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Decomposition methods for multi-horizon stochastic programming
Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional...
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Decomposition methods for monotone two-time-scale stochastic optimization problems
It is common that strategic investment decisions are made at a slow time-scale, whereas operational decisions are made at a fast time-scale. Hence,...
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Machine Learning Under Stochastic Uncertainty
New methods for machine learning under stochastic uncertainty, especially for regression problems under uncertainty are described in this chapter....
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An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation
This paper proposes a stochastic programming model and a combined solution algorithm to solve integrated resource planning (IRP) problem of electric...
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Stochastic Facility Location
This chapter discusses stochastic facility location problems. It starts with location models considering reliability objectives and then focuses on...
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Approximate option pricing under a two-factor Heston–Kou stochastic volatility model
Under a two-factor stochastic volatility jump (2FSVJ) model we obtain an exact decomposition formula for a plain vanilla option price and a...
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A two-stage stochastic optimization model for port infrastructure planning
This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products,...
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Stochastic Processes
In this chapter, we consider stochastic processes, with a focus on MA, AR, ARMA, diffusion processes, Ito’s stochastic integrals, and Ito’s...
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Approximation of multistage stochastic programming problems by smoothed quantization
We present an approximation technique for solving multistage stochastic programming problems with an underlying Markov stochastic process. This...
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Incorporating convex risk measures into multistage stochastic programming algorithms
Over the last two decades, coherent risk measures have been well studied as a principled, axiomatic way to characterize the risk of a random...
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A non-anticipative learning-optimization framework for solving multi-stage stochastic programs
We present a non-anticipative learning- and scenario-based prediction-optimization (ScenPredOpt) framework that combines deep learning, heuristics,...
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Unrelated parallel machine scheduling problem with stochastic sequence dependent setup times
Unrelated parallel machine scheduling problem (UPM) is widely studied in the scheduling literature because of its extensive application area in the...
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Evaluation of stochastic flow lines with provisioning of auxiliary material
Flow lines are often used to perform assembly operations in multi-stage processes. During these assembly operations, components that are relatively...
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Distributions and bootstrap for data-based stochastic programming
In the context of optimization under uncertainty, we consider various combinations of distribution estimation and resampling (bootstrap and bagging)...
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Decomposition Methods
The chapter first recalls few properties of recursive algorithms. Next, it introduces a general recursive constructive method. Finally, it presents...
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Problem-driven scenario clustering in stochastic optimization
In stochastic optimisation, the large number of scenarios required to faithfully represent the underlying uncertainty is often a barrier to finding...
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Multi-period descriptive sampling for scenario generation applied to the stochastic capacitated lot-sizing problem
Using scenarios to model a stochastic system’s behavior poses a dilemma. While a large(r) set of scenarios usually improves the model’s accuracy, it...
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Nested Benders’s decomposition of capacity-planning problems for electricity systems with hydroelectric and renewable generation
Nested Benders’s decomposition is an efficient means to solve large-scale optimization problems with a natural time sequence of decisions. This paper...
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Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization
We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method...
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Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns
This paper addresses a stochastic job shop scheduling problem with sequence-dependent setup times, aiming to minimize the expected maximum lateness....
