Warehouses are obliged to optimize their operations with regard to multiple objectives, such as m... more Warehouses are obliged to optimize their operations with regard to multiple objectives, such as maximizing effective use space, equipment, labor, maximize accessibility of products, maximize amount of processed orders and all this should be achieved whilst minimizing order processing times, distance traveled, broken promises, errors and not to forget the operational cost. A product placement problem for a warehouse is
Abstract: Many real-world manufacturing problems are too complex to be modelled analytically. In ... more Abstract: Many real-world manufacturing problems are too complex to be modelled analytically. In these scenarios simulation-based optimisation is a powerful tool to determine optimal system settings. While traditional optimisation methods have been unable to cope ...
SIMULATION-BASED OPTIMISATION USING LOCAL SEARCH AND NEURAL NETWORK METAMODELS Anna Persson Henri... more SIMULATION-BASED OPTIMISATION USING LOCAL SEARCH AND NEURAL NETWORK METAMODELS Anna Persson Henrik Grimm Amos Ng Centre for Intelligent Automation Box 408 University of Skövde Sweden {anna.persson, henrik ... [8] PA Fishwick, Neural network ...
AbstractThis paper presents a new metamodel-assisted metaheuristic algorithm for optimisation pr... more AbstractThis paper presents a new metamodel-assisted metaheuristic algorithm for optimisation problems involving computationally expensive simulations. The algorithm, called Global Probing Search, is a population-based algorithm designed for global optimisation. The main idea ...
... Philip Hedenstierna Virtual Systems Research Centre University of Skövde Skövde, Swedenphilip... more ... Philip Hedenstierna Virtual Systems Research Centre University of Skövde Skövde, Swedenphilip.hedenstierna@his.se Amos HC Ng Virtual Systems Research Centre University of Skövde Skövde, Sweden amos.ng@his.se ... [5] TE Vollman, WL Berry, DC Whybark and FR ...
Abstract This paper describes the research project OPTIMIST at the University of Skövde. The proj... more Abstract This paper describes the research project OPTIMIST at the University of Skövde. The project is focused on the development and application of intelligent techniques for simulation-based optimisation of real-world industrial problems. A software environment ...
A method for analyzing production systems by applying multi objective optimization and data minin... more A method for analyzing production systems by applying multi objective optimization and data mining techniques on simulation models, so called Simulation Based Innovization (SBI) is presented in this paper. The solution set generated from multi objective optimization is supposed to unveil certain relationships among variables and it is expected that using the concept of innovization to reveal these relationships could be a promising procedure. The aim of SBI is to reveal insight of the parameters affecting the system being simulated, e.g. a production or distribution system, through post-optimality analysis of the solutions acquired from multi-objective optimization and to visualize it in an understandable manner, using visual analytics techniques. This analysis is done by data mining techniques such as decision trees which are comprehensible models built up based on the correlation between the input data set and the objective solution set. The decision tree exposes the rule set that...
ABSTRACT For evaluating performance of a multi-objective optimization for finding the entire effi... more ABSTRACT For evaluating performance of a multi-objective optimization for finding the entire efficient front, a number of metrics, such as hypervolume, inverse generational distance, etc. exists. However, for evaluating an EMO algorithm for finding a subset of the efficient frontier, the existing metrics are inadequate. There does not exist many performance metrics for evaluating a partial preferred efficient set. In this paper, we suggest a metric which can be used for such purposes for both attainable and unattainable reference points. Results on a number of two-objective problems reveal its working principle and its importance in assessing different algorithms. The results are promising and encouraging for its further use.
ASME 2010 International Manufacturing Science and Engineering Conference, Volume 2, 2010
Almost in every discipline involved in Product and Production Development (PPD), optimization pro... more Almost in every discipline involved in Product and Production Development (PPD), optimization problems arrive. These real-world problems are too complex to be solved by analytical models and classical optimization methods. CAx and Virtual Manufacturing (VM) tools are on the ...
ABSTRACT In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but l... more ABSTRACT In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but locally focused non-dominated front in a decision maker’s area of interest, as close as possible to the true Pareto-front. The optimization can focus its efforts towards the preferred area and achieve a better result [7, 9, 13, 17]. The modeled and simulated systems are often stochastic and a common method to handle the objective noise is Resampling. The given preference information allows to define better resampling strategies which further improve the optimization result. In this paper, resampling strategies are proposed that base the sampling allocation on multiple factors, and thereby combine multiple resampling strategies proposed by the authors in [15]. These factors are, for example, the Pareto-rank of a solution and its distance to the decision maker’s area of interest. The proposed hybrid Dynamic Resampling Strategy DR2 is evaluated on the Reference point-guided NSGA-II optimization algorithm (R-NSGA-II) [9].
Proceedings of the 2006 Winter Simulation Conference, 2006
... One of our current research focuses which is related to this work is the design of an Interne... more ... One of our current research focuses which is related to this work is the design of an Internet-based component ar-chitecture ... Srinivas, N. and Deb, K. (1995) Multiobjective optimiza-tion using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3):221248 ...
ABSTRACT This paper introduces a novel methodology for the optimization, analysis and decision su... more ABSTRACT This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, 2011
... 408, 541 28 Skövde, Sweden e-mail: tehseen. aslam@ his. se P. Hedenstierna e-mail: philip. he... more ... 408, 541 28 Skövde, Sweden e-mail: tehseen. aslam@ his. se P. Hedenstierna e-mail: philip. hedenstierna@ his. se AHC Ng e-mail: amos. ng@ his. se L. Wang e-mail: lihui. wang@ his. se L. Wang et al.(eds.), Multi-objective ...
ABSTRACT Simulation-based innovization is a method for extracting knowledge from a simulation mod... more ABSTRACT Simulation-based innovization is a method for extracting knowledge from a simulation model and optimization. This method can help decision makers to make high-quality decisions for their manufacturing systems so as to enhance the competitiveness of companies. Nevertheless, the simulation-based innovization process can be computationally costly and having these resources in-house can be expensive. By running the process in a cloud environment instead, the company only pays for the resources they are using. This paper proposes the concept of a cloud-based computing platform that can run the simulation-based innovization process and discuss its possibilities and challenges.
ABSTRACT Evolutionary algorithms are often applied to solve multi-objective optimization problems... more ABSTRACT Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.
Warehouses are obliged to optimize their operations with regard to multiple objectives, such as m... more Warehouses are obliged to optimize their operations with regard to multiple objectives, such as maximizing effective use space, equipment, labor, maximize accessibility of products, maximize amount of processed orders and all this should be achieved whilst minimizing order processing times, distance traveled, broken promises, errors and not to forget the operational cost. A product placement problem for a warehouse is
Abstract: Many real-world manufacturing problems are too complex to be modelled analytically. In ... more Abstract: Many real-world manufacturing problems are too complex to be modelled analytically. In these scenarios simulation-based optimisation is a powerful tool to determine optimal system settings. While traditional optimisation methods have been unable to cope ...
SIMULATION-BASED OPTIMISATION USING LOCAL SEARCH AND NEURAL NETWORK METAMODELS Anna Persson Henri... more SIMULATION-BASED OPTIMISATION USING LOCAL SEARCH AND NEURAL NETWORK METAMODELS Anna Persson Henrik Grimm Amos Ng Centre for Intelligent Automation Box 408 University of Skövde Sweden {anna.persson, henrik ... [8] PA Fishwick, Neural network ...
AbstractThis paper presents a new metamodel-assisted metaheuristic algorithm for optimisation pr... more AbstractThis paper presents a new metamodel-assisted metaheuristic algorithm for optimisation problems involving computationally expensive simulations. The algorithm, called Global Probing Search, is a population-based algorithm designed for global optimisation. The main idea ...
... Philip Hedenstierna Virtual Systems Research Centre University of Skövde Skövde, Swedenphilip... more ... Philip Hedenstierna Virtual Systems Research Centre University of Skövde Skövde, Swedenphilip.hedenstierna@his.se Amos HC Ng Virtual Systems Research Centre University of Skövde Skövde, Sweden amos.ng@his.se ... [5] TE Vollman, WL Berry, DC Whybark and FR ...
Abstract This paper describes the research project OPTIMIST at the University of Skövde. The proj... more Abstract This paper describes the research project OPTIMIST at the University of Skövde. The project is focused on the development and application of intelligent techniques for simulation-based optimisation of real-world industrial problems. A software environment ...
A method for analyzing production systems by applying multi objective optimization and data minin... more A method for analyzing production systems by applying multi objective optimization and data mining techniques on simulation models, so called Simulation Based Innovization (SBI) is presented in this paper. The solution set generated from multi objective optimization is supposed to unveil certain relationships among variables and it is expected that using the concept of innovization to reveal these relationships could be a promising procedure. The aim of SBI is to reveal insight of the parameters affecting the system being simulated, e.g. a production or distribution system, through post-optimality analysis of the solutions acquired from multi-objective optimization and to visualize it in an understandable manner, using visual analytics techniques. This analysis is done by data mining techniques such as decision trees which are comprehensible models built up based on the correlation between the input data set and the objective solution set. The decision tree exposes the rule set that...
ABSTRACT For evaluating performance of a multi-objective optimization for finding the entire effi... more ABSTRACT For evaluating performance of a multi-objective optimization for finding the entire efficient front, a number of metrics, such as hypervolume, inverse generational distance, etc. exists. However, for evaluating an EMO algorithm for finding a subset of the efficient frontier, the existing metrics are inadequate. There does not exist many performance metrics for evaluating a partial preferred efficient set. In this paper, we suggest a metric which can be used for such purposes for both attainable and unattainable reference points. Results on a number of two-objective problems reveal its working principle and its importance in assessing different algorithms. The results are promising and encouraging for its further use.
ASME 2010 International Manufacturing Science and Engineering Conference, Volume 2, 2010
Almost in every discipline involved in Product and Production Development (PPD), optimization pro... more Almost in every discipline involved in Product and Production Development (PPD), optimization problems arrive. These real-world problems are too complex to be solved by analytical models and classical optimization methods. CAx and Virtual Manufacturing (VM) tools are on the ...
ABSTRACT In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but l... more ABSTRACT In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but locally focused non-dominated front in a decision maker’s area of interest, as close as possible to the true Pareto-front. The optimization can focus its efforts towards the preferred area and achieve a better result [7, 9, 13, 17]. The modeled and simulated systems are often stochastic and a common method to handle the objective noise is Resampling. The given preference information allows to define better resampling strategies which further improve the optimization result. In this paper, resampling strategies are proposed that base the sampling allocation on multiple factors, and thereby combine multiple resampling strategies proposed by the authors in [15]. These factors are, for example, the Pareto-rank of a solution and its distance to the decision maker’s area of interest. The proposed hybrid Dynamic Resampling Strategy DR2 is evaluated on the Reference point-guided NSGA-II optimization algorithm (R-NSGA-II) [9].
Proceedings of the 2006 Winter Simulation Conference, 2006
... One of our current research focuses which is related to this work is the design of an Interne... more ... One of our current research focuses which is related to this work is the design of an Internet-based component ar-chitecture ... Srinivas, N. and Deb, K. (1995) Multiobjective optimiza-tion using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3):221248 ...
ABSTRACT This paper introduces a novel methodology for the optimization, analysis and decision su... more ABSTRACT This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, 2011
... 408, 541 28 Skövde, Sweden e-mail: tehseen. aslam@ his. se P. Hedenstierna e-mail: philip. he... more ... 408, 541 28 Skövde, Sweden e-mail: tehseen. aslam@ his. se P. Hedenstierna e-mail: philip. hedenstierna@ his. se AHC Ng e-mail: amos. ng@ his. se L. Wang e-mail: lihui. wang@ his. se L. Wang et al.(eds.), Multi-objective ...
ABSTRACT Simulation-based innovization is a method for extracting knowledge from a simulation mod... more ABSTRACT Simulation-based innovization is a method for extracting knowledge from a simulation model and optimization. This method can help decision makers to make high-quality decisions for their manufacturing systems so as to enhance the competitiveness of companies. Nevertheless, the simulation-based innovization process can be computationally costly and having these resources in-house can be expensive. By running the process in a cloud environment instead, the company only pays for the resources they are using. This paper proposes the concept of a cloud-based computing platform that can run the simulation-based innovization process and discuss its possibilities and challenges.
ABSTRACT Evolutionary algorithms are often applied to solve multi-objective optimization problems... more ABSTRACT Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.
Many real-world manufacturing problems are too complex to be modelled analytically. In these scen... more Many real-world manufacturing problems are too complex to be modelled analytically. In these scenarios, simulation-based optimisation is a powerful tool to determine optimal system settings. While traditional optimisation met hods have been unable to cope with the complexities of many real-world problems approached by simulation, evolutionary methods have proven to be highly useful. This paper presents an evolutionary algorithm for
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