Optimization-based inventory and supply chain management (SCM) under uncertainty can
provide organizations a significant competitive advantage. Implementing optimization under …

Numerical algorithms to solve mathematical optimization problems efficiently are essential to
applications in many areas of engineering and computational science. To solve optimization …

Battery sizing and siting problems are computationally challenging due to the need to make
long-term planning decisions that are cognizant of short-term operational decisions. This …

This study explores the influence of uncertain waiting times together with uncertain fuel
prices, in the selection of bunker fuel stops for a shipowner engaged in tramp ship**. We …

We propose a nodal stochastic generation and transmission expansion planning model that
incorporates the output from high-resolution global climate models through load and …

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power
system requires optimization with dynamic power system constraints, due to the key role of …

We describe software for stochastic programming that uses only sampled data to obtain both
a consistent sample-average solution and a consistent estimate of confidence intervals for …

We present StochasticPrograms. jl, a user-friendly and powerful open-source framework for
stochastic programming written in the Julia language. The framework includes both …

This paper introduces the Stochastic Look Ahead Commitment (SLAC) software prototyped
and tested for the Midcontinent Independent System Operator (MISO) look ahead …

The transition of industrial sectors to achieve climate neutrality is imperative for effectively
meeting the energy and climate policy objectives. Transforming these diverse sectors faces …