Multi-horizon stochastic programming

• Published in: Computational management science Vol. 11; no. 1-2; pp. 179 - 193
• Main Authors: Kaut, Michal, Midthun, Kjetil T., Werner, Adrian S., Tomasgard, Asgeir, Hellemo, Lars, Fodstad, Marte
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2014; Springer Nature B.V
• Subjects: Business and Management; Infrastructure; Mathematical models; Meat industry; Multistage; Natural resource management; Operations research; Operations Research/Decision Theory; Optimization; Original Paper; Probability theory; Profitability; Programming; Stochastic models; Studies; Trees; Uncertainty; Norway; Multistage; Scenario tree construction; Energy planning; Stochastic programming
• ISSN: 1619-697X; 1619-6988
• DOI: 10.1007/s10287-013-0182-6

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochastic-programming formulation of the problem due to the exponential growth in model size. In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.