Multiobjective optimization and decision making in engineering sciences
Hakanen, J., & Allmendinger, R. (2021). Multiobjective optimization and decision making in engineering sciences. In J. Hakanen, & R. Allmendinger (Eds.), Optimization and Engineering, Vol. 22 (2). Special Issue on “Multiobjective optimization and decision making in engineering sciences” (22, pp. 1031-1037). Springer. Optimization and Engineering. https://doi.org/10.1007/s11081-021-09627-x
Julkaistu sarjassa
Optimization and EngineeringPäivämäärä
2021Tekijänoikeudet
© The Author(s) 2021
Real-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such problems is the identification of the most preferred trade-off solution(s) with respect to the competing criteria. Therefore, the effective combination of data, skills, and advanced engineering and management technologies is becoming a key asset to a company urging the need to rethink how to tackle modern decision making problems. This special issue focuses on the intersection between engineering, multiple criteria decision making, multiobjective optimization, and data science. The development of new models and algorithmic methods to solve such problems is in the focus as much as the application of these concepts to real problems. This special issue was motivated by the 25th International Conference on Multiple Criteria Decision Making (MCDM2019) held in Istanbul, Turkey, in 2019.
...
Julkaisija
SpringerKuuluu julkaisuun
Optimization and Engineering, Vol. 22 (2). Special Issue on “Multiobjective optimization and decision making in engineering sciences”ISSN Hae Julkaisufoorumista
1389-4420Asiasanat
Alkuperäislähde
https://www.springer.com/journal/11081Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/66400359
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
A Performance Indicator for Interactive Evolutionary Multiobjective Optimization Methods
Aghaei Pour, Pouya; Bandaru, Sunith; Afsar, Bekir; Emmerich, Michael; Miettinen, Kaisa (IEEE, 2024)In recent years, interactive evolutionary multiobjective optimization methods have been getting more and more attention. In these methods, a decision maker, who is a domain expert, is iteratively involved in the solution ... -
A New Paradigm in Interactive Evolutionary Multiobjective Optimization
Saini, Bhupinder Singh; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2020)Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving ... -
Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods
Aghaei Pour, Pouya; Bandaru, Sunith; Afsar, Bekir; Miettinen, Kaisa (ACM, 2022)Interactive methods support decision makers in finding the most preferred solution in multiobjective optimization problems. They iteratively incorporate the decision maker's preference information to find the best balance ... -
Handling expensive multiobjective optimization problems with evolutionary algorithms
Chugh, Tinkle (University of Jyväskylä, 2017)Multiobjective optimization problems (MOPs) with a large number of conflicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations ... -
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Chugh, Tinkle; Sindhya, Karthik; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2019)Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.