Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369107
E. Zitzler
Summary form only given. The field of evolutionary multi-criterion optimization has undergone a tremendous growth since the first approaches have been proposed in the mid-1980's. Due to their population-based structure, evolutionary algorithms are inherently suited to optimization problems where the goal is to find a set of solutions. For this reason and with the advent of sufficient computing resources, they have become a valuable tool to approximate the set of Pareto-optimal solutions for highly complex applications in various domains. Several trends could be observed during the last two decades. Concerning the design of EMO algorithms, the early methods used component-wise selection mechanisms, while meanwhile dominance-based fitness assignment schemes combined with diversity preservation techniques and elitist environmental selection are most popular. A further paradigm shift has been initiated where the search is based on set quality measures. A second trend is related to the performance assessment of EMO methods. The first studies were proof-of-principle results and mainly using visual comparisons to evaluate simulation results. Later, quantitative measures were introduced and a variety of approaches for assessing the quality of sets have been proposed. The issue of statistical testing in the context of random sets has gained only little attention until 2000, but has become more and more standard meanwhile. Finally, a third trend addresses theoretical aspects of EMO. Within the last four years, several studies have been presented run-time analyses of simple model algorithms for various types of problems; these complement the many empirical studies published in the second decade of EMO history. Despite the many advances that have been achieved during the last 20 years, there are several challenges ahead. The integration of the search process into the decision making process has been discussed for many years, but so far only little research has been devoted to real interactive EMO methods. In the light of this question, especially problems with a large number of objectives are of particular interest. But many other topics can be mentioned in this context: uncertainty, robustness, and integration of exact optimization methods, to name only a few.
{"title":"Two Decades Of Evolutionary Multi-Criterion Optimization: A Glance Back And A Look Ahead","authors":"E. Zitzler","doi":"10.1109/MCDM.2007.369107","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369107","url":null,"abstract":"Summary form only given. The field of evolutionary multi-criterion optimization has undergone a tremendous growth since the first approaches have been proposed in the mid-1980's. Due to their population-based structure, evolutionary algorithms are inherently suited to optimization problems where the goal is to find a set of solutions. For this reason and with the advent of sufficient computing resources, they have become a valuable tool to approximate the set of Pareto-optimal solutions for highly complex applications in various domains. Several trends could be observed during the last two decades. Concerning the design of EMO algorithms, the early methods used component-wise selection mechanisms, while meanwhile dominance-based fitness assignment schemes combined with diversity preservation techniques and elitist environmental selection are most popular. A further paradigm shift has been initiated where the search is based on set quality measures. A second trend is related to the performance assessment of EMO methods. The first studies were proof-of-principle results and mainly using visual comparisons to evaluate simulation results. Later, quantitative measures were introduced and a variety of approaches for assessing the quality of sets have been proposed. The issue of statistical testing in the context of random sets has gained only little attention until 2000, but has become more and more standard meanwhile. Finally, a third trend addresses theoretical aspects of EMO. Within the last four years, several studies have been presented run-time analyses of simple model algorithms for various types of problems; these complement the many empirical studies published in the second decade of EMO history. Despite the many advances that have been achieved during the last 20 years, there are several challenges ahead. The integration of the search process into the decision making process has been discussed for many years, but so far only little research has been devoted to real interactive EMO methods. In the light of this question, especially problems with a large number of objectives are of particular interest. But many other topics can be mentioned in this context: uncertainty, robustness, and integration of exact optimization methods, to name only a few.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117329613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369424
Luis V. Santana-Quintero, Víctor A. Serrano-Hernandez, C. Coello, A. G. Hernández-Díaz, J. M. Luque
This paper presents a new multi-objective evolutionary algorithm (MOEA) which adopts a radial basis function (RBF) approach in order to reduce the number of fitness function evaluations performed to reach the Pareto front. The specific method adopted is derived from a comparative study conducted among several RBFs. In all cases, the NSGA-II (which is an approach representative of the state-of-the-art in the area) is adopted as our search engine with which the RBFs are hybridized. The resulting algorithm can produce very reasonable approximations of the true Pareto front with a very low number of evaluations, but is not able to spread solutions in an appropriate manner. This led us to introduce a second stage to the algorithm in which it is hybridized with rough sets theory in order to improve the spread of solutions. Rough sets, in this case, act as a local search approach which is able to generate solutions in the neighborhood of the few nondominated solutions previously generated. We show that our proposed hybrid approach only requires 2,000 fitness function evaluations in order to solve test problems with up to 30 decision variables. This is a very low value when compared with today's standards reported in the specialized literature
{"title":"Use of Radial Basis Functions and Rough Sets for Evolutionary Multi-Objective Optimization","authors":"Luis V. Santana-Quintero, Víctor A. Serrano-Hernandez, C. Coello, A. G. Hernández-Díaz, J. M. Luque","doi":"10.1109/MCDM.2007.369424","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369424","url":null,"abstract":"This paper presents a new multi-objective evolutionary algorithm (MOEA) which adopts a radial basis function (RBF) approach in order to reduce the number of fitness function evaluations performed to reach the Pareto front. The specific method adopted is derived from a comparative study conducted among several RBFs. In all cases, the NSGA-II (which is an approach representative of the state-of-the-art in the area) is adopted as our search engine with which the RBFs are hybridized. The resulting algorithm can produce very reasonable approximations of the true Pareto front with a very low number of evaluations, but is not able to spread solutions in an appropriate manner. This led us to introduce a second stage to the algorithm in which it is hybridized with rough sets theory in order to improve the spread of solutions. Rough sets, in this case, act as a local search approach which is able to generate solutions in the neighborhood of the few nondominated solutions previously generated. We show that our proposed hybrid approach only requires 2,000 fitness function evaluations in order to solve test problems with up to 30 decision variables. This is a very low value when compared with today's standards reported in the specialized literature","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127789170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369439
V. Dobrokhodov, R. Statnikov
This paper introduces an effective computational environment for multi-objective decision-making, optimization and identification. The paper adopts multi-objective vector identification methodology and performance assessment provided by the parameter space investigation method (PSI). The main feature of this methodology is in the fact that various design objectives are taken into consideration in their natural form without reducing dimensionality of the problem and therefore without distorting its nature. Therefore, there is no need for artificial convolution and weighting of multiple criteria. Moreover, the design alternatives are assessed explicitly versus multiple given requirements. The main practical purpose of this work is of twofold. First, we introduce an optimization framework and technique that allows to determine feasible and Pareto sets of the numerous uncertainties inherent for real-world engineering systems. This framework tightly couples principal advantages of MatLab/Simulink simulation engine with the unique properties of the multi-objective PSI method. Second, we show key benefits of the MatLab/PSI bundle on the example of identification of the principal aerodynamic characteristics and apparent masses of the controllable circular parachute
{"title":"Multi-Criteria Identification of a Controllable Descending System","authors":"V. Dobrokhodov, R. Statnikov","doi":"10.1109/MCDM.2007.369439","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369439","url":null,"abstract":"This paper introduces an effective computational environment for multi-objective decision-making, optimization and identification. The paper adopts multi-objective vector identification methodology and performance assessment provided by the parameter space investigation method (PSI). The main feature of this methodology is in the fact that various design objectives are taken into consideration in their natural form without reducing dimensionality of the problem and therefore without distorting its nature. Therefore, there is no need for artificial convolution and weighting of multiple criteria. Moreover, the design alternatives are assessed explicitly versus multiple given requirements. The main practical purpose of this work is of twofold. First, we introduce an optimization framework and technique that allows to determine feasible and Pareto sets of the numerous uncertainties inherent for real-world engineering systems. This framework tightly couples principal advantages of MatLab/Simulink simulation engine with the unique properties of the multi-objective PSI method. Second, we show key benefits of the MatLab/PSI bundle on the example of identification of the principal aerodynamic characteristics and apparent masses of the controllable circular parachute","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127355388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369421
F. Campos, André M. M. Neves, F. D. Souza
The uncertainty may be classified into two major groups, "objective uncertainty" and "subjective uncertainty". The subject of this article is the decision making under subjective uncertainty. One of the formal models that deal with subjective uncertainty, the mathematical theory of evidence, is extended and its counter-intuitive behavior corrected, allowing the making of correct decisions in a wider range of situations than the original model. The mathematical theory of evidence, or Dempster-Shafer theory, is a popular formalism to model someone's degrees of belief. This theory provides a method for combining evidence from different sources without prior knowledge of their distributions, it is also possible to assign probability values to sets of possibilities rather than to single events only, and it is unnecessary to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. However, it has some pitfalls caused by the non-natural embodiment of the uncertainty in the results. In this paper we present a method of automatic embodiment of the uncertainty that overcomes the aforementioned pitfalls, allowing the combination of evidence with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypotheses. This is accomplished by means of a new rule of combination of bodies of evidence that embodies in the numeric results the unknown belief and conflict among the evidence, naturally modeling the epistemic reasoning
{"title":"Decision Making under Subjective Uncertainty","authors":"F. Campos, André M. M. Neves, F. D. Souza","doi":"10.1109/MCDM.2007.369421","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369421","url":null,"abstract":"The uncertainty may be classified into two major groups, \"objective uncertainty\" and \"subjective uncertainty\". The subject of this article is the decision making under subjective uncertainty. One of the formal models that deal with subjective uncertainty, the mathematical theory of evidence, is extended and its counter-intuitive behavior corrected, allowing the making of correct decisions in a wider range of situations than the original model. The mathematical theory of evidence, or Dempster-Shafer theory, is a popular formalism to model someone's degrees of belief. This theory provides a method for combining evidence from different sources without prior knowledge of their distributions, it is also possible to assign probability values to sets of possibilities rather than to single events only, and it is unnecessary to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. However, it has some pitfalls caused by the non-natural embodiment of the uncertainty in the results. In this paper we present a method of automatic embodiment of the uncertainty that overcomes the aforementioned pitfalls, allowing the combination of evidence with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypotheses. This is accomplished by means of a new rule of combination of bodies of evidence that embodies in the numeric results the unknown belief and conflict among the evidence, naturally modeling the epistemic reasoning","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369410
G. Lamont, James N. Slear, K. Melendez
The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles (UAV). The system integrates several problem domains including path planning, vehicle routing, and swarm behavior as based upon a hierarchical architecture. The developed system consists of a parallel, multi-objective evolutionary algorithm-based terrain-following parallel path planner and an evolutionary algorithm-based vehicle router. Objectives include minimizing cost and risk generally associated with a three dimensional vehicle routing problem (VRP). The culmination of this effort is the development of an extensible developmental path planning model integrated with swarm behavior and tested with a parallel UAV simulation. Discussions on the system's capabilities are presented along with recommendations for further development.
{"title":"UAV Swarm Mission Planning and Routing using Multi-Objective Evolutionary Algorithms","authors":"G. Lamont, James N. Slear, K. Melendez","doi":"10.1109/MCDM.2007.369410","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369410","url":null,"abstract":"The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles (UAV). The system integrates several problem domains including path planning, vehicle routing, and swarm behavior as based upon a hierarchical architecture. The developed system consists of a parallel, multi-objective evolutionary algorithm-based terrain-following parallel path planner and an evolutionary algorithm-based vehicle router. Objectives include minimizing cost and risk generally associated with a three dimensional vehicle routing problem (VRP). The culmination of this effort is the development of an extensible developmental path planning model integrated with swarm behavior and tested with a parallel UAV simulation. Discussions on the system's capabilities are presented along with recommendations for further development.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127613670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369116
S. Schmidt, E. Chang, T. Dillon, R. Steele
The emergence of semantic overlay networks as instruments to improve security, trust and stability in distributed virtual communities is recognized widely in the research community. We propose a fuzzy logic based framework which integrates social information such as trustworthiness, reputation and credibility ratings for individuals, alliances, organizations, services and products in e-commerce markets. This framework is designed to support the decision making process of autonomous agents during the selection of the optimal business partner. Fuzzy systems provide the ideal capabilities to process multiple criteria, which are composed of imprecise information and attribute definitions expressed in natural language. The proposed fuzzy models implement the DEco Arch framework and ontologies which provide details about concepts and their relationships in virtual communities
{"title":"Fuzzy Decision Support for Service Selection in E-Business Environments","authors":"S. Schmidt, E. Chang, T. Dillon, R. Steele","doi":"10.1109/MCDM.2007.369116","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369116","url":null,"abstract":"The emergence of semantic overlay networks as instruments to improve security, trust and stability in distributed virtual communities is recognized widely in the research community. We propose a fuzzy logic based framework which integrates social information such as trustworthiness, reputation and credibility ratings for individuals, alliances, organizations, services and products in e-commerce markets. This framework is designed to support the decision making process of autonomous agents during the selection of the optimal business partner. Fuzzy systems provide the ideal capabilities to process multiple criteria, which are composed of imprecise information and attribute definitions expressed in natural language. The proposed fuzzy models implement the DEco Arch framework and ontologies which provide details about concepts and their relationships in virtual communities","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129391914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369419
T. Matsui, M. Sakawa, Kosuke Kato, Takeshi Uno, K. Tamada
Particle swarm optimization (PSO) was proposed by Kennedy et al. as a general approximate solution method for nonlinear programming problems. Its efficiency has been shown, but there have been left some shortcomings of the method. Thus, the authors proposed a revised PSO (rPSO) method incorporating the homomorphous mapping and the multiple stretching in order to cope with these shortcomings. In this paper, we construct an interactive fuzzy satisficing method for multiobjective nonlinear programming problems based on the rPSO. Furthermore, in order to obtain better solutions in consideration of the property of multiobjective programming problems, we incorporate the direction to nondominated solutions into the rPSO. Finally, we show the efficiency of the proposed method by applying it to numerical examples
{"title":"An interactive fuzzy satisficing method through particle swarm optimization for multiobjective nonlinear programming problems","authors":"T. Matsui, M. Sakawa, Kosuke Kato, Takeshi Uno, K. Tamada","doi":"10.1109/MCDM.2007.369419","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369419","url":null,"abstract":"Particle swarm optimization (PSO) was proposed by Kennedy et al. as a general approximate solution method for nonlinear programming problems. Its efficiency has been shown, but there have been left some shortcomings of the method. Thus, the authors proposed a revised PSO (rPSO) method incorporating the homomorphous mapping and the multiple stretching in order to cope with these shortcomings. In this paper, we construct an interactive fuzzy satisficing method for multiobjective nonlinear programming problems based on the rPSO. Furthermore, in order to obtain better solutions in consideration of the property of multiobjective programming problems, we incorporate the direction to nondominated solutions into the rPSO. Finally, we show the efficiency of the proposed method by applying it to numerical examples","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"2003 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125788637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369111
R. Statnikov, K. Anil, A. Bordetsky, A. Statnikov
One of the basic engineering optimization problems is the problem of improving a prototype. This problem is constantly encountered by industrial and academic organizations that produce and design various objects (e.g., motor vehicles, machine tools, ships, and aircraft). This paper presents an approach for improving a prototype by construction of the feasible and Pareto sets while performing multicriteria analysis. We introduce visualization methods that facilitate constructing the feasible and Pareto sets. Using these techniques, an expert can correctly state and solve the problem under consideration in a series of dialogs with the computer. Finally, we present a case study of applying these methods to a problem of improving a prototype of the ship
{"title":"Visualization Tools for Multicriteria Analysis of the Prototype Improvement Problem","authors":"R. Statnikov, K. Anil, A. Bordetsky, A. Statnikov","doi":"10.1109/MCDM.2007.369111","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369111","url":null,"abstract":"One of the basic engineering optimization problems is the problem of improving a prototype. This problem is constantly encountered by industrial and academic organizations that produce and design various objects (e.g., motor vehicles, machine tools, ships, and aircraft). This paper presents an approach for improving a prototype by construction of the feasible and Pareto sets while performing multicriteria analysis. We introduce visualization methods that facilitate constructing the feasible and Pareto sets. Using these techniques, an expert can correctly state and solve the problem under consideration in a series of dialogs with the computer. Finally, we present a case study of applying these methods to a problem of improving a prototype of the ship","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369429
Seung Jun Lee, Kim Mo, P. Seong
In safety critical systems, especially in nuclear power plants (NPPs), human error has been introduced as one of the serious causes of accidents. In order to prevent human errors, many efforts have been made to improve main control room (MCR) interface designs and to develop decision support systems that allow convenient MCR operation and maintenance. In this paper, an integrated decision support system to aid the cognitive process of operators is proposed for advanced MCRs in future NPPs. This work suggests support system design considered an operator's cognitive process. Various kinds of support systems for advanced MCRs have been developed or are in development. Therefore, a design basis regarding what kinds of support systems are appropriate for MCR operators is necessary. The proposed system supports not merely a particular task, but also the entire operation process based on a human cognitive process model. It supports the operator's entire cognitive process by integrating support systems that support each cognitive activity. Furthermore, two decision support systems are developed. The fault diagnosis advisory system is to make the task of fault diagnosis easier and to reduce errors by quickly suggesting likely faults based on the highest probability of their occurrence. The operation validation system is to provide an advisory function to supervise and validate the operator's actions during abnormal environments
{"title":"Development of an Integrated Decision Support System to Aid the Cognitive Activities of Operators in Main Control Rooms of Nuclear Power Plants","authors":"Seung Jun Lee, Kim Mo, P. Seong","doi":"10.1109/MCDM.2007.369429","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369429","url":null,"abstract":"In safety critical systems, especially in nuclear power plants (NPPs), human error has been introduced as one of the serious causes of accidents. In order to prevent human errors, many efforts have been made to improve main control room (MCR) interface designs and to develop decision support systems that allow convenient MCR operation and maintenance. In this paper, an integrated decision support system to aid the cognitive process of operators is proposed for advanced MCRs in future NPPs. This work suggests support system design considered an operator's cognitive process. Various kinds of support systems for advanced MCRs have been developed or are in development. Therefore, a design basis regarding what kinds of support systems are appropriate for MCR operators is necessary. The proposed system supports not merely a particular task, but also the entire operation process based on a human cognitive process model. It supports the operator's entire cognitive process by integrating support systems that support each cognitive activity. Furthermore, two decision support systems are developed. The fault diagnosis advisory system is to make the task of fault diagnosis easier and to reduce errors by quickly suggesting likely faults based on the highest probability of their occurrence. The operation validation system is to provide an advisory function to supervise and validate the operator's actions during abnormal environments","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127414346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369409
P. Wu, R. Clothier, D. Campbell, R. Walker
This paper discusses the development of a multi-objective mission flight planning algorithm for unmanned aerial system (UAS) operations within the National Airspace System (NAS). Existing methods for multi-objective planning are largely confined to two dimensional searches and/or acyclic graphs in deterministic environments; many are computationally infeasible for large state spaces. In this paper, a multi-objective fuzzy logic decision maker is used to augment the D* Lite graph search algorithm in finding a near optimal path. This not only enables evaluation and trade-off between multiple objectives when choosing a path in three dimensional space, but also allows for the modelling of data uncertainty. A case study scenario is developed to illustrate the performance of a number of different algorithms. It is shown that a fuzzy multi-objective mission flight planner provides a viable method for embedding human expert knowledge in a computationally feasible algorithm
{"title":"Fuzzy Multi-Objective Mission Flight Planning in Unmanned Aerial Systems","authors":"P. Wu, R. Clothier, D. Campbell, R. Walker","doi":"10.1109/MCDM.2007.369409","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369409","url":null,"abstract":"This paper discusses the development of a multi-objective mission flight planning algorithm for unmanned aerial system (UAS) operations within the National Airspace System (NAS). Existing methods for multi-objective planning are largely confined to two dimensional searches and/or acyclic graphs in deterministic environments; many are computationally infeasible for large state spaces. In this paper, a multi-objective fuzzy logic decision maker is used to augment the D* Lite graph search algorithm in finding a near optimal path. This not only enables evaluation and trade-off between multiple objectives when choosing a path in three dimensional space, but also allows for the modelling of data uncertainty. A case study scenario is developed to illustrate the performance of a number of different algorithms. It is shown that a fuzzy multi-objective mission flight planner provides a viable method for embedding human expert knowledge in a computationally feasible algorithm","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121300627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：