This book describes a tool for mathematical modeling: the Python Optimization Modeling
Objects (Pyomo) software. Pyomo supports the formulation and analysis of mathematical …

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty,
dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A …

Over the last two decades, robust optimization has emerged as a computationally attractive
approach to formulate and solve single-stage decision problems affected by uncertainty …

Although stochastic programming is a powerful tool for modeling decision-making under
uncertainty, various impediments have historically prevented its wide-spread use. One factor …

Including integer variables into traditional stochastic linear programs has considerable
implications for structural analysis and algorithm design. Starting from mean-risk …

We generalize the definition of the bounds for the optimal value of the objective function for
various deterministic equivalent models in multistage stochastic programs. The parameters …

Forest ecosystems deliver multiple services crucial for human wellbeing and welfare, with
complex relationships among forest ecosystem services (FESs). Implementing sustainable …

We describe a multistage, stochastic, mixed-integer programming model for planning
capacity expansion of production facilities. A scenario tree represents uncertainty in the …

Abstract e‐Commerce activity has been increasing during recent years, and this trend is
expected to continue in the near future. e‐Commerce practices are subject to uncertainty …

We propose a scenario decomposition algorithm for stochastic 0–1 programs. The algorithm
recovers an optimal solution by iteratively exploring and cutting-off candidate solutions …