Management Science
What is Management Science?
Management Science deals with the application of advanced analytical methods to help make better decisions. The terms operations research and business analytics are sometimes used as synonyms for management science.
Management Science covers a wide range of problem-solving and mathematical modelling techniques that help managers to improve decision-making and efficiency.
Examples of these techniques are econometrics, mathematical optimization, simulation, data analytics, statistics, and decision analysis. These techniques are widely applied in areas such as finance, risk management, economics, strategic management, international business, logistics and quality management.
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Navigation: Current research topics | Selected Publications | Professional societies
Current research topics
- Large-scale optimization and evolutionary computation (learn more)
- Statistical natural language processing and semantic text analysis
- Portfolio decision analysis and resource allocation models
- Performance, productivity and efficiency analysis (learn more)
- Multiple criteria decision making and support (learn more)
- Behavioral decision theory and support
- Quantitative marketing; consumer preferences and pricing
Selected publications
Large-scale optimization and evolutionary computation
Sinha, A., Malo, P., Deb, K., Korhonen, P. and Wallenius, J. (2016): “Solving Bilevel Multi-criterion Optimization Problems with Lower Level Decision Uncertainty”. IEEE Transactions on Evolutionary Computation, vol. 20(2), pp. 199--217.
Sinha, A., Malo, P. and Kuosmanen, T. (2015): “A Multi-objective Exploratory Procedure for Regression Model Selection”. Journal of Computational and Graphical Statistics, vol. 24(1), pp. 154-182.
Statistical natural language processing and semantic text analysis
Malo, P., Sinha, A., Takala, P., Korhonen, P. and Wallenius, J. (2014): "Good debt or bad debt: Detecting semantic orientations in economic texts." Journal of the Association for Information Science and Technology, vol. 65(4), pp. 782-796.
Malo, P., Siitari, P. and Sinha, A. (2013): “Automated Query Learning with Wikipedia and Genetic Programming”. Artificial Intelligence, vol. 194, pp. 86--110.
Upreti, B., Asatiani, A., and Malo, P. (2016): “To Reach The Clouds: Application of Topic Models to the Meta-review on Cloud Computing Literature.” In: Proceedings of Annual Hawaii International Conference on Systems Sciences (HICSS-2016), pp. 3979--3988.
Portfolio Decision Analysis and resource allocation models
Vilkkumaa, E, Salo, A., Liesiö, J., Siddiqui, A. (2015). Fostering breakthrough technologies — How do optimal funding decisions depend on evaluation accuracy?, Technological Forecasting and Social Change, 96:173-190.
Vilkkumaa, E., Liesiö, J., Salo, A. (2014). Optimal Strategies for Selecting Project Portfolios Using Uncertain Value Estimates, European Journal of Operational Research, Vol. 233, pp. 772-783.
Mild, P., Liesiö, J., Salo, A. (2015). Selecting infrastructure maintenance projects with Robust Portfolio Modeling, Decision Support Systems, 77:21-30.
Fliedner, T., Liesiö, J. (2016). Adjustable Robustness for Multi-attribute Project Selection, European Journal of Operational Research, Vol. 252, pp. 931-946.
Performance, productivity and efficiency analysis
Kuosmanen, T. M. Kortelainen, K. Kultti, H. Pursiainen, A. Saastamoinen & T. Sipiläinen (2010). Sähköverkkotoiminnan kustannustehokkuuden estimointi StoNED-menetelmällä. Report by Sigma-Hat Economics Oy for Energiavirasto.
Kuosmanen, T. (2012): Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model, Energy Economics 34, 2189-2199.
Kuosmanen, T., A. Saastamoinen, and T. Sipiläinen (2013): What is the Best Practice for Benchmark Regulation of Electricity Distribution? Comparison of DEA, SFA and StoNED Methods, Energy Policy 61, 740-750.
Eskelinen, J. and Kuosmanen, T. (2013). Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach. Journal of Banking and Finance, 37 (12), 5163–5175.
Eskelinen, J., Halme, M. and Kallio, M. (2014) Bank Branch Sales Evaluation Using Extended Value Efficiency Analysis. European Journal of Operational Research, 232, 3, 654-663.
Multiple criteria decision making and support
Korhonen, P. J., Silvennoinen, K., Wallenius, J., and Öörni, A. (2012). Can a linear value function explain choices? An experimental study. European Journal of Operational Research, 219 (2), 360-367
Professional societies
Finnish Operations Research Society (FORS)
International Society on Multiple Criteria Decision Making
Association of European Operational Research Societies (EURO)
The Institute for Operations Research and the Management Sciences (INFORMS)
The ACM Special Interest Group on Genetic and Evolutionary Computation (ACM SIGEVO)