Multi-horizon stochastic programming

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 perf...

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Vydáno v:Computational management science Ročník 11; číslo 1-2; s. 179 - 193
Hlavní autoři: Kaut, Michal, Midthun, Kjetil T., Werner, Adrian S., Tomasgard, Asgeir, Hellemo, Lars, Fodstad, Marte
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2014
Springer Nature B.V
Témata:
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
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Shrnutí: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.
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ISSN:1619-697X
1619-6988
DOI:10.1007/s10287-013-0182-6