uncertainty and there are frequently numerous stakeholders with irreconcilable perspectives involved. 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 the known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different 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 provides a co-evolutionary simulation–optimization modelling-to-generate-alternatives approach that can be used to efficiently create multiple solution alternatives that satisfy required system performance criteria in highly uncertain environments and yet are maximally different in their decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this technique is specifically demonstrated using an earlier waste management case to enable direct comparisons to previous methods. Waste management systems provide an ideal setting for illustrating the modelling techniques used for such public environmental policy formulation, since they possess all of the prevalent incongruencies and system uncertainties inherent in complex planning processes.

ACS Style

Julian Scott Yeomans. Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation–optimization to municipal solid waste management. Central European Journal of Operations Research 2011, 19, 391 -413.

AMA Style

Julian Scott Yeomans. Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation–optimization to municipal solid waste management. Central European Journal of Operations Research. 2011; 19 (4):391-413.

Chicago/Turabian Style

Julian Scott Yeomans. 2011. "Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation–optimization to municipal solid waste management." Central European Journal of Operations Research 19, no. 4: 391-413.

Journal article
Published: 08 April 2009 in Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration
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Public planning formulation can prove especially complicated when system components contain considerable degrees of uncertainty. Earlier research had demonstrated the utility of using evolutionary simulation-optimization (ESO) for solid waste planning in the Municipality of Hamilton-Wentworth, Ontario. This paper demonstrates how both penalty function minimization and grey programming (GP) can be integrated into ESO in order to efficiently generate multiple good policy alternatives that meet the Municipality's required system criteria for solid waste management. Since ESO techniques can be adapted to problems in which many 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. La formulation de la planification publique peut se révéler particulièrement compliquée quand les composantes du système sont porteuses de degrés considérables d'incertitude. La recherche antérieure a démontré l'importance de l'utilisation de la méthode dite evolutionary simulation-optimization (ESO) dans la gestion des déchets solides dans la municipality de Hamilton-Wentworth (Ontario). Le présent article met en évidence la façon dont la minimisation de la fonction de pénalité et la programmation grise (GP) peuvent être intégrés à ESO afin de produire efficacement de bonnes solutions de rechange politique qui satisfont la grille de critères en vigueur dans la municipalité. Étant donné que les techniques d'ESO peuvent être adaptées aux problèmes dans lesquels les composantes du système sont stochastiques, le caractère pratique de cette approche peut être appliquée dans beaucoup d'autres domaines de planification stratégique et opérationnelle porteurs de multiples sources d'incertitude.

ACS Style

Julian Scott Yeomans. Improved Policies for Solid Waste Management in the Municipality of Hamilton-Wentworth, Ontario. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 2009, 21, 376 -393.

AMA Style

Julian Scott Yeomans. Improved Policies for Solid Waste Management in the Municipality of Hamilton-Wentworth, Ontario. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration. 2009; 21 (4):376-393.

Chicago/Turabian Style

Julian Scott Yeomans. 2009. "Improved Policies for Solid Waste Management in the Municipality of Hamilton-Wentworth, Ontario." Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 21, no. 4: 376-393.

Journal article
Published: 01 December 2008 in Journal of Environmental Informatics
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ACS Style

J.S. Yeomans. Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty. Journal of Environmental Informatics 2008, 12, 174 -186.

AMA Style

J.S. Yeomans. Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty. Journal of Environmental Informatics. 2008; 12 (2):174-186.

Chicago/Turabian Style

J.S. Yeomans. 2008. "Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty." Journal of Environmental Informatics 12, no. 2: 174-186.

Journal article
Published: 01 January 2007 in R&D Management
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This paper describes an extension to the data envelopment analysis (DEA) support system that has been used for the assessment, rating, and ranking of diverse portfolios of research and development (R&D) projects at Lucent Technologies. The approach is illustrated through its application to a large portfolio of R&D projects considered by Lucent's Advanced Technologies Group. The method proceeds by first stratifying the portfolio into comparably efficient groups of projects through the construction of a series of efficient DEA frontiers, and then by lexicographically ranking each project within these groups relative to DEA‐based contextual attractiveness measures calculated from the different partitions. The advantages to this approach are provided not only from the perspective of the specific project rankings that are produced but also from the broader managerial insights that can be derived from any resulting differences between officially sanctioned, quantitative decision‐making procedures, and the quality of the decisions that have actually been made by managers.

ACS Style

Jonathan D. Linton; Joseph Morabito; Julian Scott Yeomans. An extension to a DEA support system used for assessing R&D projects. R&D Management 2007, 37, 29 -36.

AMA Style

Jonathan D. Linton, Joseph Morabito, Julian Scott Yeomans. An extension to a DEA support system used for assessing R&D projects. R&D Management. 2007; 37 (1):29-36.

Chicago/Turabian Style

Jonathan D. Linton; Joseph Morabito; Julian Scott Yeomans. 2007. "An extension to a DEA support system used for assessing R&D projects." R&D Management 37, no. 1: 29-36.

Journal article
Published: 31 October 2005 in Journal of Environmental Management
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Evolutionary simulation-optimization (ESO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. Grey programming (GP) methods have been previously applied to numerous environmental planning problems containing uncertain information. In this paper, ESO is combined with GP for policy planning to create a hybrid solution approach named GESO. It can be shown that multiple policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created by applying GESO to this case data. The efficacy of GESO is illustrated using a municipal solid waste management case taken from the regional municipality of Hamilton-Wentworth in the Province of Ontario, Canada. The MGA capability of GESO is especially meaningful for large-scale real-world planning problems and the practicality of this procedure can easily be extended from MSW systems to many other planning applications containing significant sources of uncertainty.

ACS Style

Gordon H. Huang; Jonathan D. Linton; Julian Scott Yeomans; Reena Yoogalingam. Policy planning under uncertainty: efficient starting populations for simulation-optimization methods applied to municipal solid waste management. Journal of Environmental Management 2005, 77, 22 -34.

AMA Style

Gordon H. Huang, Jonathan D. Linton, Julian Scott Yeomans, Reena Yoogalingam. Policy planning under uncertainty: efficient starting populations for simulation-optimization methods applied to municipal solid waste management. Journal of Environmental Management. 2005; 77 (1):22-34.

Chicago/Turabian Style

Gordon H. Huang; Jonathan D. Linton; Julian Scott Yeomans; Reena Yoogalingam. 2005. "Policy planning under uncertainty: efficient starting populations for simulation-optimization methods applied to municipal solid waste management." Journal of Environmental Management 77, no. 1: 22-34.

Journal article
Published: 01 June 2004 in Journal of Environmental Informatics
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The basic intent in financial performance evaluation is to appraise current business operations internally and to benchmark them against similar business operations externally in order to identify best-in-class practices. In this paper, a new positive environmental/financial screening approach that can simultaneously include a wide combination of regulatory, technological, operational and event dimensions is created for the analyzing, rating, ranking, benchmarking, and selecting of companies from an industry sector. A method is provided that advances the use of Data Envelopment Analysis (DEA) for rating and ranking diverse groups of companies using a combination of both financial and environmental performance measures. This novel approach proceeds by first stratifying the sector into comparably efficient groups of companies through the construction of a series of efficient DEA frontiers, and then by ranking each company within these groups relative to DEA-based contextual attractiveness measures calculated from the different partitions. The method is illustrated through an application to a group of companies from the Metals and Mining Industry sector.

ACS Style

J. S. Yeomans. Rating and Evaluating the Combined Financial and Environmental Performance of Companies in the Metals and Mining Sector. Journal of Environmental Informatics 2004, 3, 95 -105.

AMA Style

J. S. Yeomans. Rating and Evaluating the Combined Financial and Environmental Performance of Companies in the Metals and Mining Sector. Journal of Environmental Informatics. 2004; 3 (2):95-105.

Chicago/Turabian Style

J. S. Yeomans. 2004. "Rating and Evaluating the Combined Financial and Environmental Performance of Companies in the Metals and Mining Sector." Journal of Environmental Informatics 3, no. 2: 95-105.

Journal article
Published: 01 September 2003 in Journal of Environmental Informatics
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Many uncertain factors exist in the planning for Municipal Solid Waste (MSW) management. In this paper, for the first time an evolutionary algorithm is combined with simulation to determine solutions for the MSW management problem. This new procedure is applied to real case data taken from the Regional Municipality of Hamilton-Wentworth in the province of Ontario and the solutions are compared to the outputs from an earlier study. It can be shown that improved solutions to this problem can be obtained and that this approach provides many practical planning and implementation benefits for problems operating under uncertain conditions.

ACS Style

J. S. Yeomans; G. H. Huang; R. Yoogalingam. Combining Simulation with Evolutionary Algorithms for Optimal Planning Under Uncertainty: An Application to Municipal Solid Waste Management Planning in the Reginonal Municipality of Hamilton-Wentworth. Journal of Environmental Informatics 2003, 2, 11 -30.

AMA Style

J. S. Yeomans, G. H. Huang, R. Yoogalingam. Combining Simulation with Evolutionary Algorithms for Optimal Planning Under Uncertainty: An Application to Municipal Solid Waste Management Planning in the Reginonal Municipality of Hamilton-Wentworth. Journal of Environmental Informatics. 2003; 2 (1):11-30.

Chicago/Turabian Style

J. S. Yeomans; G. H. Huang; R. Yoogalingam. 2003. "Combining Simulation with Evolutionary Algorithms for Optimal Planning Under Uncertainty: An Application to Municipal Solid Waste Management Planning in the Reginonal Municipality of Hamilton-Wentworth." Journal of Environmental Informatics 2, no. 1: 11-30.