Planning of Container Crossdocking for an Express Shipment Service Network

In air transportation, container crossdocking refers to a loaded container that is transferred at an airport from an incoming flight to an outgoing flight without handling the freight on the container. It reduces handling time and handling cost relative to unloading the container and sorting the freight, and is an economical alternative if a sufficient … Read more

Neur2SP: Neural Two-stage Stochastic Programming

Stochastic programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely applied and studied class of stochastic programming models. Solving 2SPs exactly requires evaluation of an expected value function that is computationally intractable. Additionally, having a mixed-integer linear program (MIP) or a nonlinear … Read more

The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: A Benders decomposition approach

Crowd-shipping is an emergent solution to avoid the negative effects caused by the growing demand for last-mile delivery services. Previous research has studied crowd-shipping typically at an operational planning level. However, the study of support infrastructure within a city logistics framework has been neglected, especially from a strategic perspective. We investigate a crowd-sourced last-mile parcel … Read more

Stochastic RWA and Lightpath Rerouting in WDM Networks

In a telecommunication network, Routing and Wavelength Assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity due to stranded bandwidth. At this … Read more

A Novel Solution Methodology for Wasserstein-based Data-Driven Distributionally Robust Problems

Distributionally robust optimization (DRO) is a mathematical framework to incorporate ambiguity over the actual data-generating probability distribution. Data-driven DRO problems based on the Wasserstein distance are of particular interest for their sound mathematical properties. For right-hand-sided uncertainty, however, existing methods rely on dual vertex enumeration rendering the problem intractable in practical applications. In this context, … Read more

Distributionally Robust Two-Stage Stochastic Programming

Distributionally robust optimization is a popular modeling paradigm in which the underlying distribution of the random parameters in a stochastic optimization model is unknown. Therefore, hedging against a range of distributions, properly characterized in an ambiguity set, is of interest. We study two-stage stochastic programs with linear recourse in the context of distributional ambiguity, and … Read more

A Regularized Smoothing Method for Fully Parameterized Convex Problems with Applications to Convex and Nonconvex Two-Stage Stochastic Programming

We present an approach to regularize and approximate solution mappings of parametric convex optimization problems that combines interior penalty (log-barrier) solutions with Tikhonov regularization. Because the regularized mappings are single-valued and smooth under reasonable conditions, they can be used to build a computationally practical smoothing for the associated optimal value function. The value function in … Read more

A conservative convergent solution for continuously distributed two-stage stochastic optimization problems

Two-stage stochastic programming is a mathematical framework widely used in real- life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision makers make use of numerical methods to obtain a near-optimal solution. Some applica- tions rely on the implementation … Read more

An Adaptive Sequential Sample Average Approximation Framework for Solving Two-stage Stochastic Programs

We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose is organized into \emph{outer} and \emph{inner} iterations as follows: during each outer iteration, a sample-path problem is implicitly generated using a sample of observations or “scenarios,” and solved only \emph{imprecisely}, to within a … Read more

A Stochastic Programming Approach for Electric Vehicle Charging Network Design

Advantages of electric vehicles (EV) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community … Read more