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  • 1.
    Aittokoski, Timo
    et al.
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Cost Effective Simulation-Based Multiobjective Optimization in Performance of Internal Combustion Engine2008In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 40, no 7, p. 593-612Article in journal (Refereed)
    Abstract [en]

    Solving real-life engineering problems requires often multiobjective, global, and efficient (in terms of objective function evaluations) treatment. In this study, we consider problems of this type by discussing some drawbacks of the current methods and then introduce a new population-based multiobjective optimization algorithm UPS-EMOA which produces a dense (not limited to the population size) approximation of the Pareto-optimal set in a computationally effective manner.

  • 2.
    Aittokoski, Timo
    et al.
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Miettinen, Kaisa
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Decreasing Computational Cost of Simulation Based Interactive Multiobjective Optimization with Adustable Solution Accuracy2008Report (Other academic)
  • 3.
    Aittokoski, Timo
    et al.
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Miettinen, Kaisa
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Efficient Evolutionary Method to Approximate the Pareto Optimal Set in Multiobjective Optimization2008In: Proceedings of the International Conference on Engineering Optimization EngOpt 2008, 2008Conference paper (Other academic)
    Abstract [en]

    Solving real-life engineering problems requires often multiobjective, global and efficient (in terms of ob-jective function evaluations) treatment. In this study, we consider problems of this type by discussingsome drawbacks of the current methods and then introduce a new population based multiobjective op-timization algorithm which produces a dense (not limited to the population size) approximation of thePareto optimal set in a computationally effective manner.

  • 4.
    Aittokoski, Timo
    et al.
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Äyrämö, Sami
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Clustering aided approach for decision making in computationally expensive multiobjective optimization2009In: Optimization Methods and Software, ISSN 1055-6788, E-ISSN 1029-4937, Vol. 24, no 2, p. 157-174Article in journal (Refereed)
    Abstract [en]

    Typically, industrial optimization problems need to be solved in an efficient, multiobjective and global manner, because they are often computationally expensive (as function values are typically based on simulations), they may contain multiple conflicting objectives, and they may have several local optima. Solving such problems may be challenging and time consuming when the aim is to find the most preferred Pareto optimal solution.

    In this study, we propose a method where we use an advanced clustering technique to reveal essential characteristics of the approximation of the Pareto optimal set, which has been generated beforehand. Thus, the decision maker (DM) is involved only after the most time consuming computation is finished. After the initiation phase, a moderate number of cluster prototypes projected to the Pareto optimal set is presented to the DM to be studied. This allows him/her to rapidly gain an overall understanding of the main characteristics of the problem without placing too much cognitive load on the DM. Furthermore, we also suggest some ways of applying our approach to different types of problems and demonstrate it with an example related to internal combustion engine design.

  • 5.
    Bokrantz, Rasmus
    et al.
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Improved plan quality in multicriteria radiation therapy optimization by projections onto the Pareto surface2012Report (Other academic)
    Abstract [en]

    We consider an approach to multicriteria radiation therapy optimization where the clinical treatment plan is selected from a representationof the set of Pareto optimal treatment plans in the form of a discrete setof plans and their combinations. The approximate nature of this representation implies that a selected plan in general has an approximationerror with respect to Pareto optimality. To assess and, if necessary, improve the quality of such plans, a technique is suggested that eliminatesthe approximation error of a given treatment plan by a projection ontothe Pareto surface. A more elaborate form of projection is also suggested that requires the projected solution to be not only as good asthe input plan in terms of objective function values, but also equallygood or better with respect to the three-dimensional dose distribution.The versatility of the suggested technique is demonstrated by application to planning for step-and-shoot and sliding window delivery ofintensity-modulated radiation therapy, and planning for spot-scanneddelivery of intensity-modulated proton therapy. Our numerical resultsshow that the proposed projections generally lead to improved sparingof organs at risk and a higher degree of dose conformity compared towhen projections are not performed.

  • 6.
    Bokrantz, Rasmus
    et al.
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory. RaySearch Laboratories, Sweden.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory. University of Jyväskylä, Finland.
    Projections onto the Pareto surface in multicriteria radiation therapy optimization2015In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 42, no 10, p. 5862-5870Article in journal (Refereed)
    Abstract [en]

    Purpose: To eliminate or reduce the error to Pareto optimality that arises in Pareto surface navigation when the Pareto surface is approximated by a small number of plans. Methods: The authors propose to project the navigated plan onto the Pareto surface as a postprocessing step to the navigation. The projection attempts to find a Pareto optimal plan that is at least as good as or better than the initial navigated plan with respect to all objective functions. An augmented form of projection is also suggested where dose-volume histogram constraints are used to prevent that the projection causes a violation of some clinical goal. The projections were evaluated with respect to planning for intensity modulated radiation therapy delivered by step-and-shoot and sliding window and spot-scanned intensity modulated proton therapy. Retrospective plans were generated for a prostate and a head and neck case. Results: The projections led to improved dose conformity and better sparing of organs at risk (OARs) for all three delivery techniques and both patient cases. The mean dose to OARs decreased by 3.1 Gy on average for the unconstrained form of the projection and by 2.0 Gy on average when dose-volume histogram constraints were used. No consistent improvements in target homogeneity were observed. Conclusions: There are situations when Pareto navigation leaves room for improvement in OAR sparing and dose conformity, for example, if the approximation of the Pareto surface is coarse or the problem formulation has too permissive constraints. A projection onto the Pareto surface can identify an inaccurate Pareto surface representation and, if necessary, improve the quality of the navigated plan.

  • 7.
    Branke, Jürgen
    et al.
    University of Karlsruhe, Institute AIFB, Germany.
    Deb, KalyanmoyMiettinen, KaisaKTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.Slowinski, RomanPoznan University of Technology, Institute of Computing Science.
    Multiobjective Optimization: Interactive and Evolutionary Approaches2008Collection (editor) (Refereed)
  • 8.
    Branke, Jürgen
    et al.
    University of Karlsruhe, Institute AIFB, Germany.
    Deb, KalyanmoyMiettinen, KaisaHelsiniki School of Economics.Slowinski, RomanPoznan University of Technology, Institute of Computing Science.
    Practical Approaches to Multi-Objective Optimization2007Conference proceedings (editor) (Refereed)
  • 9.
    Branke, Jürgen
    et al.
    University of Karlsruhe, Institute AIFB, Germany.
    Deb, KalyanmoyMiettinen, KaisaHelisinki School of Economics.Steuer, R.E.
    Practical Approaches to Multi-Objective Optimization2005Conference proceedings (editor) (Refereed)
  • 10. Deb, K.
    et al.
    Miettinen, Kaisa
    Department of Mathematical Information Technology, University of Jyväskylä.
    Sharma, D.
    A Hybrid Integrated Multi-Objective Optimization Procedure for Estimating Nadir Point2009In: Evolutionary Multi-Criterion Optimization / [ed] Fonseca, C.M.; Gandibleux, X.; Hao, J.-K.; Sevaux, M, Springer, 2009, p. 569-583Conference paper (Refereed)
    Abstract [en]

    A nadir point is constructed by the worst objective values of the solutions of the entire Pareto-optimal set. Along with the ideal point, the nadir point provides the range ofobjective values within which all Pareto-optimal solutions must lie. Thus, a nadir point is an important point to researchers and practitioners interested in multi-objectiveoptimization. Besides, if the nadir point can be computed relatively quickly, it can be used to normalize objectives in many multi-criterion decision making tasks. Importantly,estimating the nadir point is a challenging and unsolved computing problem in case of more than two objectives. In this paper, we revise a previously proposed serial application of an EMO and a local search method and suggest an integrated approach for finding the nadir point. A local search procedure based on the solution of a bi-level achievement scalarizing function is employed to extreme solutions in stabilized populations in an EMO procedure. Simulation results on a number of problems demonstrate the viability and working of the proposed procedure. 

  • 11. Deb, Kalyanmoy
    et al.
    Chaudhuri, Shamik
    Miettinen, Kaisa
    Helsinki School of Economics.
    Towards Estimating Nadir Objective Vector using Evolutionary Approaches2006In: GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE / [ed] M. Keijzer et al., New York: The Association of Computing Machinery , 2006, p. 643-650Conference paper (Refereed)
    Abstract [en]

    Nadir point plays an important role in multi-objective optimization because of its importance in estimating the range of objective values corresponding to desired Pareto-optimal solutions and also in using many classical interactive optimization techniques. Since this point corresponds to the worst Pareto-optimal solution of each objective, the task of estimating the nadir point necessitates information about the whole Pareto optimal frontier and is reported to be a difficult task using classical means. In this paper, for the first time, we have proposed a couple of modifications to an existing evolutionary multi-objective optimization procedure to focus its search towards the extreme objective values front-wise. On up to 20-objective optimization problems, both proposed procedures are found to be capable of finding a near nadir point quickly and reliably. Simulation results are interesting and should encourage further studies and applications in estimating the nadir point, a process which should lead to a better interactive procedure of finding and arriving at a desired Pareto-optimal solution.

  • 12. Deb, Kalyanmoy
    et al.
    Greco, SalvatoreMiettinen, KaisaUniversity of Jyväskylä.Zitzler, E
    Hybrid and Robust Approaches to Multiobjective Optimization2009Conference proceedings (editor) (Refereed)
  • 13. Deb, Kalyanmoy
    et al.
    Miettinen, Kaisa
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    A Review of Nadir Point Estimation Procedures Using Evolutionary Approaches: A Tale of Dimensionality Reduction2008Report (Other academic)
    Abstract [en]

    Estimation of the nadir objective vector is an important task, particularly for multi-objective optimization problems having more than two conflicting objectives. Along with the ideal point, nadir point can be used to normalize the objectives so that multi-objective optimization algorithms can be used more reliably. The knowledge of the nadir point is also a pre-requisite to many multiple criteria decision making methodologies.Moreover, nadir point is useful for an aid in interactive methodologies and visualization softwares catered for multi-objective optimization. However, the computation of exact nadir point formore than two objectives is not an easy matter, simply because nadir point demands the knowledge of extreme Paretooptimal solutions. In the past few years, researchers have proposed several nadir point estimation procedures using evolutionary optimization methodologies. In this paper, we review the past studies and reveal an interesting chronicle of events in this direction. To make the estimation procedure computationally faster and more accurate, the methodologies were refined one after the other by mainly focusing on increasingly lower dimensional subset of Pareto-optimal solutions. Simulation results on a number of numerical test problems demonstrate better efficacy of the approach which aims to find only the extreme Pareto-optimal points compared to its higher-dimensional counterparts.

  • 14. Deb, Kalyanmoy
    et al.
    Miettinen, Kaisa
    Chaudhuri, Shamik
    An Estimation of Nadir Objective Vector using a Hybrid Evolutionary-Cum-Local-Search Procedure2009Report (Other academic)
    Abstract [en]

    A nadir objective vector is constructed from the worstPareto-optimal objective values in a multi-objectiveoptimization problem and is an important entity tocompute because of its significance in estimating therange of objective values in the Pareto-optimal frontand also in executing a number of interactive multi-objective optimization techniques. Along with theideal objective vector, it is also needed for the purposeof normalizing different objectives, so as to facilitatea comparison and agglomeration of the objectives.However, the task of estimating the nadir objectivevector necessitates information about the completePareto-optimal front and has been reported to be adifficult task, and importantly an unsolved and openresearch issue. In this paper, we propose certain mod-ifications to an existing evolutionary multi-objectiveoptimization procedure to focus its search towardsthe extreme objective values and combine it with areference-point based local search approach to con-stitute a couple of hybrid procedures for a reliableestimation of the nadir objective vector. With upto 20-objective optimization test problems and on athree-objective engineering design optimization prob-lem, one of the proposed procedures is found to becapable of finding the nadir objective vector reliably.The study clearly shows the significance of an evolu-tionary computing based search procedure in assist-ing to solve an age-old important task in the field ofmulti-objective optimization.

  • 15. Deb, Kalyanmoy
    et al.
    Miettinen, Kaisa
    Chaudhuri, Shamik
    Estimating Nadir Objective Vector: Hybrid of Evolutionary and Local Search2008Report (Other academic)
  • 16.
    Erkki, Heikkola
    et al.
    Numerola Oy, Jyväskylä, Finland.
    Miettinen, Kaisa
    Helsinki Sch Econ.
    Nieminen, Paavo
    Department of Mathematical Information Technology, University of Jyväskylä.
    Multiobjective Optimization of an Ultrasonic Transducer using NIMBUS2006In: Ultrasonics, ISSN 0041-624X, E-ISSN 1874-9968, Vol. 44, no 4, p. 368-380Article in journal (Refereed)
    Abstract [en]

    The optimal design of an ultrasonic transducer is a multiobjective optimization problem since the final outcome needs to satisfy several conflicting criteria. Simulation tools are often used to avoid expensive and time-consuming experiments, but even simulations may be inefficient and lead to inadequate results if they are based only on trial and error. In this work, the interactive multiobjective optimization method NIMBUS is applied in designing a high-power ultrasonic transducer. The performance of the transducer is simulated with a finite element model, and three design goals are formulated as objective functions to be minimized. To find an appropriate compromise solution, additional preference information is needed from a decision maker, who in our case is an expert in transducer design. A realistic design problem is formulated, and an interactive solution process is described. Our findings demonstrate that interactive multiobjective optimization methods, combined with numerical simulation models, can efficiently help in finding new solution approaches and possibilities as well as new understanding of real-life problems as entirenesses. In this case, the decision maker found a solution that was better with respect to all three objectives than the conventional unoptimized design.

  • 17.
    Eskelinen, Petri
    et al.
    Helsinki School of Economics.
    Miettinen, Kaisa
    Trade-off Analysis Tool for Interactive Nonlinear Multiobjective Optimization2008In: 20TH INTERNATIONAL CONFERENCE, EURO MINI CONFERENCE CONTINUOUS OPTIMIZATION AND KNOWLEDGE-BASED TECHNOLOGIES, EUROPT'2008 / [ed] L. Saralauskas, G. W. Weber, E. K. Zavadskas, 2008, p. 223-228Conference paper (Refereed)
    Abstract [en]

    In interactive methods, a decision maker (DM) directs the search for the most preferred Pareto optimal solution with his/her preferences. We propose a tool that can be used to support the DM. With this tool, the DM can conveniently learn about local trade-offs and judge whether they are worthwhile. The tool is based on an idea where the DM is able to vary a selected Pareto optimal objective vector. The varied vector is treated as a reference point which is then projected to the tangent hyperplane of the Pareto optimal set at the selected Pareto optimal solution. This information can be used to reflect what kind of Pareto optimal solutions and trade-offs are available in a local neighborhood of the selected solution. This tool is especially useful when trade-off analysis must be carried out fast and without increasing computation workload.

  • 18.
    Eskelinen, Petri
    et al.
    Helsinki School of Economics.
    Miettinen, Kaisa
    Department of Mathematical Information Technology, University of Jyväskylä, Finland.
    Trade-Off Analysis Tool with Applicability Study for Interactive Nonlinear Multiobjective Optimization2008Report (Other academic)
  • 19.
    Eskelinen, Petri
    et al.
    Helsinki School of Economics.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Klamroth, Kathrin
    Institute of Applied Mathematics, University of Erlangen Nuremberg.
    Hakanen, Jussi
    University of Jyväskylä.
    Interactive Learning-Oriented Decision Support Tool for Nonlinear Multiobjective Optimization: Pareto Navigator2007Report (Other academic)
    Abstract [en]

        We describe a new interactive learning-oriented method called Pareto navigatorfor nonlinear multiobjective optimization. In the method, first a polyhedral approx-imation of the Pareto optimal set is formed in the objective function space usinga relatively small set of Pareto optimal solutions representing the Pareto optimalset. Then the decision maker can navigate around the polyhedral approximationand direct the search for promising regions where the most preferred solution couldbe located. In this way, the decision maker can learn about the interdependenciesbetween the conflicting objectives and possibly adjust one’s preferences. Once aninteresting region has been identified, the polyhedral approximation can be mademore accurate in that region or the decision maker can ask for the closest counter-part in the actual Pareto optimal set. If desired, (s)he can continue with anotherinteractive method from the solution obtained. Pareto navigator can be seen as a nonlinear extension of the linear Pareto race method. Pareto navigator is computa-tionally efficient because most of the computations are performed in the polyhedralapproximation and for that reason function evaluations of the actual objective func-tions are not needed. Thus, the method is well suited especially for problems withcomputationally costly functions. Furthermore, thanks to the visualization tech-nique used, the method is applicable also for problems with three or more objectivefunctions, and in fact it is best suited for such problems. We illustrate the methodand the underlying ideas with an example.

  • 20.
    Eskelinen, Petri
    et al.
    Helsinki School of Economics.
    Ruuska, Sauli
    University of Jyväskylä.
    Miettinen, Kaisa
    Dept. of Mathematical Information Technology, University of Jyväskylä.
    Wiecek, Margaret M
    Clemson University.
    Mustajoki, Jyri
    Aalto University.
    A Scenario-Based Interactive Multiobjective Optimization Method for Decision Making under Uncertainty2010In: Uncertainty and robustness in planning and decision making, URPDM 2010: proceedings of the 25th Mini-EURO Conference / [ed] C. H. Antunes, D. Rios Insua, L Candido Dias, 2010Conference paper (Refereed)
  • 21. Haanpaa, T
    et al.
    Aittokoski, T
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Hakanen, Jussi
    University of Jyväskylä.
    An Approach to Minimize the Number of Function Evaluations in Global Optimization2010In: 24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, Proceedings / [ed] R. Kasimbeyli, C. Dincer, S. Ozpeynirci, L. Sakalauskas, Vilnius Gediminas Technical University Publishing House 'Technika' , 2010, p. 161-166Conference paper (Refereed)
  • 22. Haarala, M
    et al.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Mäkelä, Marko M.
    Department of Mathematical Information Technology, University of Jyväskylä.
    New limited memory bundle method for large-scale nonsmooth optimization2004In: Optimization Methods and Software, ISSN 1055-6788, E-ISSN 1029-4937, Vol. 19, no 6, p. 673-692Article in journal (Refereed)
    Abstract [en]

    Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables. In such problems, the direct application of smooth gradient-based methods may lead to a failure due to the nonsmooth nature of the problem. On the other hand, none of the current general nonsmooth optimization methods is efficient in large-scale settings. In this article, we describe a new limited memory variable metric based bundle method for nonsmooth large-scale optimization. In addition, we introduce a new set of academic test problems for large-scale nonsmooth minimization. Finally, we give some encouraging results from numerical experiments using both academic and practical test problems.

  • 23.
    Haarala, Napsu
    et al.
    School of Computational and Applied Mathematics, University of the Witwatersrand.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Mäkelä, Marko M.
    Department of Mathematical Information Technology, University of Jyväskylä.
    Globally Convergent Limited Memory Bundle Method for Large-Scale Nonsmooth Optimization2007In: Mathematical programming, ISSN 0025-5610, E-ISSN 1436-4646, Vol. 109, no 1, p. 181-205Article in journal (Refereed)
    Abstract [en]

    Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of thousands of variables. In the paper [Haarala, Miettinen, Mäkelä, Optimization Methods and Software, 19, (2004), pp. 673-692] we have described an efficient method for large-scale nonsmooth optimization. In this paper, we introduce a new variant of this method and prove its global convergence for locally Lipschitz continuous objective functions, which are not necessarily differentiable or convex. In addition, we give some encouraging results from numerical experiments.

  • 24.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Kawajiri, Yoshiaki
    Biegler, Lorenz T
    Miettinen, Kaisa
    Interactive Multiobjective Optimizationof Superstructure SMB Processes2009In: Multiobjective Programming and Goal Programming / [ed] V. Barichard, M. Ehrgott, X. Gandibleux, V. T'kindt, Springer Berlin/Heidelberg, 2009, p. 221-230Conference paper (Refereed)
    Abstract [en]

    We consider multiobjective optimization problems arising from super-structure formulation of Simulated Moving Bed (SMB) processes. SMBs are widelyused in many industrial separations of chemical products and they are challengingfrom the optimization point of view. We employ efficient interactive multiobjec-tive optimization which enables considering several conflicting objectives simulta-neously without unnecessary simplifications as have been done in previous studies.The interactive IND-NIMBUS software combined with the IPOPT optimizer is usedto solve multiobjective SMB design problems. The promising results of solving asuperstructure SMB optimization problem with four objectives demonstrate the use-fulness of the approach.

  • 25.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Kawajiri, Yoshiaki
    Miettinen, Kaisa
    University of Jyväskylä.
    Biegler, Lorenz T.
    Interactive Multi-Objective Optimization for Simulated Moving Bed Processes2007In: Control and Cybernetics, ISSN 0324-8569, Vol. 36, no 2, p. 282-230Article in journal (Refereed)
    Abstract [en]

    In this paper, efficient optimization techniques are used to solve multi-objective optimization problems arising from Simulated Moving Bed (SMB) processes. SMBs are widely used in many industrial separations of chemical products and they are very challenging from the optimization point of view. With the help of interactive multi-objective optimization, several conflicting objectives can be considered simultaneously without making unnecessary simplifications, as it has been done in previous studies. The optimization techniques used are the interactive NIMBUS R method and the IPOPT optimizer. To demonstrate the usefulness of these techniques, the results of solving an SMB optimization problem with four objectives are reported.

  • 26.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Kawajiri, Yoshiaki
    Miettinen, Kaisa
    Helsinki School of Economics.
    Biegler, Lorenz T.
    Interactive Multi-Objective Optimization of Simulated Moving Bed Processes using IND-NIMBUS and IPOPT2006Report (Other academic)
    Abstract [en]

     In this paper, efficient optimization techniques are used to solve multi-objectiveoptimization problems arising from Simulated Moving Bed (SMB) processes. SMBsare widely used in many industrial separations of chemical products and they arevery challenging from the optimization point of view. With the help of interactivemulti-objective optimization, several conflicting objectives can be considered simul-taneously without making unnecessary simplifications as has been done in previousstudies. The optimization techniques used are the interactive NIMBUS R methodand the IPOPT optimizer. To demonstrate the usefulness of these techniques, theresults of solving an SMB optimization problem with four objectives are reported.

  • 27.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Miettinen, Kaisa
    University of Jyväskylä .
    Mäkelä, Marko M.
    Department of Mathematical Information Technology, University of Jyväskylä.
    Towards Multiobjective Process Synthesis2005Report (Other academic)
  • 28.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Mäkelä, Marko M.
    Department of Mathematical Information Technology, University of Jyväskylä.
    Manninen, J.
    On Interactive Multiobjective Optimization with NIMBUS in Chemical Process Design2005In: Journal of Multi-Criteria Decision Analysis, ISSN 1057-9214, E-ISSN 1099-1360, Vol. 13, no 2-3, p. 125-134Article in journal (Refereed)
    Abstract [en]

    We study multiobjective optimization problems arising from chemical process simulation. The interactive multiobjective optimization method NIMBUS®, developed at the University of Jyväskylä, is combined with the BALAS® process simulator, developed at the VTT Technical Research Center of Finland, in order to provide a new interactive tool for designing chemical processes. Continuous interaction between the method and the designer provides a new efficient approach to explore Pareto optimal solutions and helps the designer to learn about the behaviour of the process. As an example of how the new tool can be used, we report the results of applying it in a heat recovery system design problem related to the process water system of a paper mill.

  • 29.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Mäkelä, M.M.
    An Application of Multiobjective Optimization to Process Simulation2004In: CD-Proceedings of 4th European Congress on Computational Methods in Applied Sciences and Engineering: ECCOMAS2004, Volume II / [ed] Neittaanmäki, P., Rossi, T., Korotov, S., Onate, E., Periaux, J., Knörzer, D., 2004Conference paper (Refereed)
    Abstract [en]

    We study multiobjective optimization problems arising from chemical process simulation. Interactive multiobjective optimization method NIMBUS, developed at the University of Jyväskylä, is combined with the BALAS process simulator, developed at theTechnical Research Center of Finland, in order to provide a new interactive tool for designing chemical processes. Continuous interaction between the method and the designer provides an efficient approach to explore the Pareto optimal solutions and helps the designer to learn about the behaviour of the process. This new approach has been applied to a water allocation problem for an integrated plant containing a thermomechanical pulping plant and a paper mill.

  • 30.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Mäkelä, Marko M.
    Department of Mathematical Information Technology, University of Jyväskylä.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Manninen, J.
    On Interactive Multiobjective Optimization with NIMBUS in Chemical Process Design2004In: CD-Proceedings of MCDM2004, the 17th International Conference on Multiple Criteria Decision Making / [ed] Wedley, W.C., 2004Conference paper (Other academic)
  • 31.
    Hakanen, Jussi
    et al.
    University of Jyväskylä.
    Sahlstedt, Kristian E.
    Miettinen, Kaisa
    University of Jyväskylä.
    Simulation-Based Interactive Multiobjective Optimization in Wastewater Treatment2008In: Proceedings of the International Conference on Engineering Optimization EngOpt 2008, Rio de Janeiro, Brazil, 2008, p. 328-337Conference paper (Other academic)
    Abstract [en]

    In this paper, we describe a new interactive tool developed for wastewater treatment plant design. The tool is aimed at supporting the designer in designing new wastewater treatment plants as well as optimizing the performance of already available plants. The idea is to utilize interactive multiobjective optimization which enables the designer to consider the design with respect to several conflicting evaluation criteria simultaneously. This is more important than ever because the requirements for wastewater treatment plants are getting tighter and tighter from both environmental and economical reasons. By combining a process simulator to simulate wastewater treatment and an interactive multiobjective optimization software to aid the designer during the design process, we obtain a practically useful tool for decision support. The applicability of our tool is illustrated with a case study related to municipal wastewater treatment where three conflicting evaluation criteria are considered.

  • 32.
    Hartikainen, Markus
    et al.
    University of Jyväskylä, Finland.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    A Computationally Inexpensive Approach in Multiobjective Heat Exchanger Network Synthesis2010In: Proceedings of the 2nd International Conference on Applied Operational Research / [ed] M. Collan, 2010, p. 99-109Conference paper (Refereed)
  • 33.
    Hartikainen, Markus
    et al.
    University of Jyväskylä, Finland.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Wiecek, Margaret M
    Clemson University.
    A Pareto Front Approximation with Inherent Nondominance and the Delaunay Triangulation2010Report (Other academic)
  • 34.
    Hartikainen, Markus
    et al.
    University of Jyväskylä, Finland.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Wiecek, Margaret M
    Clemson University.
    Constructing a Pareto front approximation for decision making2011In: Mathematical Methods of Operations Research, ISSN 1432-2994, E-ISSN 1432-5217, Vol. 73, no 2, p. 209-234Article in journal (Refereed)
    Abstract [en]

    An approach to constructing a Pareto front approximation to computationally expensive multiobjective optimization problems is developed. The approximation is constructed as a sub-complex of a Delaunay triangulation of a finite set of Pareto optimal outcomes to the problem. The approach is based on the concept of inherent nondominance. Rules for checking the inherent nondominance of complexes are developed and applying the rules is demonstrated with examples. The quality of the approximation is quantified with error estimates. Due to its properties, the Pareto front approximation works as a surrogate to the original problem for decision making with interactive methods.

  • 35.
    Hartikainen, Markus
    et al.
    Dept. of Mathematical Information Technology, University of Jyväskylä.
    Miettinen, Kaisa
    Dept. of Mathematical Information Technology, University of Jyväskylä.
    Wiecek, Margaret M
    Dept. of Mathematical Information Technology, University of Jyväskylä.
    Decision Making on Pareto Front Approximations with Inherent Nondominance2011In: New State of MCDM in the 21st Century / [ed] Y. Shi, S. Wang, G. Kou, J. Wallenius, Springer Berlin/Heidelberg, 2011, p. 35-45Conference paper (Refereed)
  • 36.
    Hartikainen, Markus
    et al.
    University of Jyväskylä, Finland.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Wiecek, Margaret M
    Clemson University.
    Inherent Nondominance - Pareto Front Approximations for Decision Making2010Report (Other academic)
  • 37. Hartikainen, Markus
    et al.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Wiecek, Margaret M
    Clemson University.
    PAINT: An Interpolation method for Computationally Expensive Multiobjective Optimization Problems2011Report (Other academic)
  • 38. Heikkola, E.
    et al.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Nieminen, P.
    Applying IND-NIMBUS to a Design Problem in High-Power Ultrasonics2005Report (Other academic)
  • 39.
    Hämäläinen, Jari
    et al.
    University of Kuopio, Department of Physics.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Madetoja, E.
    Mäkelä, M.M.
    Tarvainen, P.
    Multiobjective Decision Making for Papermaking2004In: CD-Proceedings of MCDM2004, the 17th International Conference on Multiple Criteria Decision Making / [ed] Wedley, W.C., 2004Conference paper (Other academic)
  • 40.
    Kaario, Katja
    et al.
    Department of Mathematical Information Technology, University of Jyväskylä.
    Miettinen, Kaisa
    KTH, Superseded Departments, Mathematics.
    Mäkelä, Marko M.
    Department of Mathematical Information Technology, University of Jyväskylä.
    Saariluoma, P.
    Symbolic and Graphic Classification in Interactive Multiobjective Optimization System WWW-NIMBUS2004In: CD-Proceedings of 4th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS2004, Volume II / [ed] Neittaanmäki, P., Rossi, T., Korotov, S., Onate, E., Periaux, J., Knörzer, D., 2004Conference paper (Refereed)
  • 41. Kirilov, L.
    et al.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Solutions' Properties and Numerical Testing of an Interactive Method REF-LEX2005In: Proceedings of the International Conference on Computer Systems and Technologies -- CompSysTech'05 / [ed] B. Rachev, A. Smrikarov, Bulgarian Chapter of ACM , 2005, p. IIIA9.1-IIIA9.6Conference paper (Refereed)
  • 42.
    Klamroth, Kathrin
    et al.
    Institute of Applied Mathematics, University of Erlangen Nuremberg.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Integrating Approximation and Interactive Decision Making in Multicriteria Optimization2008In: Operations Research, ISSN 0030-364X, E-ISSN 1526-5463, Vol. 56, no 1, p. 222-234Article in journal (Refereed)
    Abstract [en]

    We present a new interactive hybrid approach for solving multicriteria optimization problems where features of approximation methods and interactive approaches are incorporated. We produce rough approximations of the nondominated set and let the decision maker indicate with the help of reference points where to refine the approximation. In this way, (s)he iteratively directs the search toward the best nondominated solution. After the decision maker has identified the most interesting region of the nondominated set, the final solution can be fine-tuned with existing interactive methods. We suggest different ways of updating the reference point as well as discuss visualizations that can be used in comparing different nondominated solutions. The new method is computationally relatively inexpensive and easy to use for the decision maker.

  • 43.
    Klamroth, Kathrin
    et al.
    Institute of Applied Mathematics, University of Erlangen Nuremberg.
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Interactive Approach Utilizing Approximations of the Nondominated Set2005Report (Other academic)
    Abstract [en]

        We present a new interactive approach for solving multicriteria opti-mization problems. We produce rough approximations of the nondomi-nated set and let the decision maker indicate with the help of referencepoints where to refine the approximation. In this way, (s)he iterativelydirects the search towards the best nondominated solution. After the deci-sion maker has identified the most interesting region of the nondominatedset, the final solution can be fine-tuned with existing interactive methods.We suggest different ways of updating the reference point as well as dis-cuss visualizations that can be used in comparing different nondominatedsolutions. The new method is computationally inexpensive and easy to usefor the decision maker.

  • 44. Lahdelma, Risto
    et al.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Salminen, Pekka
    School of Business and Economics, University of Jyväskylä.
    Reference Point Approach for Multiple Decision Makers2005In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 164, no 3, p. 785-791Article in journal (Refereed)
    Abstract [en]

    We consider multiple criteria decision-making problems where a group of decision-makers wants to find the most preferred solution from a discrete set of alternatives. We develop a method that uses achievement functions for charting subsets of reference points that would support a certain alternative to be the most preferred one. The resulting descriptive information is provided to the decision-makers in the form of reference acceptability indices and central reference points for each decision alternative. Then, the decision-makers can compare this information with their own preferences. We demonstrate the use of the method using a strategic multiple criteria decision model for an electricity retailer.

  • 45. Lahdelma, Risto
    et al.
    Miettinen, Kaisa
    University of Helsinki.
    Salminen, Pekka
    School of Business and Economics, University of Jyväskylä.
    Tervonen, T.
    Computational Methods for Stochastic Multicriteria Acceptability Analysis2004In: CD-Procdeedings of 4th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS2004, Volume II / [ed] Neittaanmäki, P., Rossi, T., Korotov, S., Onate, E., Periaux, J., Knörzer, D., 2004Conference paper (Refereed)
  • 46.
    Laukkanen, T
    et al.
    Aalto University, School of Science and Technology, Department of Energy Technology.
    Tveit, T.-M.
    Aalto University, School of Science and Technology, Department of Energy Technology.
    Ojalehto, V
    Department of Mathematical Information Technology, University of Jyväskylä.
    Miettinen, Kaisa
    Dept. of Mathematical Information Technology, University of Jyväskylä.
    Fogelholm, C.-J.
    Aalto University, School of Science and Technology, Department of Energy Technology.
    An Interactive Multi-Objective Approach to Heat Exchanger Network Synthesis2010In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 34, no 6, p. 943-952Article in journal (Refereed)
    Abstract [en]

    In this work we present a multi-objective approach to heat exchanger network synthesis. The approach solves a modified version of the Synheat model using an interactive multi-objective optimisation method, NIMBUS, which is implemented in GAMS. The results obtained demonstrate the potential of interactive multi-objective optimisation.

  • 47. Laukkanen, T.
    et al.
    Tveit, T.-M.
    Ojalehto, V.
    Miettinen, Kaisa
    University of Jyväskylä.
    Fogelholm, C.-J.
    Multiobjective GAMS-NIMBUS Tool Applied for the Heat Exchanger Network Synthesis2009Report (Other academic)
  • 48. Laukkanen, Timo
    et al.
    Tveit, Tor-Martin
    Ojalehto, Vesa
    Miettinen, Kaisa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Fogelholm, Carl-Johan
    Bilevel heat exchanger network synthesis with an interactive multi-objective optimization method2012In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 48, p. 301-316Article in journal (Refereed)
    Abstract [en]

    Heat exchanger network synthesis (HENS) has been an active research area for more than 40 years because well-designed heat exchanger networks enable heat recovery in process industries in an energy-and cost-efficient manner. Due to ever increasing global competition and need to decrease the harmful effects done on the environment, there still is a continuous need to improve the heat exchanger networks and their synthesizing methods. In this work we present a HENS method that combines an interactive multi-objective optimization method with a simultaneous bilevel HENS method, where the bilevel part of the method is based on grouping of process streams and building aggregate streams from the grouped streams. This is done in order to solve medium-sized industrial HENS problems efficiently with good final solutions. The combined method provides an opportunity to solve HENS problems efficiently also regarding computing effort and at the same time optimizing all the objectives of HENS simultaneously and in a genuine multi-objective manner without using weighting factors. This enables the designer or decision maker to be in charge of the design procedure and guide the search into areas that the decision maker is most interested in. Two examples are solved with the proposed method. The purpose of the first example is to help in illustrating the steps in the overall method. The second example is obtained from the literature.

  • 49.
    Lotov, Alexander V.
    et al.
    Dorodnicyn Computing Center of the Russian Academy of Sciences.
    Kamenev, George K.
    Dorodnicyn Computing Center of the Russian Academy of Sciences.
    Berezkin, Vadim E.
    Dorodnicyn Computing Center of the Russian Academy of Sciences.
    Miettinen, Kaisa
    Helsinki School of Economics.
    Optimal control of cooling process in continuous casting of steel using a visualization-based multi-criteria approach2005In: Applied Mathematical Modelling, ISSN 0307-904X, E-ISSN 1872-8480, Vol. 29, no 7, p. 653-672Article in journal (Refereed)
    Abstract [en]

    This paper is devoted to the application of a new visualization-based multi-criteria approach in an optimal control problem related to the cooling process in the continuous casting of steel. The purpose is to develop such a control that results in steel of the best possible quality, that is, minimizes the defects in the final product. Since the constraints describing technological and metallurgical requirements result in an empty set of the feasible controls of the cooling process, the aim of the study is to find a control that violates the constraints as little as possible. The problem is formulated as a multi-criteria optimization problem and the constraint violations play the role of selection criteria.

    Our approach is based on the application of both a nonlinear mathematical model of the cooling process that features a system of partial differential equations, and a new decision support technique, called Interactive Decision Maps technique, that uses the on-line visualization of the non-dominated frontier.

  • 50. Lotov, A.V.
    et al.
    Miettinen, Kaisa
    University of Jyväskylä.
    Visualizing the Pareto Frontier2008In: Multiobjective Optimization: Interactive And Evolutionary Approaches / [ed] Branke, J; Deb, K; Miettinen, K; Slowinski, R, Springer Berlin/Heidelberg, 2008, p. 213-243Conference paper (Refereed)
    Abstract [en]

    We describe techniques for visualizing the Pareto optimal set that can be used if the multiobjective optimization problem considered has more than two objective functions. The techniques discussed can be applied in the framework of both MCDM and EMO approaches. First, lessons learned from methods developed for biobjective problems are considered. Then, visualization techniques for convex multiobjective optimization problems based on a polyhedral approximation of the Pareto optimal set are discussed. Finally, some visualization techniques are considered that use a pointwise approximation of the Pareto optimal set.

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