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Decision Making under Uncertainty

Our research on decision making under uncertainty is concerned with stochastic and robust optimisation within the context of uncertainty and risk. This involves modelling and optimisation of stochastic systems arising in engineering, defence, economics, finance, project management and supply chain management, etc. In particular, we research on worst-case design and risk management, scenario analysis, stochastic performance modelling and evaluation of computing network models.

A particular aspect of decision making under uncertainty is that of information collection. If additional information can be collected, but at a cost, how should the effort be spent to reduce the uncertainty where this is most beneficial to decision making in the future.

ORMS faculty

Selected Publications

J. Branke, S. Meisel, and C. Schmidt. “Simulated annealing in the presence of noise”. Journal of Heuristics 14, 2008, 627-654.

J. Branke, S. Chick, and C. Schmidt. “Selecting a selection procedure”. Management Science 53(12), 2007, 1916-1932.

K. Deb, S. Gupta, D. Daum, J. Branke, A.K. Mall, and D. Padmanabhan. “Reliability-Based Optimization Using Evolutionary Algorithms”. IEEE Transactions on Evolutionary Computation 13(5), 2009, 1054-1074.

D. Du, B. Chen, and D. Xu. Quantifying the efficiency of price-only contracts in push supply chains over demand distributions of known supports. Omega 42 (2014), 98–108.

N. Gulpinar, B. Rustem, and S. Zakovic. Stochastic optimization and worst-case decisions, Lecture Notes in Economics and Mathematical Systems 588 (2007), 317–338.

N. Gulpinar, P. Harrison, B. Rustem, Louis F. Pau, T. Field, and U. Harder, Performance optimization of a tandem M/GI/1 router network with batch arrivals, Journal of Cluster Computing 10(2007), 203–216.

N. Gulpinar and B. Rustem, Worst-case optimal robust decisions for multi-period portfolio optimization, European Journal of Operational Research 183(2007), 981–1000.

N. Gülpınar, B. Rustem, and R. Settergren, Optimisation and simulation approaches to scenario tree generation, Journal of Economics Dynamics and Control 28(2004), 1291–1315.

N. Gülpınar, B. Rustem, and R. Settergren, Multistage Stochastic Programming in Computational Finance, in: Computational Methods in Decision Making, Economics and Finance: Optimization Models, Kluwer Academic Publishers, 2002, 33–45.

Y. Jin and J. Branke. Evolutionary optimization in uncertain environments – A survey. IEEE Transactions on Evolutionary Computation 9(3), 2005, 303-318

F. Liu, M. Giulietti, and B. Chen. Joint Optimization of generation and storage in the presence of wind. IET Renewable Power Generation. May 2016 (DOI: 10.1049/iet-rpg.2015.0547)

I. Pänke, J. Branke, and Y. Jin. “Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation”. IEEE Transactions on Evolutionary Computation 10(4), 2006, 405-420.

C. Wang, X. Doan and B. Chen. Price of anarchy for non-atomic congestion games with stochastic demands. Transportation Research Part B: Methodological 70 (2014), 90–111.