On the Number of Stages in Multistage Stochastic Programs

Multistage stochastic programs are a viable modeling tool for sequential decisions conditional on information revealed at different points in time (stages). However, as the number of stages increases their applicability is soon halted by the curse of dimensionality. A typical, sometimes forced, alternative is to approximate stages by their expected values thus considering fewer stages … Read more

Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach

Emergency care necessitates adequate and timely treatment, which has unfortunately been compromised by crowding in many emergency departments (EDs). To address this issue, we study patient scheduling in EDs so that mandatory targets imposed on each patient’s door-to-provider time and length of stay can be collectively met with the largest probability. Exploiting patient flow data … Read more

Revisiting some results on the sample complexity of multistage stochastic programs and some extensions

In this work we present explicit definitions for the sample complexity associated with the Sample Average Approximation (SAA) Method for instances and classes of multistage stochastic optimization problems. For such, we follow the same notion firstly considered in Kleywegt et al. (2001). We define the complexity for an arbitrary class of problems by considering its … Read more

A robust optimization model for the risk averse reservoir management problem

This paper presents a new formulation for the risk averse stochastic reservoir management problem. Using recent advances in robust optimization and stochastic programming, we propose a dynamic, multi-objective model based on minimization of a multidimensional risk measure associated with floods and droughts for a hydro-electrical complex. We present our model and then identify approximate solutions … Read more

Robust optimization with ambiguous stochastic constraints under mean and dispersion information

In this paper we consider ambiguous stochastic constraints under partial information consisting of means and dispersion measures of the underlying random parameters. Whereas the past literature used the variance as the dispersion measure, here we use the mean absolute deviation from the mean (MAD). This makes it possible to use the old result of Ben-Tal … Read more

Stochastic versus Robust Optimization for a Transportation Problem

In this paper we consider a transportation problem under uncertainty related to gypsum replenishment for a cement producer. The problem is to determine the number of vehicles to book at the beginning of each week to replenish gypsum at all the cement factories of the producer in order to minimize the total cost, given by … Read more

Robust Inventory Routing with Flexible Time Window Allocation

This paper studies a robust maritime inventory routing problem with time windows and stochastic travel times. One of the novelties of the problem is that the length and placement of the time windows are also decision variables. Such problems arise in the design and negotiation of long-term delivery contracts with customers who require on-time deliveries … Read more

The impact of wind uncertainty on the strategic valuation of distributed electricity storage

The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an … Read more

A Counterexample to “Threshold Boolean form for joint probabilistic constraints with random technology matrix”

Recently, in the paper “Threshold Boolean form for joint probabilistic constraints with random technology matrix” (Math. Program. 147:391–427, 2014), Kogan and Lejeune proposed a set of mixed-integer programming formulations for probabilistically constrained stochastic programs having random constraint matrix and finite support distribution. We show that the proposed formulations do not in general correctly model such … Read more

A note on sample complexity of multistage stochastic programs

We derive a \emph{lower bound} for the \emph{sample complexity} of the Sample Average Approximation method for a certain class of multistage stochastic optimization problems. In previous works, \emph{upper bounds} for such problems were derived. We show that the dependence of the \emph{lower bound} with respect to the complexity parameters and the problem’s data are comparable … Read more