The Experts below are selected from a list of 3909 Experts worldwide ranked by ideXlab platform
Michiel C J Bliemer - One of the best experts on this subject based on the ideXlab platform.
-
Optimization of externalities using DTM measures: a Pareto Optimal multi objective optimization using the evolutionary algorithm SPEA2+
2020Co-Authors: Luc Johannes Josephus Wismans, Eric C Van Berkum, Michiel C J BliemerAbstract:Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto Optimal Set is determined by employing the SPEA2+ evolutionary algorithm.
-
pruning and ranking the Pareto Optimal Set application for the dynamic multi objective network design problem
Journal of Advanced Transportation, 2014Co-Authors: Luc Johannes Josephus Wismans, Ties Brands, Eric C Van Berkum, Michiel C J BliemerAbstract:Solving the multi-objective network design problem (MONDP) resorts to a Pareto Optimal Set. This Set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto Optimal Set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto Optimal Set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto Optimal Set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto Optimal Set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non-dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade-offs has been identified as the more suitable method to assist the decision making process.
-
Handling multiple objectives in optimization of externalities as objectives for dynamic traffic management.
European Journal of Transport and Infrastructure Research, 2014Co-Authors: Luc Johannes Josephus Wismans, Eric C Van Berkum, Michiel C J BliemerAbstract:Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem (NDP) and incorporation of externality objectives results in a multi objective network design problem (MO NDP)). Solving this problem resorts in a Pareto Optimal Set of solutions. A framework is presented with the non-dominated sorting algorithm (NSGAII), the Streamline dynamic traffic assignment model and several externality models, that is used to solve this MO NDP. With a numerical experiment it is shown that the Pareto Optimal Set provides important information for the decision making process, which would not have been available if the optimization problem was simplified by incorporation of a compensation principle in advance. However, in the end a solution has to be chosen as the best compromise. Since the Pareto Optimal Set can be difficult to comprehend, ranking it may be necessary to assist the decision makers. Cost benefit analysis which uses the economic compensation principle is a method that is often used for ranking the alternatives. This research shows, that travel time costs are by far the most dominant objective. Therefore other ranking methods should be considered. Differences between these methods are explained and it is illustrated that the outcomes and therefore the eventual decisions taken can be different.
-
Pruning and ranking the Pareto Optimal Set, application for the dynamic multi‐objective network design problem
Journal of Advanced Transportation, 2012Co-Authors: Luc Johannes Josephus Wismans, Ties Brands, Eric C Van Berkum, Michiel C J BliemerAbstract:Solving the multi-objective network design problem (MONDP) resorts to a Pareto Optimal Set. This Set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto Optimal Set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto Optimal Set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto Optimal Set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto Optimal Set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non-dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade-offs has been identified as the more suitable method to assist the decision making process.
Luc Johannes Josephus Wismans - One of the best experts on this subject based on the ideXlab platform.
-
Optimization of externalities using DTM measures: a Pareto Optimal multi objective optimization using the evolutionary algorithm SPEA2+
2020Co-Authors: Luc Johannes Josephus Wismans, Eric C Van Berkum, Michiel C J BliemerAbstract:Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto Optimal Set is determined by employing the SPEA2+ evolutionary algorithm.
-
An Approach for Determining Rules used to Select Viable Junction Design Alternatives Based on Multiple Objectives
Advances in Science Technology and Engineering Systems Journal, 2020Co-Authors: Erwin Marco Bezembinder, Luc Johannes Josephus Wismans, Eric C Van BerkumAbstract:Transport planners and engineers frequently face the challenge to determine the best design for a specific junction. Many road design manuals provide guidelines for the design and evaluation of different junction alternatives, however these mostly refer to specialized software in which the performances of design alternatives can be modelled. In the first stage of the design process, such assessments of many alternatives are undesirable due to time and budget constraints. There is a need for quick design rules which need limited input data. Although some of these rules exist, their usability is limited due to inconsistencies in rules and non-transparency in combination with objectives. In this paper, we present an approach by which consistent and transparent junction design rules can be determined. The resulting rules can be used to predict a Set of viable junction design alternatives for the first stage of the junction design assessment process. The predicted Set is in fact the Pareto Optimal Set of solutions for multiple objectives, e.g. regarding operational, safety and/or environmental impact. The Pareto Optimal Set of solutions always contains the best solution, whatever Set of weights is used for different objectives in a later stage of the assessment process, thus handling multiple objectives in a straightforward manner. The rules are derived from a dataSet by using decision tree data mining techniques. For this, a large dataSet is first generated, using performance models, with Pareto Optimal Sets of junction design alternatives for a large amount of, randomly generated, traffic volumes. The approach is applied and evaluated on cases for two different countries. Results show that for over 90% of the situations the Pareto Optimal Set can be predicted by the new rules, whereas existing rules hardly reach 33%. The new rules provide junction design alternatives with a better performance.
-
pruning and ranking the Pareto Optimal Set application for the dynamic multi objective network design problem
Journal of Advanced Transportation, 2014Co-Authors: Luc Johannes Josephus Wismans, Ties Brands, Eric C Van Berkum, Michiel C J BliemerAbstract:Solving the multi-objective network design problem (MONDP) resorts to a Pareto Optimal Set. This Set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto Optimal Set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto Optimal Set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto Optimal Set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto Optimal Set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non-dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade-offs has been identified as the more suitable method to assist the decision making process.
-
Handling multiple objectives in optimization of externalities as objectives for dynamic traffic management.
European Journal of Transport and Infrastructure Research, 2014Co-Authors: Luc Johannes Josephus Wismans, Eric C Van Berkum, Michiel C J BliemerAbstract:Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem (NDP) and incorporation of externality objectives results in a multi objective network design problem (MO NDP)). Solving this problem resorts in a Pareto Optimal Set of solutions. A framework is presented with the non-dominated sorting algorithm (NSGAII), the Streamline dynamic traffic assignment model and several externality models, that is used to solve this MO NDP. With a numerical experiment it is shown that the Pareto Optimal Set provides important information for the decision making process, which would not have been available if the optimization problem was simplified by incorporation of a compensation principle in advance. However, in the end a solution has to be chosen as the best compromise. Since the Pareto Optimal Set can be difficult to comprehend, ranking it may be necessary to assist the decision makers. Cost benefit analysis which uses the economic compensation principle is a method that is often used for ranking the alternatives. This research shows, that travel time costs are by far the most dominant objective. Therefore other ranking methods should be considered. Differences between these methods are explained and it is illustrated that the outcomes and therefore the eventual decisions taken can be different.
-
Pruning and ranking the Pareto Optimal Set, application for the dynamic multi‐objective network design problem
Journal of Advanced Transportation, 2012Co-Authors: Luc Johannes Josephus Wismans, Ties Brands, Eric C Van Berkum, Michiel C J BliemerAbstract:Solving the multi-objective network design problem (MONDP) resorts to a Pareto Optimal Set. This Set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto Optimal Set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto Optimal Set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto Optimal Set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto Optimal Set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non-dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade-offs has been identified as the more suitable method to assist the decision making process.
Eric C Van Berkum - One of the best experts on this subject based on the ideXlab platform.
-
Optimization of externalities using DTM measures: a Pareto Optimal multi objective optimization using the evolutionary algorithm SPEA2+
2020Co-Authors: Luc Johannes Josephus Wismans, Eric C Van Berkum, Michiel C J BliemerAbstract:Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto Optimal Set is determined by employing the SPEA2+ evolutionary algorithm.
-
An Approach for Determining Rules used to Select Viable Junction Design Alternatives Based on Multiple Objectives
Advances in Science Technology and Engineering Systems Journal, 2020Co-Authors: Erwin Marco Bezembinder, Luc Johannes Josephus Wismans, Eric C Van BerkumAbstract:Transport planners and engineers frequently face the challenge to determine the best design for a specific junction. Many road design manuals provide guidelines for the design and evaluation of different junction alternatives, however these mostly refer to specialized software in which the performances of design alternatives can be modelled. In the first stage of the design process, such assessments of many alternatives are undesirable due to time and budget constraints. There is a need for quick design rules which need limited input data. Although some of these rules exist, their usability is limited due to inconsistencies in rules and non-transparency in combination with objectives. In this paper, we present an approach by which consistent and transparent junction design rules can be determined. The resulting rules can be used to predict a Set of viable junction design alternatives for the first stage of the junction design assessment process. The predicted Set is in fact the Pareto Optimal Set of solutions for multiple objectives, e.g. regarding operational, safety and/or environmental impact. The Pareto Optimal Set of solutions always contains the best solution, whatever Set of weights is used for different objectives in a later stage of the assessment process, thus handling multiple objectives in a straightforward manner. The rules are derived from a dataSet by using decision tree data mining techniques. For this, a large dataSet is first generated, using performance models, with Pareto Optimal Sets of junction design alternatives for a large amount of, randomly generated, traffic volumes. The approach is applied and evaluated on cases for two different countries. Results show that for over 90% of the situations the Pareto Optimal Set can be predicted by the new rules, whereas existing rules hardly reach 33%. The new rules provide junction design alternatives with a better performance.
-
pruning and ranking the Pareto Optimal Set application for the dynamic multi objective network design problem
Journal of Advanced Transportation, 2014Co-Authors: Luc Johannes Josephus Wismans, Ties Brands, Eric C Van Berkum, Michiel C J BliemerAbstract:Solving the multi-objective network design problem (MONDP) resorts to a Pareto Optimal Set. This Set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto Optimal Set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto Optimal Set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto Optimal Set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto Optimal Set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non-dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade-offs has been identified as the more suitable method to assist the decision making process.
-
Handling multiple objectives in optimization of externalities as objectives for dynamic traffic management.
European Journal of Transport and Infrastructure Research, 2014Co-Authors: Luc Johannes Josephus Wismans, Eric C Van Berkum, Michiel C J BliemerAbstract:Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem (NDP) and incorporation of externality objectives results in a multi objective network design problem (MO NDP)). Solving this problem resorts in a Pareto Optimal Set of solutions. A framework is presented with the non-dominated sorting algorithm (NSGAII), the Streamline dynamic traffic assignment model and several externality models, that is used to solve this MO NDP. With a numerical experiment it is shown that the Pareto Optimal Set provides important information for the decision making process, which would not have been available if the optimization problem was simplified by incorporation of a compensation principle in advance. However, in the end a solution has to be chosen as the best compromise. Since the Pareto Optimal Set can be difficult to comprehend, ranking it may be necessary to assist the decision makers. Cost benefit analysis which uses the economic compensation principle is a method that is often used for ranking the alternatives. This research shows, that travel time costs are by far the most dominant objective. Therefore other ranking methods should be considered. Differences between these methods are explained and it is illustrated that the outcomes and therefore the eventual decisions taken can be different.
-
Pruning and ranking the Pareto Optimal Set, application for the dynamic multi‐objective network design problem
Journal of Advanced Transportation, 2012Co-Authors: Luc Johannes Josephus Wismans, Ties Brands, Eric C Van Berkum, Michiel C J BliemerAbstract:Solving the multi-objective network design problem (MONDP) resorts to a Pareto Optimal Set. This Set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto Optimal Set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto Optimal Set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto Optimal Set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto Optimal Set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non-dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade-offs has been identified as the more suitable method to assist the decision making process.
Alexis Guigue - One of the best experts on this subject based on the ideXlab platform.
-
approximation of the Pareto Optimal Set for multiobjective Optimal control problems using viability kernels
ESAIM: Control Optimisation and Calculus of Variations, 2014Co-Authors: Alexis GuigueAbstract:This paper provides a convergent numerical approximation of the Pareto Optimal Set for finite-horizon multiobjective Optimal control problems in which the objective space is not necessarily convex. Our approach is based on Viability Theory. We first introduce a Set-valued return function V and show that the epigraph of V equals the viability kernel of a certain related augmented dynamical system. We then introduce an approximate Set-valued return function with finite Set-values as the solu- tion of a multiobjective dynamic programming equation. The epigraph of this approximate Set-valued return function equals to the finite discrete viability kernel resulting from the convergent numerical ap- proximation of the viability kernel proposed in (P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre. Birkhauser, Boston (1999) 177-247. P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Set-Valued Analysis 8 (2000) 111-126). As a result, the epigraph of the approximate Set-valued return function con- verges to the epigraph of V. The approximate Set-valued return function finally provides the proposed numerical approximation of the Pareto Optimal Set for every initial time and state. Several numerical examples illustrate our approach. Mathematics Subject Classification. 49M2, 49L20, 54C60, 90C29.
-
a convergent approximation of the Pareto Optimal Set for finite horizon multiobjective Optimal control problems moc using viability theory
arXiv: Optimization and Control, 2012Co-Authors: Alexis GuigueAbstract:The objective of this paper is to provide a convergent numerical approximation of the Pareto Optimal Set for nite-horizon multiobjective Optimal control problems for which the objective space is not necessarily convex. Our approach is based on Viability Theory. We rst introduce the Set-valued return function V and show that the epigraph of V is equal to the viability kernel of a properly chosen closed Set for a properly chosen dynamics. We then introduce an approximate Set- valued return function with nite Set-values as the solution of a multiobjective dynamic programming equation. The epigraph of this approximate Set-valued return function is shown to be equal to the nite discrete viability kernel resulting from the convergent numerical approximation of the viability kernel proposed in (4, 5). As a result, the epigraph of the approximate Set-valued return function converges towards the epigraph of V. The approximate Set-valued return function nally provides the proposed numerical approximation of the Pareto Optimal Set for every initial time and state. Several numerical examples are provided.
-
Pareto Optimality and Multiobjective Trajectory Planning for a 7-DOF Redundant Manipulator
IEEE Transactions on Robotics, 2010Co-Authors: Alexis Guigue, Mojtaba Ahmadi, Rob Langlois, John M. HayesAbstract:This paper presents a novel approach to solve multiobjective robotic trajectory planning problems. It proposes to find the Pareto Optimal Set, rather than a single solution usually obtained through scalarization, e.g., weighting the objective functions. Using the trajectory planning problem for a redundant manipulator as part of a captive trajectory simulation system, the general discrete dynamic programming (DDP) approximation method presented in our previous work is shown to be a promising approach to obtain a close representation of the Pareto Optimal Set. When compared with the Set obtained by varying the weights, the results confirm that the DDP approximation method can find approximate Pareto objective vectors, where the weighting method fails, and can generally provide a closer representation of the actual Pareto Optimal Set.
Defeng Chang - One of the best experts on this subject based on the ideXlab platform.
-
Optimal reactive power dispatch using particle swarms optimization algorithm based Pareto Optimal Set
International Symposium on Neural Networks, 2009Co-Authors: Yan Li, Panpan Jing, Defeng Hu, Buhan Zhang, Xinbo Ruan, Xiaoyang Miao, Defeng ChangAbstract:An improved particle swarms optimization algorithm based on Pareto Optimal Set is proposed to optimize the reactive power in power system, which is a multiple objectives optimization problem. The proposed algorithm develops the new fitness assignment and random inertia weight strategy, problem-specific linkages can be learned by examining a randomly chosen collection of points in the search space, the improved algorithm also has the ability to avoid getting trapped in local optima due to prematurity, applying it to the calculation of the power systems of IEEE6-bus and IEEE14-bus, the calculation results prove its effectiveness.
-
ISNN (3) - Optimal Reactive Power Dispatch Using Particle Swarms Optimization Algorithm Based Pareto Optimal Set
Advances in Neural Networks – ISNN 2009, 2009Co-Authors: Yan Li, Panpan Jing, Defeng Hu, Buhan Zhang, Xinbo Ruan, Xiaoyang Miao, Defeng ChangAbstract:An improved particle swarms optimization algorithm based on Pareto Optimal Set is proposed to optimize the reactive power in power system, which is a multiple objectives optimization problem. The proposed algorithm develops the new fitness assignment and random inertia weight strategy, problem-specific linkages can be learned by examining a randomly chosen collection of points in the search space, the improved algorithm also has the ability to avoid getting trapped in local optima due to prematurity, applying it to the calculation of the power systems of IEEE6-bus and IEEE14-bus, the calculation results prove its effectiveness.