A large number of problems in production planning and scheduling, location, transportation,
finance, and engineering design require that decisions be made in the presence of …

In this chapter we discuss Monte Carlo sampling methods for solving large scale stochastic
programming problems. We concentrate on the “exterior” approach where a random sample …

This paper provides a review and commentary on the past, present, and future of numerical
optimization algorithms in the context of machine learning applications. Through case …

This is a substantial revision of the previous edition with added new material. The
presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk …

This paper proposes a stochastic programming model and solution algorithm for solving
supply chain network design problems of a realistic scale. Existing approaches for these …

This paper presents a methodology for using varying sample sizes in batch-type
optimization methods for large-scale machine learning problems. The first part of the paper …

This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

A stochastic program SP with solution value z∗ can be approximately solved by sampling n
realizations of the program's stochastic parameters, and by solving the resulting …

This study proposes a two-stage stochastic programming model to plan the transportation of
vital first-aid commodities to disaster-affected areas during emergency response. A multi …

The beginning of stochastic programming, and in particular stochastic linear programming
(SLP), dates back to the 50's and early 60's of the last century. Pioneers wheat that time …